{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":52304,"offset":52294,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"```json\n{\n \"description\": \"\ud83d\udc76 \u062d\u0627\u0641\u0638 \u0639\u0644\u0649 \u0633\u0644\u0627\u0645\u0629 \u0635\u063a\u064a\u0631\u0643 \u0628\u0648\u0627\u0642\u064a\u0627\u062a \u0627\u0644\u0631\u0643\u0628\u0629 \u0627\u0644\u0645\u0635\u0645\u0645\u0629 \u062e\u0635\u064a\u0635\u064b\u0627 \u0644\u0644\u0623\u0637\u0641\u0627\u0644\u060c \u0627\u0644\u062d\u0627\u0645\u064a\u0629 \u0644\u0623\u0639\u0644\u0649 \u0645\u0633\u062a\u0648\u0649 \u0636\u062f \u0627\u0644\u0643\u062f\u0645\u0627\u062a \u0648\u0627\u0644\u062e\u062f\u0648\u0634 \u0641\u064a \u0645\u0631\u0627\u062d\u0644\u0647\u0645 \u0627\u0644\u0623\u0648\u0644\u0649 \u0645\u0646 \u0627\u0644\u062d\u0628\u0648 \ud83d\udc3e \u0648\u0627\u0644\u0645\u0634\u064a. \\n\\n\ud83c\udfe1 **\u0645\u0645\u064a\u0632\u0627\u062a \u0627\u0644\u0645\u0646\u062a\u062c \u0627\u0644\u0631\u0627\u0626\u0639\u0629:** \\n\\n- \ud83c\udf1f **\u0642\u0645\u0627\u0634 \u0645\u0631\u0646 \u0648\u0645\u0631\u064a\u062d:** \u062a\u0645 \u062a\u0635\u0646\u064a\u0639 \u0627\u0644\u0634\u0631\u0627\u0628 \u0645\u0646 \u0642\u0645\u0627\u0634 \u0645\u0631\u0646 \u064a\u062a\u0643\u064a\u0641 \u0645\u0639 \u0634\u0643\u0644 \u0648\u0645\u0646\u062d\u0646\u064a\u0627\u062a \u0627\u0644\u0631\u0643\u0628\u0629\u060c \u0645\u0645\u0627 \u064a\u0636\u0645\u0646 \u0631\u0627\u062d\u0629 \u0643\u0627\u0645\u0644\u0629 \u0644\u0637\u0641\u0644\u0643 \u0623\u062b\u0646\u0627\u0621 \u0627\u0644\u062d\u0631\u0643\u0629. \\n\\n- \ud83d\udee1\ufe0f **\u062d\u0645\u0627\u064a\u0629 \u0645\u0643\u062b\u0641\u0629:** \u062a\u062a\u0645\u064a\u0632 \u0645\u0646\u0637\u0642\u0629 \u062d\u0645\u0627\u064a\u0629 \u0627\u0644\u0631\u0643\u0628\u0629 \u0628\u0648\u062c\u0648\u062f \u0627\u0644\u0633\u0644\u064a\u0643\u0648\u0646 \u0645\u0627\u0646\u0639 \u0627\u0644\u0627\u0646\u0632\u0644\u0627\u0642 \u0627\u0644\u0630\u064a \u064a\u0642\u0648\u0645 \u0628\u0627\u0645\u062a\u0635\u0627\u0635 \u0627\u0644\u0635\u062f\u0645\u0627\u062a \u0644\u0636\u0645\u0627\u0646 \u0623\u0641\u0636\u0644 \u062d\u0645\u0627\u064a\u0629 \u0644\u0631\u0643\u0628\u0629 \u0627\u0644\u0637\u0641\u0644. \\n\\n- \ud83d\ude80 **\u062d\u0631\u064a\u0629 \u0627\u0644\u062d\u0631\u0643\u0629:** \u062a\u0645 \u062a\u0635\u0645\u064a\u0645 \u0627\u0644\u0648\u0627\u0642\u064a\u0627\u062a \u0628\u062d\u064a\u062b \u0644\u0627 \u062a\u0624\u062b\u0631 \u0639\u0644\u0649 \u062d\u0631\u064a\u0629 \u062d\u0631\u0643\u0629 \u0627\u0644\u0637\u0641\u0644\u060c \u0645\u0645\u0627 \u064a\u0645\u0646\u062d \u0627\u0644\u0623\u0645\u0647\u0627\u062a \u0631\u0627\u062d\u0629 \u0627\u0644\u0628\u0627\u0644 \u0648\u0627\u0644\u0645\u062a\u0627\u0646\u0629 \u0641\u064a \u0646\u0641\u0633 \u0627\u0644\u0648\u0642\u062a. \\n\\n\u0627\u062c\u0639\u0644 \u0643\u0644 \u062e\u0637\u0648\u0629 \u0644\u0635\u063a\u064a\u0631\u0643 \u0622\u0645\u0646\u0629 \ud83e\uddb6 \u0648\u0645\u0631\u064a\u062d\u0629 \u0645\u0639 \u0647\u0630\u0647 \u0627\u0644\u0648\u0627\u0642\u064a\u0627\u062a \u0627\u0644\u062a\u064a \u0644\u0627 \u064a\u0645\u0643\u0646 \u0627\u0644\u0627\u0633\u062a\u063a\u0646\u0627\u0621 \u0639\u0646\u0647\u0627! \u0627\u0644\u0622\u0646 \u064a\u0645\u0643\u0646 \u0644\u0635\u063a\u064a\u0631\u0643 \u0627\u0644\u0627\u0633\u062a\u0643\u0634\u0627\u0641 \ud83d\udcda \u0648\u0627\u0644\u0644\u0639\u0628 \u0628\u0643\u0644 \u062b\u0642\u0629.\"\n}\n```","behavior":"degenerate_repetition","conversation_hash":"d466f08ab3b5c7c1c1c2da10548f956c","conversation_redacted":false,"conversation_toxic":false,"country":"Denmark","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":1568,"n_turns_in_conversation":1,"n_words":253,"source_row_offset":52294,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:06 GMT","turn_id":"1095bf8dd85a453baf238ab5","turn_index":0,"user_text":"Generate a captivating product description for listing.\n Aim for engaging language that captivates potential buyers and highlights why this product is a must-have.\n Include details such as [points, sub-points, '\n', '-']. \n , Use of emojis related to the subject of the description such as \ud83c\udfe1 , at the beginning of each main point., Return the generated description in json format with 'description' key and value type string.\n The product is \"\u0627\u0644\u0645\u0648\u0627\u0635\u0641\u0627\u062a:\n\u0648\u0627\u0642\u064a\u0627\u062a \u0631\u0643\u0628\u0629 \u0644\u0644\u0623\u0637\u0641\u0627\u0644 \u064a\u062d\u0627\u0641\u0638 \u0639 \u0637\u0641\u0644\u0643 \u0645\u0646 \u0627\u0644\u0625\u0635\u0627\u0628\u0629 \u0628\u0627\u0644\u0643\u062f\u0645\u0627\u062a \u0648\u0627\u0644\u062e\u062f\u0648\u0634 \u0623\u062b\u0646\u0627\u0621 \u062a\u0639\u0644\u0645 \u0627\u0644\u0645\u0634\u064a \u0648\u0627\u0644\u062d\u0628\u0648 \u0641\u064a \u0634\u0647\u0648\u0631\u0647 \u0627\u0644\u0623\u0648\u0644\u0649. \r\n\u0627\u0644\u0634\u0631\u0627\u0628 \u0645\u0635\u0646\u0639 \u0645\u0646 \u0642\u0645\u0627\u0634 \u0645\u0631\u0646 \u064a\u062a\u062e\u0630 \u0634\u0643\u0644 \u0648\u0645\u0646\u062d\u0646\u064a\u0627\u062a \u0627\u0644\u0631\u0643\u0628\u0629 \u0623\u062b\u0646\u0627\u0621 \u0627\u0644\u062d\u0631\u0643\u0629 \u0628\u0643\u0644 \u0633\u0647\u0648\u0644\u0629. \r\n\u0645\u0646\u0637\u0642\u0629 \u062d\u0645\u0627\u064a\u0629 \u0627\u0644\u0631\u0643\u0628\u0629 \u0645\u0646 \u0627\u0644\u0633\u0644\u064a\u0643\u0648\u0646 \u0645\u0627\u0646\u0639 \u0627\u0644\u0627\u0646\u0632\u0644\u0627\u0642 \u0644\u0627\u0645\u062a\u0635\u0627\u0635 \u0627\u0644\u0635\u062f\u0645\u0627\u062a \u0648\u0636\u0645\u0627\u0646 \u0623\u0641\u0636\u0644 \u062d\u0645\u0627\u064a\u0629 \u0644\u0631\u0643\u0628\u0629 \u0627\u0644\u0637\u0641\u0644\r\n. \u0644\u0627 \u064a\u0624\u062b\u0631 \u0639 \u062d\u0631\u064a\u0629 \u062d\u0631\u0643\u0629 \u0627\u0644\u0637\u0641\u0644.\" \n Remove brands(Store Name) or replace it if required."},{"assistant_initiates":false,"asst_text":"\u4ece\u4ee3\u7801\u4e2d\u60a8\u5df2\u7ecf\u6b63\u786e\u5730\u6307\u5b9a\u4e86\u5b57\u4f53\u8def\u5f84\uff0c\u5e76\u5c06\u5b57\u4f53\u5c5e\u6027\u5e94\u7528\u4e8e matplotlib \u7684 `rcParams` \u4e2d\uff0c\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u89e3\u51b3\u5b57\u4f53\u7f3a\u5931\u8b66\u544a\u7684\u95ee\u9898\u3002\u7136\u800c\uff0c\u53ea\u8981\u786e\u4fdd\u6240\u7528\u5b57\u4f53\u5728\u7ed8\u56fe\u4e2d\u53ef\u4ee5\u6b63\u786e\u52a0\u8f7d\u5e76\u4e14\u5176\u8def\u5f84\u6b63\u786e\u5373\u53ef\u3002\n\n\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5173\u4e8e\u5982\u4f55\u5728\u60a8\u7684\u4ee3\u7801\u4e2d\u5e94\u7528\u5b57\u4f53\u8bbe\u7f6e\u7684\u793a\u4f8b\uff1a\n\n```python\nimport os\nimport gymnasium as gym\nimport numpy as np\nfrom stable_baselines3 import PPO\nfrom stable_baselines3.common.env_util import make_vec_env\nimport openmc\nimport matplotlib.pyplot as plt\nfrom matplotlib import font_manager\n\n# \u8bbe\u7f6e\u6838\u6570\u636e\u8def\u5f84\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\n\n# \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53\u8def\u5f84\nfont_path = '/home/unknowxxs/ttf/DejaVuSans.ttf' # \u5c06\u5176\u66ff\u6362\u4e3a\u4e2d\u6587\u5b57\u4f53\u7684\u8def\u5f84\nprop = font_manager.FontProperties(fname=font_path)\nplt.rcParams['font.family'] = prop.get_name()\n\nclass NuclearReactorEnv(gym.Env):\n def __init__(self):\n super().__init__()\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\n \n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\n self.observation_space = gym.spaces.Box(\n low=np.array([500, 0, 0.5, 0, 0]),\n high=np.array([1500, 3000, 1.5, 1, 100]),\n dtype=np.float32\n )\n \n self.max_steps = 1000\n self.current_step = 0\n self._setup_model()\n \n def _setup_model(self):\n # \u8bbe\u7f6e\u6750\u6599\n # UO2\u71c3\u6599\n self.fuel = openmc.Material(name='fuel')\n self.fuel.add_nuclide('U235', 0.05)\n self.fuel.add_nuclide('U238', 0.95)\n self.fuel.add_nuclide('O16', 2.0)\n self.fuel.set_density('g/cm3', 10.0)\n self.fuel.temperature = 293.6 # K\n\n # \u6c34\u51b7\u5374\u5242\n self.water = openmc.Material(name='water')\n self.water.add_nuclide('H1', 2)\n self.water.add_nuclide('O16', 1)\n self.water.set_density('g/cm3', 1.0)\n self.water.temperature = 293.6 # K\n\n # \u63a7\u5236\u68d2 (B4C)\n self.control_rod = openmc.Material(name='control_rod')\n self.control_rod.add_nuclide('B10', 0.8)\n self.control_rod.add_nuclide('B11', 0.2)\n self.control_rod.add_nuclide('C12', 1.0)\n self.control_rod.set_density('g/cm3', 2.52)\n\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\n\n # \u521b\u5efa\u51e0\u4f55\u4f53\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\n fuel_region = -cylinder\n \n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\n\n # \u521b\u5efauniverse\u548cgeometry\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\n self.geometry = openmc.Geometry(root_universe)\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\n\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\n self.settings = openmc.Settings()\n self.settings.batches = 100\n self.settings.inactive = 10\n self.settings.particles = 1000\n self.settings.temperature = {'method': 'interpolation'}\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\n \n def init_visualization(self):\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\n \n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\n self.history = {\n 'temperature': [],\n 'power': [],\n 'k_eff': [],\n 'burnup': [],\n 'control_rod': [],\n 'steps': []\n }\n \n # \u914d\u7f6e\u5b50\u56fe\n self.lines = {}\n \n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\n self.axs[0, 0].legend()\n \n # k_eff\u56fe\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\n self.axs[0, 1].legend()\n \n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\n self.axs[1, 0].legend()\n \n # \u71c3\u8017\u56fe\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\n self.axs[1, 1].legend()\n \n plt.tight_layout()\n\n def update_visualization(self):\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\n self.history['temperature'].append(self.temperature)\n self.history['power'].append(self.power)\n self.history['k_eff'].append(self.k_eff)\n self.history['burnup'].append(self.burnup)\n self.history['control_rod'].append(self.control_rod_pos)\n self.history['steps'].append(self.current_step)\n \n # \u66f4\u65b0\u56fe\u8868\n steps = self.history['steps']\n \n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\n self.lines['temp'].set_data(steps, self.history['temperature'])\n self.lines['power'].set_data(steps, self.history['power'])\n self.axs[0, 0].relim()\n self.axs[0, 0].autoscale_view()\n \n # \u66f4\u65b0k_eff\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\n self.axs[0, 1].relim()\n self.axs[0, 1].autoscale_view()\n \n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\n self.lines['control'].set_data(steps, self.history['control_rod'])\n self.axs[1, 0].relim()\n self.axs[1, 0].autoscale_view()\n \n # \u66f4\u65b0\u71c3\u8017\n self.lines['burnup'].set_data(steps, self.history['burnup'])\n self.axs[1, 1].relim()\n self.axs[1, 1].autoscale_view()\n \n plt.draw()\n plt.pause(0.01)\n\n def reset(self, seed=None):\n super().reset(seed=seed)\n self.current_step = 0\n self.temperature = 600 # K\n self.power = 1000 # MW\n self.k_eff = 1.0\n self.burnup = 0 # MWd/kgU\n self.control_rod_pos = 0.5\n \n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\n self.init_visualization()\n \n return self._get_observation(), {}\n \n def _get_observation(self):\n return np.array([\n self.temperature,\n self.power,\n self.k_eff,\n self.control_rod_pos,\n self.burnup\n ], dtype=np.float32)\n \n def step(self, action):\n self.current_step += 1\n \n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\n self.control_rod_pos = action / 10.0\n \n # \u8fd0\u884cOpenMC\u6a21\u62df\n try:\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\n self.fuel.temperature = self.temperature\n \n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\n openmc.run()\n print(\"\\033[91m\u6a21\u62df\u7ed3\u679c: \")\n # \u83b7\u53d6k_eff\n with openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\n self.k_eff = sp.k_effective.nominal_value\n \n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\n self._update_state()\n \n # \u66f4\u65b0\u53ef\u89c6\u5316\n self.update_visualization()\n \n # \u8ba1\u7b97\u5956\u52b1\n reward = self._calculate_reward()\n \n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\n done = self.current_step >= self.max_steps or self._is_unsafe()\n \n except Exception as e:\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\n reward = -1000\n done = True\n \n return self._get_observation(), reward, done, False, {}\n \n def _update_state(self):\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\n power_factor = (self.k_eff - 1.0) * 5000\n self.power = np.clip(self.power + power_factor, 0, 3000)\n \n # \u66f4\u65b0\u6e29\u5ea6\n delta_temp = (self.power - 1000) * 0.1\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\n \n # \u66f4\u65b0\u71c3\u8017\n self.burnup += self.power * 0.001\n \n def _calculate_reward(self):\n reward = 0\n \n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\n \n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\n if self._is_unsafe():\n reward -= 1000\n \n return reward\n \n def _is_unsafe(self):\n return (self.temperature > 1400 or \n self.power > 2500 or \n self.k_eff > 1.2)\n\ndef train_agent():\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\n \n model = PPO(\n \"MlpPolicy\",\n env,\n verbose=1,\n learning_rate=0.0003,\n n_steps=2048,\n batch_size=64,\n n_epochs=10\n )\n \n model.learn(total_timesteps=1)\n model.save(\"ppo_nuclear_reactor\")\n \n return model\n\ndef evaluate_agent(model, num_episodes=5):\n env = NuclearReactorEnv()\n \n for episode in range(num_episodes):\n obs, _ = env.reset()\n episode_reward = 0\n done = False\n \n while not done:\n action, _ = model.predict(obs)\n obs, reward, done, _, _ = env.step(action)\n episode_reward += reward\n \n # \u6253\u5370\u5f53\u524d\u72b6\u6001\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\n print(f\"k_eff: {env.k_eff:.3f}\")\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\n print(\"------------------------\")\n \n plt.savefig(f'episode_{episode+1}_results.png')\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\n \n plt.close('all')\n\nmodel = train_agent()\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\nevaluate_agent(model)\n```\n\n\u8bf7\u786e\u4fdd\u8def\u5f84 `/home/unknowxxs/ttf/DejaVuSans.ttf` \u4e2d\u7684\u5b57\u4f53\u6587\u4ef6\u5b58\u5728\uff0c\u6216\u8005\u4f7f\u7528\u53e6\u4e00\u4e2a\u6709\u6548\u7684\u4e2d\u6587\u5b57\u4f53\u6587\u4ef6\u8def\u5f84\u3002\u5982\u679c\u6b64\u5b57\u4f53\u672a\u80fd\u6b63\u786e\u663e\u793a\u4e2d\u6587\u5b57\u7b26\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u9009\u62e9\u5176\u4ed6\u652f\u6301 CJK\uff08\u4e2d\u6587\u3001\u65e5\u6587\u3001\u97e9\u6587\uff09\u5b57\u7b26\u7684\u5b57\u4f53\uff0c\u5982 Noto Sans CJK \u6216 SimSun\u3002","behavior":"degenerate_repetition","conversation_hash":"10f71e689e1422deafb212e546e04d03","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":144652,"n_turns_in_conversation":1,"n_words":9866,"source_row_offset":52295,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:10 GMT","turn_id":"f1c8e0ee695417bab92235eb","turn_index":0,"user_text":"User: \u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: No boundary conditions were applied to any surfaces! ---------------\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nUser: \u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: No boundary conditions were applied to any surfaces! ---------------\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u4ece\u9519\u8bef\u6d88\u606f \"No boundary conditions were applied to any surfaces!\"\uff08\u672a\u5c06\u8fb9\u754c\u6761\u4ef6\u5e94\u7528\u4e8e\u4efb\u4f55\u8868\u9762\uff09\u6765\u770b\uff0c\u60a8\u7684OpenMC\u6a21\u62df\u53ef\u80fd\u7f3a\u5c11\u5bf9\u51e0\u4f55\u8868\u9762\u7684\u8fb9\u754c\u6761\u4ef6\u8bbe\u7f6e\u3002\u5728OpenMC\u4e2d\uff0c\u5b9a\u4e49\u51e0\u4f55\u8fb9\u754c\u6761\u4ef6\u662f\u5fc5\u9700\u7684\uff0c\u4ee5\u786e\u5b9a\u7c92\u5b50\u5728\u4e0e\u51e0\u4f55\u8868\u9762\u76f8\u4e92\u4f5c\u7528\u65f6\u7684\u884c\u4e3a\u3002\n\n\u4ee5\u4e0b\u662f\u4e00\u4e9b\u53ef\u4ee5\u5c1d\u8bd5\u7684\u6b65\u9aa4\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff1a\n\n1. **\u5b9a\u4e49\u51e0\u4f55\u8fb9\u754c\u6761\u4ef6**\uff1a\n \u5728\u5b9a\u4e49\u51e0\u4f55\u4f53\u65f6\uff0c\u5982\u679c\u51e0\u4f55\u5305\u542b\u65e0\u9650\u7a7a\u95f4\u6216\u60a8\u5e0c\u671b\u8bbe\u7f6e\u5468\u8fb9\u7684\u8fb9\u754c\u884c\u4e3a\uff0c\u60a8\u9700\u8981\u4e3a\u8868\u9762\u6307\u5b9a\u8fb9\u754c\u6761\u4ef6\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u7528 `boundary_type` \u53c2\u6570\u6765\u8bbe\u7f6e\u8868\u9762\u7684\u8fb9\u754c\u6761\u4ef6\u3002\n\n \u589e\u52a0\u8fb9\u754c\u6761\u4ef6\u7684\u8bbe\u5b9a\u5230\u60a8\u7684\u51e0\u4f55\u4f53\u5b9a\u4e49\u4e2d\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60f3\u5b9a\u4e49\u53cd\u5c04\u8fb9\u754c\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a\n\n ```python\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective')\n ```\n\n2. **\u68c0\u67e5\u51e0\u4f55\u4f53\u5b8c\u6574\u6027**\uff1a\n \u786e\u4fdd\u51e0\u4f55\u4f53\u662f\u95ed\u5408\u7684\uff0c\u5e76\u4e14\u6750\u6599\u5206\u914d\u662f\u5728\u5b9a\u4e49\u7684\u533a\u57df\u5185\u7684\uff0c\u51e0\u4f55\u4f53\u9700\u8981\u662f\u5b8c\u6574\u4e14\u5c01\u95ed\u7684\u3002\n\n3. **\u589e\u52a0\u5916\u90e8\u201c\u76d2\u5b50\u201d**\uff1a\n \u5728\u53cd\u5e94\u5806\u6216\u611f\u5174\u8da3\u7684\u533a\u57df\u5916\u589e\u52a0\u4e00\u4e2a\u5927\u5c3a\u5bf8\u7684\u76d2\u5b50\uff0c\u5e76\u5e94\u7528\u201c\u5916\u201d\u8fb9\u754c\u6761\u4ef6\uff08\u5982\u771f\u7a7a\u6216\u53cd\u5c04\uff09\uff1a\n\n ```python\n outer_box = openmc.model.rectangular_prism(\n width=200, height=200, boundary_type='reflective'\n )\n ```\n\n \u5e76\u5728\u521b\u5efageometry\u7684\u65f6\u5019\u5305\u62ec\u8fd9\u4e2a\u5916\u90e8\u201c\u76d2\u5b50\u201d\uff1a\n\n ```python\n outer_region = +outer_box & -cylinder\n fuel_cell = openmc.Cell(fill=self.fuel, region=outer_region)\n ```\n\n\u901a\u8fc7\u4ee5\u4e0a\u8bbe\u5b9a\uff0c\u53ef\u4ee5\u786e\u4fdd\u5728OpenMC\u4e2d\u7684\u51e0\u4f55\u4f53\u4e2d\u5e94\u7528\u4e86\u6b63\u786e\u7684\u8fb9\u754c\u6761\u4ef6\uff0c\u4ece\u800c\u6d88\u9664\u8fd9\u4e2a\u9519\u8bef\u3002\u5982\u679c\u95ee\u9898\u4ecd\u7136\u5b58\u5728\uff0c\u8bf7\u786e\u4fdd\u4ee3\u7801\u4e2d\u7684\u51e0\u4f55\u548c\u6750\u6599\u8bbe\u7f6e\u4ee3\u7801\u90fd\u5df2\u5305\u542b\u5728\u5185\uff0c\u5e76\u4e14\u6ca1\u6709\u9057\u6f0f\u5176\u4ed6\u6240\u9700\u7684\u51e0\u4f55\u4f53\u6216\u8bbe\u7f6e\u3002\nUser: \u5c06\u4fee\u6539\u7684\u5168\u90e8\u4ee3\u7801\u5199\u51fa\u6765\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nUser: \u5c06\u521a\u624d\u6ca1\u8f93\u51fa\u5b8c\u6210\u7684\u63a5\u4e0a\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u4e3a\u4e86\u4fee\u590d\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: No boundary conditions were applied to any surfaces!\" \u7684\u95ee\u9898\uff0c\u9700\u8981\u786e\u4fdd\u5728OpenMC\u7684\u51e0\u4f55\u4e2d\u6b63\u786e\u5b9a\u4e49\u4e86\u8fb9\u754c\u6761\u4ef6\u3002\u8fd9\u91cc\u662f\u4fee\u6539\u540e\u7684\u5b8c\u6574\u4ee3\u7801\uff0c\u5305\u62ec\u8fb9\u754c\u6761\u4ef6\u7684\u8bbe\u7f6e\uff1a\n\n```python\nimport os\nimport gymnasium as gym\nimport numpy as np\nfrom stable_baselines3 import PPO\nfrom stable_baselines3.common.env_util import make_vec_env\nimport openmc\nimport matplotlib.pyplot as plt\n\n#plt.style.use(plt.style.available)\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\nclass NuclearReactorEnv(gym.Env):\n def __init__(self):\n super().__init__()\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\n \n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\n self.observation_space = gym.spaces.Box(\n low=np.array([500, 0, 0.5, 0, 0]),\n high=np.array([1500, 3000, 1.5, 1, 100]),\n dtype=np.float32\n )\n \n self.max_steps = 1000\n self.current_step = 0\n self._setup_model()\n \n def _setup_model(self):\n # \u8bbe\u7f6e\u6750\u6599\n # UO2\u71c3\u6599\n self.fuel = openmc.Material(name='fuel')\n self.fuel.add_nuclide('U235', 0.05)\n self.fuel.add_nuclide('U238', 0.95)\n self.fuel.add_nuclide('O16', 2.0)\n self.fuel.set_density('g/cm3', 10.0)\n self.fuel.temperature = 900 # K\n\n # \u6c34\u51b7\u5374\u5242\n self.water = openmc.Material(name='water')\n self.water.add_nuclide('H1', 2)\n self.water.add_nuclide('O16', 1)\n self.water.set_density('g/cm3', 1.0)\n self.water.temperature = 600 # K\n\n # \u63a7\u5236\u68d2 (B4C)\n self.control_rod = openmc.Material(name='control_rod')\n self.control_rod.add_nuclide('B10', 0.8)\n self.control_rod.add_nuclide('B11', 0.2)\n self.control_rod.add_nuclide('C12', 1.0)\n self.control_rod.set_density('g/cm3', 2.52)\n\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\n\n # \u521b\u5efa\u51e0\u4f55\u4f53\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\n fuel_region = -cylinder\n \n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\n\n # \u521b\u5efauniverse\u548cgeometry\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\n self.geometry = openmc.Geometry(root_universe)\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\n\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\n self.settings = openmc.Settings()\n self.settings.batches = 100\n self.settings.inactive = 10\n self.settings.particles = 1000\n self.settings.temperature = {'method': 'interpolation'}\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\n \n def init_visualization(self):\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\n \n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\n self.history = {\n 'temperature': [],\n 'power': [],\n 'k_eff': [],\n 'burnup': [],\n 'control_rod': [],\n 'steps': []\n }\n \n # \u914d\u7f6e\u5b50\u56fe\n self.lines = {}\n \n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\n self.axs[0, 0].legend()\n \n # k_eff\u56fe\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\n self.axs[0, 1].legend()\n \n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\n self.axs[1, 0].legend()\n \n # \u71c3\u8017\u56fe\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\n self.axs[1, 1].legend()\n \n plt.tight_layout()\n\n def update_visualization(self):\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\n self.history['temperature'].append(self.temperature)\n self.history['power'].append(self.power)\n self.history['k_eff'].append(self.k_eff)\n self.history['burnup'].append(self.burnup)\n self.history['control_rod'].append(self.control_rod_pos)\n self.history['steps'].append(self.current_step)\n \n # \u66f4\u65b0\u56fe\u8868\n steps = self.history['steps']\n \n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\n self.lines['temp'].set_data(steps, self.history['temperature'])\n self.lines['power'].set_data(steps, self.history['power'])\n self.axs[0, 0].relim()\n self.axs[0, 0].autoscale_view()\n \n # \u66f4\u65b0k_eff\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\n self.axs[0, 1].relim()\n self.axs[0, 1].autoscale_view()\n \n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\n self.lines['control'].set_data(steps, self.history['control_rod'])\n self.axs[1, 0].relim()\n self.axs[1, 0].autoscale_view()\n \n # \u66f4\u65b0\u71c3\u8017\n self.lines['burnup'].set_data(steps, self.history['burnup'])\n self.axs[1, 1].relim()\n self.axs[1, 1].autoscale_view()\n \n plt.draw()\n plt.pause(0.01)\n\n def reset(self, seed=None):\n super().reset(seed=seed)\n self.current_step = 0\n self.temperature = 600 # K\n self.power = 1000 # MW\n self.k_eff = 1.0\n self.burnup = 0 # MWd/kgU\n self.control_rod_pos = 0.5\n \n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\n self.init_visualization()\n \n return self._get_observation(), {}\n \n def _get_observation(self):\n return np.array([\n self.temperature,\n self.power,\n self.k_eff,\n self.control_rod_pos,\n self.burnup\n ], dtype=np.float32)\n \n def step(self, action):\n self.current_step += 1\n \n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\n self.control_rod_pos = action / 10.0\n \n # \u8fd0\u884cOpenMC\u6a21\u62df\n try:\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\n self.fuel.temperature = self.temperature\n \n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\n result = openmc.run()\n \n # \u83b7\u53d6k_eff\n with openmc.StatePoint(result) as sp:\n self.k_eff = sp.k_effective.nominal_value\n \n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\n self._update_state()\n \n # \u66f4\u65b0\u53ef\u89c6\u5316\n self.update_visualization()\n \n # \u8ba1\u7b97\u5956\u52b1\n reward = self._calculate_reward()\n \n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\n done = self.current_step >= self.max_steps or self._is_unsafe()\n \n except Exception as e:\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\n reward = -1000\n done = True\n \n return self._get_observation(), reward, done, False, {}\n \n def _update_state(self):\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\n power_factor = (self.k_eff - 1.0) * 5000\n self.power = np.clip(self.power + power_factor, 0, 3000)\n \n # \u66f4\u65b0\u6e29\u5ea6\n delta_temp = (self.power - 1000) * 0.1\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\n \n # \u66f4\u65b0\u71c3\u8017\n self.burnup += self.power * 0.001\n \n def _calculate_reward(self):\n reward = 0\n \n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\n \n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\n if self._is_unsafe():\n reward -= 1000\n \n return reward\n \n def _is_unsafe(self):\n return (self.temperature > 1400 or \n self.power > 2500 or \n self.k_eff > 1.2)\n\ndef train_agent():\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\n \n model = PPO(\n \"MlpPolicy\",\n env,\n verbose=1,\n learning_rate=0.0003,\n n_steps=2048,\n batch_size=64,\n n_epochs=10\n )\n \n model.learn(total_timesteps=10)\n model.save(\"ppo_nuclear_reactor\")\n \n return model\n\ndef evaluate_agent(model, num_episodes=5):\n env = NuclearReactorEnv()\n \n for episode in range(num_episodes):\n obs, _ = env.reset()\n episode_reward = 0\n done = False\n \n while not done:\n action, _ = model.predict(obs)\n obs, reward, done, _, _ = env.step(action)\n episode_reward += reward\n \n # \u6253\u5370\u5f53\u524d\u72b6\u6001\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\n print(f\"k_eff: {env.k_eff:.3f}\")\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\n print(\"------------------------\")\n \n plt.savefig(f'episode_{episode+1}_results.png')\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\n \n plt.close('all')\n\nmodel = train_agent()\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\nevaluate_agent(model)\n```\n\n\u5728\u8fd9\u4e2a\u7248\u672c\u4e2d\uff0c\u6211\u5728\u521b\u5efa\u5706\u67f1\u4f53\u7684\u65f6\u5019\u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6 `boundary_type='reflective'`\u3002\u8fd9\u6837\u53ef\u4ee5\u9632\u6b62\u9519\u8bef \"No boundary conditions were applied to any surfaces!\" \u7684\u53d1\u751f\u3002\u8fd9\u6837\u4e3a\u51e0\u4f55\u4f53\u5b9a\u4e49\u4e86\u5408\u9002\u7684\u8fb9\u754c\u6761\u4ef6\u4ee5\u786e\u4fdd\u6a21\u62df\u7684\u7c92\u5b50\u884c\u4e3a\u662f\u5b8c\u6574\u7684\u3002\nUser: ERROR: Nuclear data library does not contain cross sections for U235 at or near\n 900.000000 K.\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u9519\u8bef\u6d88\u606f \"Nuclear data library does not contain cross sections for U235 at or near 900.000000 K\" \u6307\u51fa\u60a8\u7684\u6838\u6570\u636e\u5e93\u4e2d\u6ca1\u6709\u5728900 K\u9644\u8fd1\u7684\u94c0-235\u7684\u622a\u9762\u6570\u636e\u3002\u8fd9\u53ef\u80fd\u662f\u6838\u6570\u636e\u5e93\u7684\u6e29\u5ea6\u8303\u56f4\u4e0d\u5305\u62ec900 K\uff0c\u6216\u8005\u8be5\u6e29\u5ea6\u9644\u8fd1\u6ca1\u6709\u8fdb\u884c\u63d2\u503c\u3002\n\n\u4ee5\u4e0b\u662f\u4e00\u4e9b\u89e3\u51b3\u65b9\u6848\uff1a\n\n1. **\u66f4\u6539\u6e29\u5ea6\u8bbe\u7f6e**\uff1a\n \u68c0\u67e5\u5e76\u66f4\u6539\u6750\u6599\u6e29\u5ea6\u8bbe\u7f6e\u4e3a\u60a8\u7684\u6838\u6570\u636e\u5e93\u63d0\u4f9b\u7684\u6e29\u5ea6\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u67e5\u9605\u6838\u6570\u636e\u5e93\u6587\u6863\u6765\u627e\u5230\u53ef\u7528\u7684\u6e29\u5ea6\u3002\u5982\u679c\u5e93\u4f8b\u5982\u63d0\u4f9b\u7684\u662f600 K\uff0c800 K\uff0c\u548c1000 K\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u5176\u4e2d\u7684\u4e00\u4e2a\uff1a\n\n ```python\n self.fuel.temperature = 1000 # \u4f7f\u7528\u53ef\u7528\u7684\u6e29\u5ea6\n ```\n\n2. **\u4f7f\u7528\u6e29\u5ea6\u63d2\u503c**\uff1a\n \u5982\u679c\u5e93\u652f\u6301\u6e29\u5ea6\u63d2\u503c\uff0c\u53ef\u4ee5\u8bbe\u7f6e `temperature['method']` \u4e3a 'interpolation'\uff0c\u4e0d\u8fc7\u60a8\u5df2\u7ecf\u8bbe\u7f6e\u4e86\u6b64\u9009\u9879\u3002\u786e\u4fdd\u6570\u636e\u76ee\u5f55\u4e2d\u7684 `cross_sections.xml` \u6587\u4ef6\u786e\u5b9e\u652f\u6301\u63d2\u503c\u3002\u5426\u5219\uff0c\u5728\u6240\u6709\u4f7f\u7528\u7684\u6838\u6750\u6599\u4e0a\u68c0\u67e5\u5e76\u4f7f\u7528\u786e\u5207\u53ef\u7528\u7684\u6e29\u5ea6\u3002\n\n3. **\u6838\u6570\u636e\u5e93\u95ee\u9898**\uff1a\n \u786e\u4fdd\u60a8\u7684\u6838\u6570\u636e\u76ee\u5f55\u914d\u7f6e\u8def\u5f84\u6b63\u786e\uff0c\u5e76\u4e14\u6570\u636e\u5b8c\u6574\u65e0\u8bef\u3002\u67d0\u4e9b\u6570\u636e\u5305\u53ef\u80fd\u9700\u8981\u624b\u52a8\u6dfb\u52a0\u652f\u6301\u6240\u9700\u6e29\u5ea6\u7684\u622a\u9762\u3002\n\n\u5982\u6240\u6709\u8fd9\u4e9b\u65b9\u6cd5\u5747\u4e0d\u6709\u6548\uff0c\u8bf7\u786e\u4fdd\u6838\u6570\u636e\u5e93\u4e2d\u786e\u5b9e\u5305\u542b\u6240\u4f7f\u7528\u7684\u6750\u6599\u548c\u6e29\u5ea6\u7684\u622a\u9762\u6570\u636e\uff0c\u5e76\u8003\u8651\u8c03\u6574\u60a8\u7684\u8bbe\u8ba1\u548c\u6a21\u62df\u5230\u652f\u6301\u7684\u8303\u56f4\u4e2d\u3002\nUser: \u65ad\u70b9\u7684\u4f5c\u7528\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u5728\u7a0b\u5e8f\u8c03\u8bd5\u4e2d\uff0c**\u65ad\u70b9**\u7684\u4f5c\u7528\u662f\u6682\u505c\u7a0b\u5e8f\u7684\u6267\u884c\u8fdb\u7a0b\uff0c\u4ee5\u4fbf\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u89c2\u5bdf\u548c\u68c0\u67e5\u7a0b\u5e8f\u5728\u8be5\u7279\u5b9a\u70b9\u65f6\u7684\u72b6\u6001\u3002\u8fd9\u5305\u62ec\u67e5\u770b\u53d8\u91cf\u7684\u503c\u3001\u7a0b\u5e8f\u6d41\u3001\u5185\u5b58\u72b6\u6001\u548c\u5176\u4ed6\u5b9e\u65f6\u4fe1\u606f\uff0c\u53ef\u4ee5\u5e2e\u52a9\u5f00\u53d1\u4eba\u5458\u7406\u89e3\u54ea\u91cc\u51fa\u4e86\u95ee\u9898\u4ee5\u53ca\u5176\u539f\u56e0\u3002\n\n\u5728Python\u4e2d\uff0c\u901a\u8fc7\u96c6\u6210\u5f00\u53d1\u73af\u5883\uff08IDE\uff09\u5982PyCharm\u3001Visual Studio Code\u548c\u5176\u4ed6\u8c03\u8bd5\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u5f88\u65b9\u4fbf\u5730\u8bbe\u7f6e\u65ad\u70b9\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4f7f\u7528\u65ad\u70b9\u7684\u4e3b\u8981\u76ee\u7684\u548c\u4f18\u70b9\uff1a\n\n1. **\u9010\u6b65\u6267\u884c**\uff1a\u901a\u8fc7\u5728\u5173\u952e\u4f4d\u7f6e\u8bbe\u7f6e\u65ad\u70b9\uff0c\u53ef\u4ee5\u4e00\u6b65\u4e00\u6b65\u5730\u624b\u52a8\u79fb\u52a8\u7a0b\u5e8f\u6267\u884c\uff08\u5373\u5355\u6b65\u8c03\u8bd5\uff09\uff0c\u4ece\u800c\u89c2\u5bdf\u7a0b\u5e8f\u72b6\u6001\u53ca\u5176\u53d8\u5316\u3002\n2. **\u68c0\u67e5\u72b6\u6001**\uff1a\u5728\u65ad\u70b9\u5904\uff0c\u53ef\u4ee5\u68c0\u67e5\u53d8\u91cf\u7684\u503c\u5e76\u66f4\u4e86\u89e3\u5176\u4e0a\u4e0b\u6587\uff0c\u8fd9\u5bf9\u4e8e\u627e\u51fa\u903b\u8f91\u9519\u8bef\u3001\u9a8c\u8bc1\u7b97\u6cd5\u6b63\u786e\u6027\u975e\u5e38\u91cd\u8981\u3002\n3. **\u6761\u4ef6\u65ad\u70b9**\uff1a\u53ef\u4ee5\u8bbe\u7f6e\u67d0\u4e9b\u6761\u4ef6\u4e0b\u624d\u4f1a\u89e6\u53d1\u7684\u65ad\u70b9\uff0c\u4f7f\u5f97\u8c03\u8bd5\u8fc7\u7a0b\u66f4\u4e3a\u9ad8\u6548\uff0c\u8fd9\u6837\u4e0d\u4f1a\u4e3a\u6bcf\u6b21\u5355\u6b65\u90fd\u9700\u8981\u624b\u52a8\u79fb\u5230\u7279\u5b9a\u6761\u4ef6\u3002\n4. **\u52a8\u6001\u5206\u6790**\uff1a\u53ef\u4ee5\u8fd0\u884c\u5230\u7279\u5b9a\u70b9\u65f6\u6682\u505c\uff0c\u8fdb\u884c\u5185\u5b58\u8c03\u8bd5\u548c\u6027\u80fd\u5206\u6790\u3002\n\n\u5728\u60a8\u7684\u60c5\u51b5\u91cc\uff0c\u5982\u679c\u9047\u5230\u4e86\u6e29\u5ea6\u76f8\u5173\u7684\u622a\u9762\u6570\u636e\u9519\u8bef\uff0c\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u65ad\u70b9\u6682\u505c\u5230\u6e29\u5ea6\u8bbe\u7f6e\u90e8\u5206\uff0c\u68c0\u67e5\u5728\u8fd9\u4e2a\u9636\u6bb5\u7684\u53d8\u91cf\u503c\u548c\u6587\u4ef6\u8def\u5f84\u3002\u5728\u4f7f\u7528\u8c03\u8bd5\u5de5\u5177\u4e2d\u7684\u65ad\u70b9\uff0c\u53ef\u4ee5\u66f4\u7cbe\u786e\u5730\u53d1\u73b0\u95ee\u9898\u7684\u6839\u672c\u539f\u56e0\u3002\n\n\u5982\u679c\u60a8\u5e0c\u671b\u8fdb\u4e00\u6b65\u4e86\u89e3\u5982\u4f55\u5728\u7279\u5b9aIDE\u4e2d\u4f7f\u7528\u65ad\u70b9\uff0c\u6211\u53ef\u4ee5\u63d0\u4f9b\u9488\u5bf9\u6027\u5efa\u8bae\u3002\nUser: with openmc.StatePoint(result) as sp:\n self.k_eff = sp.k_effective.nominal_value\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u5728\u60a8\u7684\u4ee3\u7801\u4e2d\uff0c\u4ee5\u4e0b\u8fd9\u6bb5\u4ee3\u7801\u5728\u83b7\u53d6\u6838\u53cd\u5e94\u5806\u6a21\u62df\u7ed3\u679c\u4e2d\u7684\u6709\u6548\u589e\u6b96\u56e0\u5b50\uff08k_eff\uff09\uff1a\n\n```python\nwith openmc.StatePoint(result) as sp:\n self.k_eff = sp.k_effective.nominal_value\n```\n\n\u8fd9\u6bb5\u4ee3\u7801\u7684\u4f5c\u7528\u89e3\u8bfb\u5982\u4e0b\uff1a\n\n1. **`openmc.StatePoint(result)`**:\n - `result` \u662f openmc \u8fd0\u884c\u51fd\u6570 `openmc.run()` \u7684\u8f93\u51fa\uff0c\u8fd9\u901a\u5e38\u662f\u5305\u542b\u5728\u60a8\u7684\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55\u6216\u6307\u5b9a\u8def\u5f84\u4e2d\u7684\u201cstatepoint\u201d\u6587\u4ef6\u3002\u8be5\u6587\u4ef6\u5305\u542b\u6a21\u62df\u5b8c\u6210\u540e\u7684\u7c92\u5b50\u8ba1\u7b97\u7edf\u8ba1\u7ed3\u679c\u3002\n - `openmc.StatePoint` \u7528\u4e8e\u52a0\u8f7d\u5e76\u8bbf\u95ee statepoint \u6587\u4ef6\u4e2d\u7684\u6a21\u62df\u7ed3\u679c\u3002\n\n2. **\u4f7f\u7528 `with` \u8bed\u53e5**:\n - `with` \u8bed\u53e5\u662f Python \u4e2d\u7528\u4e8e\u8fdb\u884c\u4e0a\u4e0b\u6587\u7ba1\u7406\uff0c\u786e\u4fdd\u5728\u79bb\u5f00 `with` \u5757\u65f6\uff0c\u8d44\u6e90\uff08\u8fd9\u91cc\u662f\u6253\u5f00\u7684 statepoint \u6587\u4ef6\uff09\u81ea\u52a8\u6b63\u786e\u5173\u95ed\u3002\n - \u8fd9\u6837\u505a\u4e0d\u4ec5\u662f\u826f\u597d\u7684\u8d44\u6e90\u7ba1\u7406\u5b9e\u8df5\uff0c\u8fd8\u53ef\u4ee5\u9632\u6b62\u6253\u5f00\u6587\u4ef6\u540e\u5fd8\u8bb0\u5173\u95ed\u5bfc\u81f4\u7684\u8d44\u6e90\u6cc4\u9732\u3002\n\n3. **`k_effective.nominal_value`**:\n - `sp.k_effective` \u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u6709\u6548\u500d\u589e\u56e0\u6570\u7ec4\u4ef6\u7684\u5bf9\u8c61\uff0c\u5305\u62ec\u5176\u540d\u4e49\u503c\u3001\u6807\u51c6\u504f\u5dee\u7b49\u3002\n - `.nominal_value` \u83b7\u53d6\u8be5\u6709\u6548\u500d\u589e\u56e0\u5b50\u7684\u5747\u503c\uff08nominal value\uff09\uff0c\u8fd9\u662f\u6709\u6548\u5ea6\u91cf\u53cd\u5e94\u5806\u4e34\u754c\u6027\u65b9\u7a0b\u7684\u6807\u51c6\u65b9\u6cd5\u3002\n\n\u901a\u8fc7\u8fd9\u4e2a\u8fc7\u7a0b\uff0c\u60a8\u5c06\u6a21\u62df\u7684\u4e3b\u8981\u76ee\u6807\u8f93\u51fa\u4e4b\u4e00\u2014\u2014\u6709\u6548\u500d\u589e\u56e0\u5b50\uff08k_eff\uff09\u52a0\u8f7d\u5230\u60a8\u7684\u6a21\u62df\u73af\u5883\u4e2d\uff0c\u7528\u4e8e\u540e\u7eed\u72b6\u6001\u66f4\u65b0\u6216\u7b56\u7565\u8bc4\u4f30\u3002\n\n\u5982\u679c\u5728\u8fd9\u4e2a\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u4e86\u9519\u8bef\uff0c\u5efa\u8bae\u786e\u8ba4\uff1a\n- \u8fd0\u884c\u7684\u6a21\u62df\u4ea7\u751f\u4e86\u6709\u6548\u7684 statepoint \u6587\u4ef6\u3002\n- `result` \u53d8\u91cf\u6307\u5411\u4e86\u6b63\u786e\u7684 statepoint \u6587\u4ef6\u8def\u5f84\u3002\n- openmc \u7684\u65e5\u5fd7\u4e2d\u662f\u5426\u6709\u5173\u4e8e\u6a21\u62df\u5931\u8d25\u7684\u5176\u4ed6\u539f\u56e0\u4fe1\u606f\uff0c\u5c24\u5176\u5728\u6838\u6750\u6599\u8bbe\u7f6e\u4e2d\u3002\nUser: result = openmc.run()\u8fd4\u56de\u4e3a\u7a7a\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n#plt.style.use(plt.style.available)\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 900 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 600 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100)\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\u5e76\u751f\u6210 XML\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n \r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # [\u4ee3\u7801\u521b\u5efa\u51e0\u4f55]\r\n \r\n # \u521b\u5efauniverse\u548cgeometry\u5e76\u751f\u6210 XML\r\n root_universe = openmc.Universe(cells=[fuel_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n \r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml \r\n \r\n \r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n # os.remove('/home/unknowxxs/python/statepoint.50.h5')\r\n result = openmc.run()\r\n \r\n \r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(result) as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=10)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u5982\u679c `result = openmc.run()` \u8fd4\u56de\u4e3a\u7a7a\uff0c\u53ef\u80fd\u662f\u56e0\u4e3a\u6a21\u62df\u672a\u80fd\u6210\u529f\u8fd0\u884c\u6216\u8005\u9519\u8bef\u53d1\u751f\u5728\u8fd0\u884c\u8fc7\u7a0b\u4e2d\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u53ef\u80fd\u7684\u539f\u56e0\u548c\u68c0\u67e5\u6b65\u9aa4\uff1a\n\n1. **\u68c0\u67e5 OpenMC \u8f93\u51fa\u548c\u65e5\u5fd7**\uff1a\n - \u68c0\u67e5 OpenMC \u751f\u6210\u7684\u8f93\u51fa\u6587\u4ef6\uff08\u4f8b\u5982 `statepoint` \u6587\u4ef6\uff09\u548c\u65e5\u5fd7\uff0c\u4ee5\u786e\u5b9a\u6a21\u62df\u662f\u5426\u6210\u529f\u5b8c\u6210\u3002\n - OpenMC \u7684\u65e5\u5fd7\u901a\u5e38\u4f1a\u6307\u51fa\u6a21\u62df\u5931\u8d25\u7684\u539f\u56e0\uff0c\u5305\u62ec\u51e0\u4f55\u914d\u7f6e\u95ee\u9898\u3001\u6750\u6599\u5b9a\u4e49\u9519\u8bef\u3001\u6838\u6570\u636e\u8def\u5f84\u4e0d\u6b63\u786e\u7b49\u3002\n\n2. **\u786e\u8ba4\u6587\u4ef6\u8def\u5f84**\uff1a\n - \u786e\u4fdd `cross_sections.xml` \u8def\u5f84\u6b63\u786e\uff0cOpenMC \u9700\u8981\u5b83\u6765\u5bfb\u627e\u6838\u6570\u636e\u3002\u5982\u679c\u8def\u5f84\u4e0d\u6b63\u786e\u6216\u8005\u6587\u4ef6\u635f\u574f\uff0cOpenMC \u5c06\u65e0\u6cd5\u52a0\u8f7d\u5fc5\u8981\u7684\u622a\u9762\u6570\u636e\u3002\n - \u786e\u4fdd\u5de5\u4f5c\u76ee\u5f55\u6709\u5199\u5165\u6743\u9650\uff0c\u4ee5\u4fbf OpenMC \u53ef\u4ee5\u751f\u6210\u8f93\u51fa\u6587\u4ef6\u3002\n\n3. **\u6a21\u62df\u53c2\u6570**\uff1a\n - \u68c0\u67e5\u8bbe\u7f6e\u7684\u6a21\u62df\u53c2\u6570\uff0c\u4f8b\u5982\u6279\u6b21\u6570\u3001\u7c92\u5b50\u6570\u7b49\u3002\u786e\u8ba4\u8fd9\u4e9b\u53c2\u6570\u5728\u8d44\u6e90\u5141\u8bb8\u7684\u8303\u56f4\u5185\u3002\u6b64\u5916\uff0c\u5982\u679c\u7c92\u5b50\u6570\u8fc7\u5c11\uff0c\u53ef\u80fd\u5bfc\u81f4\u7edf\u8ba1\u8bef\u5dee\u5927\uff0c\u4ece\u800c\u5f71\u54cd\u622a\u9762\u8ba1\u7b97\u3002\n - \u786e\u4fdd `materials.xml`, `geometry.xml`, \u548c `settings.xml` \u8fd9\u4e9b\u5fc5\u8981\u7684\u6587\u4ef6\u5df2\u6b63\u786e\u751f\u6210\uff0c\u5e76\u5305\u542b\u9002\u5f53\u7684\u6570\u636e\u3002\n\n4. **\u9519\u8bef\u5904\u7406**\uff1a\n - \u4f7f\u7528 `try-except` \u6355\u83b7\u6a21\u62df\u8fd0\u884c\u4e2d\u53d1\u751f\u7684\u9519\u8bef\uff0c\u5e76\u8f93\u51fa\u9519\u8bef\u6d88\u606f\u4ee5\u5e2e\u52a9\u5b9a\u4f4d\u95ee\u9898\u3002\n - \u60a8\u5f53\u524d\u4ee3\u7801\u4e2d\u5df2\u5305\u542b\u9519\u8bef\u5904\u7406\u5757\uff0c\u786e\u4fdd\u4ece\u4e2d\u83b7\u53d6\u9519\u8bef\u65e5\u5fd7\u4ee5\u5206\u6790\u5177\u4f53\u5931\u8d25\u539f\u56e0\u3002\n\n5. **\u9010\u6b65\u8c03\u8bd5**\uff1a\n - \u4f7f\u7528\u65ad\u70b9\u6216\u8005\u589e\u52a0\u8c03\u8bd5\u8f93\u51fa\uff0c\u4ee5\u68c0\u67e5\u5728 `openmc.run()` \u8c03\u7528\u524d\u540e\u7684\u53d8\u91cf\u72b6\u6001\u3002\n - \u9a8c\u8bc1\u6240\u6709\u7684\u6a21\u62df\u6b65\u9aa4\u662f\u5426\u90fd\u5df2\u5b8c\u6210\uff0c\u4f8b\u5982\u6750\u6599\u5c5e\u6027\u662f\u5426\u5df2\u6b63\u786e\u5e94\u7528\u3001\u51e0\u4f55\u662f\u5426\u5b8c\u5168\u5b9a\u4e49\u7b49\u3002\n\n6. **\u53ef\u80fd\u7684\u66ff\u4ee3\u65b9\u6848**\uff1a\n - \u5982\u679c\u83b7\u53d6\u4e0d\u5230\u6709\u6548\u7684 `statepoint` \u6587\u4ef6\uff0c\u53ef\u4ee5\u5728\u7ec8\u7aef\u76f4\u63a5\u8fd0\u884c\u6a21\u62df\u547d\u4ee4\uff0c\u4ee5\u624b\u52a8\u67e5\u770b\u63a7\u5236\u53f0\u8f93\u51fa\u4e2d\u51fa\u73b0\u7684\u5177\u4f53\u9519\u8bef\u3002\n\n\u5982\u679c\u8fd9\u4e9b\u68c0\u67e5\u4e0d\u80fd\u89e3\u51b3\u95ee\u9898\uff0c\u8bf7\u63d0\u4f9b\u66f4\u591a\u65e5\u5fd7\u6216\u9519\u8bef\u4fe1\u606f\uff0c\u4ee5\u4fbf\u66f4\u5177\u4f53\u5730\u5e2e\u52a9\u8bca\u65ad\u95ee\u9898\u3002\u901a\u8fc7\u8fd9\u4e9b\u4fe1\u606f\u60a8\u5c06\u80fd\u591f\u83b7\u5f97\u66f4\u6e05\u6670\u7684\u5173\u4e8e\u6a21\u62df\u4e2d\u65ad\u6216\u5931\u8d25\u7684\u539f\u56e0\u5206\u6790\u3002\nUser: \u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: 'StatePoint' object has no attribute 'k_effective'\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n# \u8bbe\u7f6e\u6838\u6570\u636e\u8def\u5f84\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\n\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 293.6 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 293.6 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n\r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\r\n\r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n\r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n openmc.run()\r\n print(\"\\033[91m\u6a21\u62df\u7ed3\u679c: \")\r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=1)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: `StatePoint` \u5bf9\u8c61\u6ca1\u6709 `k_effective` \u5c5e\u6027\u7684\u9519\u8bef\u901a\u5e38\u4e0e `OpenMC` \u7684\u63a5\u53e3\u53d8\u5316\u6709\u5173\u3002\u5728\u4f60\u7684\u4ee3\u7801\u4e2d\uff0c\u53ef\u80fd\u6b63\u5728\u8bbf\u95ee `StatePoint` \u7684\u5c5e\u6027\u4e0d\u6b63\u786e\u3002\u8bf7\u68c0\u67e5\u4ee5\u4e0b\u4fee\u590d\u5efa\u8bae\uff1a\n\nOpenMC \u7684 `StatePoint` \u5bf9\u8c61\u5177\u6709 `keff` \u5c5e\u6027\u800c\u4e0d\u662f `k_effective`\u3002\u8bf7\u66f4\u65b0\u60a8\u7684\u4ee3\u7801\u4ee5\u6b63\u786e\u8bbf\u95ee\u6b64\u5c5e\u6027\uff1a\n\n```python\nwith openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\n self.k_eff = sp.keff[0] # \u83b7\u53d6 k_eff \u7684\u540d\u4e49\u503c\n```\n\n\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7 `sp.keff` \u83b7\u53d6\u4e00\u4e2a\u6570\u7ec4\uff0c\u8be5\u6570\u7ec4\u5305\u542b `k_eff` \u7684\u540d\u4e49\u503c\u548c\u6807\u51c6\u504f\u5dee\uff0c\u56e0\u6b64\u4f7f\u7528 `[0]` \u6765\u8bbf\u95ee\u540d\u4e49\u503c\u3002\n\n\u786e\u4fdd\u5f15\u7528\u4e86\u6b63\u786e\u7684 `statepoint` \u6587\u4ef6\uff0c\u547d\u540d\u548c\u8def\u5f84\u8981\u4e0e `openmc.run()` \u521b\u5efa\u7684\u6587\u4ef6\u4e00\u81f4\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u6765\u9a8c\u8bc1\uff1a\n\n1. \u6a21\u62df\u7ed3\u675f\u540e\uff0c\u67e5\u770b\u751f\u6210\u4e86\u54ea\u4e9b statepoint \u6587\u4ef6\uff0c\u4ee5\u53ca\u5b83\u4eec\u7684\u547d\u540d\u53ca\u8def\u5f84\u3002\n2. \u786e\u4fdd `openmc.run()` \u6ca1\u6709\u8fd4\u56de\u9519\u8bef\uff0c\u5e76\u6210\u529f\u751f\u6210\u671f\u671b\u7684\u8f93\u51fa\u6587\u4ef6\u3002\n\n\u5982\u679c\u8fd9\u4e9b\u8c03\u6574\u548c\u68c0\u67e5\u4ecd\u7136\u4e0d\u80fd\u89e3\u51b3\u95ee\u9898\uff0c\u8bf7\u53c2\u8003 OpenMC \u6587\u6863\uff0c\u786e\u8ba4\u5f53\u524d\u4f7f\u7528\u7684 OpenMC \u7248\u672c\u5bf9 `StatePoint` \u7684\u89e3\u6790\u662f\u5426\u6709\u5176\u4ed6API\u53d8\u66f4\u3002\u4e5f\u53ef\u4ee5\u4ece\u65e5\u5fd7\u6216\u5176\u4ed6\u8f93\u51fa\u67e5\u627e\u8fdb\u4e00\u6b65\u7684\u9519\u8bef\u63d0\u793a\u4ee5\u8bca\u65ad\u95ee\u9898\u3002\nUser: \u6a21\u62df\u7ed3\u679c: \n\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: can't convert an affine function () to float; use x.nominal_value\nTraceback (most recent call last):\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n# \u8bbe\u7f6e\u6838\u6570\u636e\u8def\u5f84\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\n\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 293.6 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 293.6 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n\r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\r\n\r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n\r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n openmc.run()\r\n print(\"\\033[91m\u6a21\u62df\u7ed3\u679c: \")\r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=1)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u5728\u60a8\u7684 OpenMC \u6a21\u62df\u4e2d\u6536\u5230\u9519\u8bef\u6d88\u606f\uff0c\u8bf4\u660e\u5728\u5c1d\u8bd5\u83b7\u53d6 k_eff \u65f6\u9047\u5230\u4e86 `AffineScalarFunc` \u800c\u4e0d\u662f\u7b80\u5355\u7684\u6d6e\u70b9\u6570\u3002\u5728 OpenMC \u4e2d\uff0c`k_eff` \u662f\u4e00\u4e2a\u5e26\u6709\u4e0d\u786e\u5b9a\u5ea6\u7684\u6807\u91cf\uff0c\u56e0\u6b64\u5b83\u662f\u901a\u8fc7 `uncertainties` \u5305\u4e2d\u7684 `AffineScalarFunc` \u7c7b\u578b\u8868\u793a\u7684\u3002\u8fd9\u610f\u5473\u7740\u5b83\u65e2\u6709\u4e00\u4e2a\u540d\u4e49\u503c\uff08nominal value\uff09\u4e5f\u6709\u4e00\u4e2a\u6807\u51c6\u8bef\u5dee\u3002\n\n\u8981\u89e3\u51b3\u6b64\u95ee\u9898\uff0c\u9700\u8981\u786e\u4fdd\u4ece `AffineScalarFunc` \u83b7\u53d6\u540d\u4e49\u503c\uff08\u5373\u57fa\u672c\u7684\u6d6e\u70b9\u503c\uff09\uff0c\u800c\u4e0d\u662f\u5c1d\u8bd5\u76f4\u63a5\u5c06\u5176\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u4fee\u6539\u4ee3\u7801\uff1a\n\n\u66ff\u6362\u8fd9\u884c\u4ee3\u7801\uff1a\n```python\nself.k_eff = sp.k_effective.nominal_value\n```\n\n\u4e3a\uff1a\n```python\nself.k_eff = sp.keff.nominal_value # \u83b7\u53d6 k_eff \u7684\u540d\u4e49\u503c\n```\n\n\u60a8\u8fd8\u53ef\u4ee5\u6dfb\u52a0\u4e00\u884c\u4ee3\u7801\u6ce8\u91ca\u4ee5\u89e3\u91ca\u4e3a\u4ec0\u4e48\u8981\u8fd9\u6837\u505a\uff1a\n```python\n# \u63d0\u53d6\u540d\u4e49\u503c\u4ee5\u7528\u4e8e\u540e\u7eed\u7684\u6570\u503c\u8ba1\u7b97\uff0c\u5ffd\u7565\u4e0d\u786e\u5b9a\u6027\n```\n\n\u6b64\u5916\uff0c\u8bf7\u786e\u4fdd\u5728\u6a21\u62df\u5f00\u59cb\u524d\u5df2\u6b63\u786e\u6307\u5b9a\u6240\u6709\u8def\u5f84\uff0c\u5e76\u786e\u4fdd `statepoint.100.h5` \u662f OpenMC \u751f\u6210\u7684\u6587\u4ef6\u3002\u8fd0\u884c OpenMC \u540e\uff0c\u786e\u8ba4\u65e0\u5f02\u5e38\u5e76\u8f93\u51fa `statepoint` \u6587\u4ef6\u542b\u6709\u671f\u671b\u7684\u7ed3\u679c\u3002\n\n\u5982\u4ecd\u9047\u5230\u95ee\u9898\uff0c\u786e\u4fdd\u8f93\u51fa\u6587\u4ef6\u786e\u5b9e\u5728\u6307\u5b9a\u8def\u5f84\u4e2d\u751f\u6210\uff0c\u5e76\u4e14\u6ca1\u6709\u88ab\u5f02\u5e38\u4e2d\u6b62\u6216\u5199\u5165\u5931\u8d25\u7684\u60c5\u51b5\u3002\u901a\u8fc7\u68c0\u67e5 OpenMC \u7684\u65e5\u5fd7\u8f93\u51fa\u4ee5\u786e\u8ba4\u6a21\u62df\u8fc7\u7a0b\u7684\u987a\u5229\u8fdb\u884c\u3002\nUser: /mnt/wslg/distro/home/unknowxxs/miniconda3/lib/python3.12/tkinter/__init__.py:861: UserWarning: Glyph 27493 (\\N{CJK UNIFIED IDEOGRAPH-6B65}) missing from font(s) DejaVu Sans.\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n# \u8bbe\u7f6e\u6838\u6570\u636e\u8def\u5f84\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\n\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 293.6 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 293.6 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n\r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\r\n\r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n\r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n openmc.run()\r\n print(\"\\033[91m\u6a21\u62df\u7ed3\u679c: \")\r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=1)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant: \u4ece\u8b66\u544a\u4fe1\u606f\u6765\u770b\uff0c\u60a8\u9047\u5230\u7684\u95ee\u9898\u662f\u7531\u4e8e\u7f3a\u5c11\u67d0\u4e9b\u5b57\u4f53\u6765\u6b63\u786e\u663e\u793a\u4e2d\u6587\u5b57\u7b26\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u7a0b\u5e8f\u8bd5\u56fe\u4f7f\u7528 `DejaVu Sans` \u5b57\u4f53\u6765\u663e\u793a\u5b57\u7b26\uff0c\u4f46\u53d1\u73b0\u5b57\u4f53\u7f3a\u5c11\u7279\u5b9a\u7684\u4e2d\u6587\u5b57\u7b26\uff08\u5728\u60a8\u7684\u60c5\u51b5\u4e0b\u662f**\u6b65**\u8fd9\u4e2a\u5b57\u7b26\uff09\u3002\n\n\u5728\u4f7f\u7528 Matplotlib \u8fdb\u884c\u53ef\u89c6\u5316\u65f6\uff0c\u9ed8\u8ba4\u5b57\u4f53\u53ef\u80fd\u4e0d\u652f\u6301\u6240\u6709\u8bed\u8a00\u7684\u5b57\u7b26\uff0c\u6240\u4ee5\u9700\u8981\u4e3a\u7f3a\u5931\u7684\u5b57\u7b26\u63d0\u4f9b\u5408\u9002\u7684\u5b57\u4f53\u65b9\u6848\u3002\n\n\u4ee5\u4e0b\u662f\u5e38\u89c1\u7684\u89e3\u51b3\u65b9\u6848\uff1a\n\n1. **\u5b89\u88c5\u652f\u6301\u4e2d\u6587\u7684\u5b57\u4f53**\uff1a\n \u60a8\u53ef\u4ee5\u5728\u7cfb\u7edf\u4e2d\u5b89\u88c5\u4e00\u79cd\u652f\u6301\u4e2d\u6587\u7684\u5b57\u4f53\uff0c\u4f8b\u5982 `Noto Sans CJK` \u6216 `SimSun` (\u5b8b\u4f53)\u3002\u5728\u4e00\u4e9bLinux\u53d1\u884c\u7248\u4e0a\uff0c\u53ef\u4ee5\u7528\u5305\u7ba1\u7406\u5668\u5b89\u88c5\u8fd9\u4e9b\u5b57\u4f53\u3002\n\n2. **\u5728Matplotlib\u4e2d\u8bbe\u7f6e\u5b57\u4f53**\uff1a\n \u60a8\u53ef\u4ee5\u5728\u4ee3\u7801\u4e2d\u6307\u5b9a\u5b57\u4f53\uff0c\u4ee5\u786e\u4fddMatplotlib\u4f7f\u7528\u652f\u6301\u4e2d\u6587\u5b57\u7b26\u7684\u5b57\u4f53\uff1a\n\n ```python\n import matplotlib.pyplot as plt\n from matplotlib import font_manager\n\n # \u8bbe\u7f6e\u5b57\u4f53\n font_path = '/path/to/chinese/font.ttf' # \u66ff\u6362\u4e3a\u60a8\u7684\u5b57\u4f53\u6587\u4ef6\u8def\u5f84\uff0c\u5982 SimSun.ttf\n prop = font_manager.FontProperties(fname=font_path)\n plt.rcParams['font.family'] = prop.get_name()\n ```\n\n \u6ce8\uff1a\u5728Windows\u4e0a\uff0c\u60a8\u53ef\u4ee5\u5c06\u8def\u5f84\u6307\u5411 Windows \u81ea\u5e26\u7684\u4e2d\u6587\u5b57\u4f53\u8def\u5f84\uff0c\u5982 `C:/Windows/Fonts/simsun.ttc`\u3002\n\n3. **\u914d\u7f6e matplotlibrc \u6587\u4ef6**\uff1a\n \u901a\u8fc7\u7f16\u8f91 `matplotlibrc` \u6587\u4ef6\uff0c\u60a8\u53ef\u4ee5\u4e3a\u6240\u6709Matplotlib\u7ed8\u56fe\u8bbe\u7f6e\u5168\u5c40\u9ed8\u8ba4\u5b57\u4f53\u3002\u67e5\u627e `~/.matplotlib/matplotlibrc` \u6587\u4ef6\uff0c\u5728\u5176\u4e2d\u627e\u5230 `font.family`\uff0c\u6309\u9700\u8bbe\u7f6e\u4e3a\u652f\u6301\u4e2d\u6587\u7684\u5b57\u4f53\u3002\n\n4. **\u52a8\u6001\u5b57\u4f53\u52a0\u8f7d**\uff1a\n \u5982\u679c\u5728\u67d0\u4e9b\u60c5\u5f62\u4e0b\uff0c\u60a8\u4e0d\u5e0c\u671b\u66f4\u6539\u5168\u5c40\u8bbe\u7f6e\uff0c\u4e5f\u53ef\u4ee5\u5728\u521b\u5efa\u7279\u5b9a\u6587\u672c\u7684\u5730\u65b9\u52a8\u6001\u5e94\u7528\u5b57\u4f53\u3002\u4f8b\u5982\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8bbe\u7f6e\u7279\u5b9a\u6587\u672c\u7684\u5b57\u4f53\uff1a\n\n ```python\n plt.title('\u6807\u9898', fontproperties=prop) # \u8fd9\u91cc prop \u662f\u4f7f\u7528 FontProperties \u521b\u5efa\u7684\n ```\n\n\u6309\u8fd9\u4e9b\u6b65\u9aa4\u8bbe\u7f6e\u597d\u540e\uff0c\u76f8\u5e94\u7684\u5b57\u7b26\u5e94\u8be5\u53ef\u4ee5\u6b63\u5e38\u663e\u793a\u800c\u4e0d\u518d\u51fa\u73b0\u7f3a\u5931\u8b66\u544a\u3002\u5982\u6709\u8fdb\u4e00\u6b65\u95ee\u9898\uff0c\u8bf7\u786e\u8ba4\u5b57\u4f53\u8def\u5f84\u548c\u5b57\u4f53\u5e93\u5df2\u6b63\u786e\u52a0\u8f7d\uff0c\u5e76\u4e14\u5b57\u4f53\u786e\u5b9e\u5305\u542b\u60a8\u6240\u9700\u7684\u5b57\u7b26\u96c6\u3002\nUser: font_path = '/home/unknowxxs/ttf/DejaVuSans.ttf' # \u66ff\u6362\u4e3a\u60a8\u7684\u5b57\u4f53\u6587\u4ef6\u8def\u5f84\uff0c\u5982 SimSun.ttf\nprop = font_manager.FontProperties(fname=font_path)\nplt.rcParams['font.family'] = prop.get_name()\n```python\nimport os\r\nimport gymnasium as gym\r\nimport numpy as np\r\nfrom stable_baselines3 import PPO\r\nfrom stable_baselines3.common.env_util import make_vec_env\r\nimport openmc\r\nimport matplotlib.pyplot as plt\r\n\r\n# \u8bbe\u7f6e\u6838\u6570\u636e\u8def\u5f84\r\nopenmc.config['cross_sections'] = '/home/unknowxxs/python/data/cross_sections.xml'\r\n\r\nclass NuclearReactorEnv(gym.Env):\r\n def __init__(self):\r\n super().__init__()\r\n self.action_space = gym.spaces.Discrete(11) # \u63a7\u5236\u68d2\u4f4d\u7f6e\uff0c0-10\r\n \r\n # \u89c2\u5bdf\u7a7a\u95f4\uff1a\u6e29\u5ea6\u3001\u529f\u7387\u3001k_eff\u3001\u63a7\u5236\u68d2\u4f4d\u7f6e\u3001\u71c3\u6599\u71c3\u8017\r\n self.observation_space = gym.spaces.Box(\r\n low=np.array([500, 0, 0.5, 0, 0]),\r\n high=np.array([1500, 3000, 1.5, 1, 100]),\r\n dtype=np.float32\r\n )\r\n \r\n self.max_steps = 1000\r\n self.current_step = 0\r\n self._setup_model()\r\n \r\n def _setup_model(self):\r\n # \u8bbe\u7f6e\u6750\u6599\r\n # UO2\u71c3\u6599\r\n self.fuel = openmc.Material(name='fuel')\r\n self.fuel.add_nuclide('U235', 0.05)\r\n self.fuel.add_nuclide('U238', 0.95)\r\n self.fuel.add_nuclide('O16', 2.0)\r\n self.fuel.set_density('g/cm3', 10.0)\r\n self.fuel.temperature = 293.6 # K\r\n\r\n # \u6c34\u51b7\u5374\u5242\r\n self.water = openmc.Material(name='water')\r\n self.water.add_nuclide('H1', 2)\r\n self.water.add_nuclide('O16', 1)\r\n self.water.set_density('g/cm3', 1.0)\r\n self.water.temperature = 293.6 # K\r\n\r\n # \u63a7\u5236\u68d2 (B4C)\r\n self.control_rod = openmc.Material(name='control_rod')\r\n self.control_rod.add_nuclide('B10', 0.8)\r\n self.control_rod.add_nuclide('B11', 0.2)\r\n self.control_rod.add_nuclide('C12', 1.0)\r\n self.control_rod.set_density('g/cm3', 2.52)\r\n\r\n # \u521b\u5efa\u6750\u6599\u96c6\u5408\r\n self.materials = openmc.Materials([self.fuel, self.water, self.control_rod])\r\n self.materials.export_to_xml() # \u751f\u6210 materials.xml\r\n\r\n # \u521b\u5efa\u51e0\u4f55\u4f53\r\n # \u7b80\u5316\u7684\u5706\u67f1\u5f62\u53cd\u5e94\u5806\u5806\u82af\r\n cylinder = openmc.ZCylinder(r=100, boundary_type='reflective') # \u589e\u52a0\u4e86\u8fb9\u754c\u6761\u4ef6\r\n fuel_region = -cylinder\r\n \r\n fuel_cell = openmc.Cell(fill=self.fuel, region=fuel_region)\r\n outside_cell = openmc.Cell(region=+cylinder, fill=self.water) # \u521b\u5efa\u5916\u90e8\u5305\u56f4\u7269\r\n\r\n # \u521b\u5efauniverse\u548cgeometry\r\n root_universe = openmc.Universe(cells=[fuel_cell, outside_cell])\r\n self.geometry = openmc.Geometry(root_universe)\r\n self.geometry.export_to_xml() # \u751f\u6210 geometry.xml\r\n\r\n # \u8bbe\u7f6e\u6a21\u62df\u53c2\u6570\u5e76\u751f\u6210 XML\r\n self.settings = openmc.Settings()\r\n self.settings.batches = 100\r\n self.settings.inactive = 10\r\n self.settings.particles = 1000\r\n self.settings.temperature = {'method': 'interpolation'}\r\n self.settings.export_to_xml() # \u751f\u6210 settings.xml\r\n \r\n def init_visualization(self):\r\n \"\"\"\u521d\u59cb\u5316\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n plt.ion() # \u5f00\u542f\u4ea4\u4e92\u6a21\u5f0f\r\n self.fig, self.axs = plt.subplots(2, 2, figsize=(15, 10))\r\n self.fig.suptitle('\u6838\u53cd\u5e94\u5806\u8fd0\u884c\u72b6\u6001\u76d1\u6d4b', fontsize=16)\r\n \r\n # \u521d\u59cb\u5316\u6570\u636e\u5b58\u50a8\r\n self.history = {\r\n 'temperature': [],\r\n 'power': [],\r\n 'k_eff': [],\r\n 'burnup': [],\r\n 'control_rod': [],\r\n 'steps': []\r\n }\r\n \r\n # \u914d\u7f6e\u5b50\u56fe\r\n self.lines = {}\r\n \r\n # \u6e29\u5ea6\u548c\u529f\u7387\u56fe\r\n self.axs[0, 0].set_title('\u6e29\u5ea6\u548c\u529f\u7387\u53d8\u5316')\r\n self.axs[0, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['temp'], = self.axs[0, 0].plot([], [], 'r-', label='\u6e29\u5ea6 (K)')\r\n self.lines['power'], = self.axs[0, 0].plot([], [], 'b-', label='\u529f\u7387 (MW)')\r\n self.axs[0, 0].legend()\r\n \r\n # k_eff\u56fe\r\n self.axs[0, 1].set_title('k_eff\u53d8\u5316')\r\n self.axs[0, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['k_eff'], = self.axs[0, 1].plot([], [], 'g-', label='k_eff')\r\n self.axs[0, 1].axhline(y=1.0, color='r', linestyle='--', label='\u4e34\u754c\u503c')\r\n self.axs[0, 1].legend()\r\n \r\n # \u63a7\u5236\u68d2\u4f4d\u7f6e\u56fe\r\n self.axs[1, 0].set_title('\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].set_xlabel('\u6b65\u6570')\r\n self.lines['control'], = self.axs[1, 0].plot([], [], 'k-', label='\u63a7\u5236\u68d2\u4f4d\u7f6e')\r\n self.axs[1, 0].legend()\r\n \r\n # \u71c3\u8017\u56fe\r\n self.axs[1, 1].set_title('\u71c3\u6599\u71c3\u8017')\r\n self.axs[1, 1].set_xlabel('\u6b65\u6570')\r\n self.lines['burnup'], = self.axs[1, 1].plot([], [], 'm-', label='\u71c3\u8017 (MWd/kgU)')\r\n self.axs[1, 1].legend()\r\n \r\n plt.tight_layout()\r\n\r\n def update_visualization(self):\r\n \"\"\"\u66f4\u65b0\u53ef\u89c6\u5316\u56fe\u8868\"\"\"\r\n # \u66f4\u65b0\u5386\u53f2\u6570\u636e\r\n self.history['temperature'].append(self.temperature)\r\n self.history['power'].append(self.power)\r\n self.history['k_eff'].append(self.k_eff)\r\n self.history['burnup'].append(self.burnup)\r\n self.history['control_rod'].append(self.control_rod_pos)\r\n self.history['steps'].append(self.current_step)\r\n \r\n # \u66f4\u65b0\u56fe\u8868\r\n steps = self.history['steps']\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\u548c\u529f\u7387\r\n self.lines['temp'].set_data(steps, self.history['temperature'])\r\n self.lines['power'].set_data(steps, self.history['power'])\r\n self.axs[0, 0].relim()\r\n self.axs[0, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0k_eff\r\n self.lines['k_eff'].set_data(steps, self.history['k_eff'])\r\n self.axs[0, 1].relim()\r\n self.axs[0, 1].autoscale_view()\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.lines['control'].set_data(steps, self.history['control_rod'])\r\n self.axs[1, 0].relim()\r\n self.axs[1, 0].autoscale_view()\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.lines['burnup'].set_data(steps, self.history['burnup'])\r\n self.axs[1, 1].relim()\r\n self.axs[1, 1].autoscale_view()\r\n \r\n plt.draw()\r\n plt.pause(0.01)\r\n\r\n def reset(self, seed=None):\r\n super().reset(seed=seed)\r\n self.current_step = 0\r\n self.temperature = 600 # K\r\n self.power = 1000 # MW\r\n self.k_eff = 1.0\r\n self.burnup = 0 # MWd/kgU\r\n self.control_rod_pos = 0.5\r\n \r\n # \u521d\u59cb\u5316\u53ef\u89c6\u5316\r\n self.init_visualization()\r\n \r\n return self._get_observation(), {}\r\n \r\n def _get_observation(self):\r\n return np.array([\r\n self.temperature,\r\n self.power,\r\n self.k_eff,\r\n self.control_rod_pos,\r\n self.burnup\r\n ], dtype=np.float32)\r\n \r\n def step(self, action):\r\n self.current_step += 1\r\n \r\n # \u66f4\u65b0\u63a7\u5236\u68d2\u4f4d\u7f6e\r\n self.control_rod_pos = action / 10.0\r\n \r\n # \u8fd0\u884cOpenMC\u6a21\u62df\r\n try:\r\n # \u66f4\u65b0\u6750\u6599\u6e29\u5ea6\r\n self.fuel.temperature = self.temperature\r\n \r\n # \u8fd0\u884c\u4e34\u754c\u8ba1\u7b97\r\n openmc.run()\r\n print(\"\\033[91m\u6a21\u62df\u7ed3\u679c: \")\r\n # \u83b7\u53d6k_eff\r\n with openmc.StatePoint(\"/home/unknowxxs/python/statepoint.100.h5\") as sp:\r\n self.k_eff = sp.k_effective.nominal_value\r\n \r\n # \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001\r\n self._update_state()\r\n \r\n # \u66f4\u65b0\u53ef\u89c6\u5316\r\n self.update_visualization()\r\n \r\n # \u8ba1\u7b97\u5956\u52b1\r\n reward = self._calculate_reward()\r\n \r\n # \u68c0\u67e5\u662f\u5426\u7ed3\u675f\r\n done = self.current_step >= self.max_steps or self._is_unsafe()\r\n \r\n except Exception as e:\r\n print(f\"\u6a21\u62df\u8fc7\u7a0b\u51fa\u9519: {e}\")\r\n reward = -1000\r\n done = True\r\n \r\n return self._get_observation(), reward, done, False, {}\r\n \r\n def _update_state(self):\r\n # \u57fa\u4e8ek_eff\u66f4\u65b0\u529f\u7387\r\n power_factor = (self.k_eff - 1.0) * 5000\r\n self.power = np.clip(self.power + power_factor, 0, 3000)\r\n \r\n # \u66f4\u65b0\u6e29\u5ea6\r\n delta_temp = (self.power - 1000) * 0.1\r\n self.temperature = np.clip(self.temperature + delta_temp, 500, 1500)\r\n \r\n # \u66f4\u65b0\u71c3\u8017\r\n self.burnup += self.power * 0.001\r\n \r\n def _calculate_reward(self):\r\n reward = 0\r\n \r\n # \u5956\u52b1\u7a33\u5b9a\u7684\u8fd0\u884c\u72b6\u6001\r\n reward -= abs(self.power - 1000) * 0.01 # \u76ee\u6807\u529f\u73871000MW\r\n reward -= abs(self.k_eff - 1.0) * 100 # \u76ee\u6807k_eff=1\r\n reward -= abs(self.temperature - 900) * 0.1 # \u76ee\u6807\u6e29\u5ea6900K\r\n \r\n # \u60e9\u7f5a\u4e0d\u5b89\u5168\u72b6\u6001\r\n if self._is_unsafe():\r\n reward -= 1000\r\n \r\n return reward\r\n \r\n def _is_unsafe(self):\r\n return (self.temperature > 1400 or \r\n self.power > 2500 or \r\n self.k_eff > 1.2)\r\n\r\ndef train_agent():\r\n env = make_vec_env(NuclearReactorEnv, n_envs=4)\r\n \r\n model = PPO(\r\n \"MlpPolicy\",\r\n env,\r\n verbose=1,\r\n learning_rate=0.0003,\r\n n_steps=2048,\r\n batch_size=64,\r\n n_epochs=10\r\n )\r\n \r\n model.learn(total_timesteps=1)\r\n model.save(\"ppo_nuclear_reactor\")\r\n \r\n return model\r\n\r\ndef evaluate_agent(model, num_episodes=5):\r\n env = NuclearReactorEnv()\r\n \r\n for episode in range(num_episodes):\r\n obs, _ = env.reset()\r\n episode_reward = 0\r\n done = False\r\n \r\n while not done:\r\n action, _ = model.predict(obs)\r\n obs, reward, done, _, _ = env.step(action)\r\n episode_reward += reward\r\n \r\n # \u6253\u5370\u5f53\u524d\u72b6\u6001\r\n print(f\"\u6b65\u9aa4 {env.current_step}:\")\r\n print(f\"\u6e29\u5ea6: {env.temperature:.1f}K\")\r\n print(f\"\u529f\u7387: {env.power:.1f}MW\")\r\n print(f\"k_eff: {env.k_eff:.3f}\")\r\n print(f\"\u63a7\u5236\u68d2\u4f4d\u7f6e: {env.control_rod_pos:.2f}\")\r\n print(f\"\u71c3\u8017: {env.burnup:.2f}MWd/kgU\")\r\n print(\"------------------------\")\r\n \r\n plt.savefig(f'episode_{episode+1}_results.png')\r\n print(f\"Episode {episode + 1}: \u603b\u5956\u52b1 = {episode_reward:.2f}\\n\")\r\n \r\n plt.close('all')\r\n\r\nmodel = train_agent()\r\nprint(\"\\n\u8bc4\u4f30\u6838\u53cd\u5e94\u5806\u63a7\u5236\u6027\u80fd\uff1a\")\r\nevaluate_agent(model)\r\n\n```\nAssistant:"},{"assistant_initiates":false,"asst_text":"This quote emphasizes the distinction between \"past\" and \"history.\" \n\n1. **The Past**: The past refers to all events that have ever occurred, regardless of their documentation or perception. It is an objective collection of occurrences, experiences, and facts that are part of human experience and existence. The past includes everything that has happened, whether it was witnessed, recorded, or even acknowledged.\n\n2. **History**: In contrast, history is the narrative or interpretation of those past events as recorded or reported by individuals, historians, or cultures. History is constructed through various lenses and is subject to bias, perspective, and interpretation. What we know about history is shaped by the sources available, the perspectives of those who recorded it, and the societal and cultural contexts in which it is interpreted.\n\nIn essence, the quote suggests that while the past is an unchangeable reality, history is subjective and can vary significantly depending on who tells the story. Different interpretations can emerge from the same events based on the values, beliefs, and intentions of the historians or recorders. \n\nThis distinction underscores the importance of critical thinking when studying history, as it necessitates examining sources, understanding context, and recognizing the potential for bias in historical narratives.","behavior":"degenerate_repetition","conversation_hash":"f16a73cf666df0679b8afa6cf7349f09","conversation_redacted":false,"conversation_toxic":false,"country":"Bahrain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":1450,"n_turns_in_conversation":4,"n_words":213,"source_row_offset":52296,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:15 GMT","turn_id":"4e530931a5d9e37f46cd63a3","turn_index":0,"user_text":"\u201cThe past actually happened but history is only what someone wrote down\u201d \nexplanation:"},{"assistant_initiates":false,"asst_text":"The quote highlights the difference between \"past\" and \"history.\" \n\n- **The Past**: Refers to all events that have occurred, independent of documentation or perception. It's an objective reality of what happened.\n\n- **History**: Is the narrative or interpretation of those events as recorded by individuals or societies. It is subjective and influenced by context, bias, and perspective.\n\nIn essence, the past is an unchangeable reality, while history is a constructed narrative that can vary based on who recounts it. This underscores the need for critical thinking when studying history.","behavior":"degenerate_repetition","conversation_hash":"f16a73cf666df0679b8afa6cf7349f09","conversation_redacted":false,"conversation_toxic":false,"country":"Bahrain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":605,"n_turns_in_conversation":4,"n_words":92,"source_row_offset":52297,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:15 GMT","turn_id":"baeffb80da55526e2d7f4b74","turn_index":1,"user_text":"make it shorter"},{"assistant_initiates":false,"asst_text":"The quote highlights the difference between the \"past\" and \"history.\" The past encompasses all events that have occurred, independent of any documentation or perception, and represents an objective reality. In contrast, history is the narrative or interpretation of those events as recorded by individuals or societies. It is subjective and influenced by context, bias, and perspective. Essentially, the past is an unchangeable reality, while history is a constructed narrative that can vary depending on who recounts it. This distinction emphasizes the importance of critical thinking when studying history.","behavior":"degenerate_repetition","conversation_hash":"f16a73cf666df0679b8afa6cf7349f09","conversation_redacted":false,"conversation_toxic":false,"country":"Bahrain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":623,"n_turns_in_conversation":4,"n_words":90,"source_row_offset":52298,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:15 GMT","turn_id":"b83a971dd1879d3206326563","turn_index":2,"user_text":"without points"},{"assistant_initiates":false,"asst_text":"The quote highlights the difference between \"past\" and \"history.\" The past includes all the events that have happened, no matter if anyone wrote them down or not, making it an objective reality. History, on the other hand, is the story or interpretation of those events as told by people. It is subjective and can be influenced by personal views and the context in which it is told. Essentially, the past is a fixed truth, while history is a version of the past that can change depending on who is telling it. This difference shows why it's important to think critically when studying history.","behavior":"degenerate_repetition","conversation_hash":"f16a73cf666df0679b8afa6cf7349f09","conversation_redacted":false,"conversation_toxic":false,"country":"Bahrain","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":610,"n_turns_in_conversation":4,"n_words":106,"source_row_offset":52299,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:15 GMT","turn_id":"ca7c4ea06f7e592a402f803d","turn_index":3,"user_text":"use easier words"},{"assistant_initiates":false,"asst_text":"\u062f\u0631 \u0635\u0648\u0631\u062a\u06cc \u06a9\u0647 \u062f\u0631 \u06cc\u06a9 \u0639\u0642\u062f \u0645\u0648\u0642\u062a (\u0635\u06cc\u063a\u0647) \u0632\u0648\u062c \u0628\u0647\u200c\u0637\u0648\u0631 \u0635\u0631\u06cc\u062d \u0645\u062f\u062a \u0628\u0627\u0642\u06cc\u200c\u0645\u0627\u0646\u062f\u0647 \u0627\u0632 \u0639\u0642\u062f \u0631\u0627 \u0628\u0630\u0644 \u0646\u06a9\u0646\u062f \u0648 \u06cc\u06a9 \u0642\u0637\u0639 \u0631\u0627\u0628\u0637\u0647 \u0648 \u062c\u062f\u0627\u06cc\u06cc \u0686\u0646\u062f \u0633\u0627\u0644\u0647 \u0645\u06cc\u0627\u0646 \u0637\u0631\u0641\u06cc\u0646 \u0627\u062a\u0641\u0627\u0642 \u0628\u06cc\u0641\u062a\u062f\u060c \u0627\u062b\u0628\u0627\u062a \u0628\u0630\u0644 \u0645\u062f\u062a \u06cc\u0627 \u0627\u0646\u0642\u0636\u0627\u06cc \u0639\u0642\u062f \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u0647 \u0686\u0627\u0644\u0634 \u0628\u06a9\u0634\u062f. \u0627\u0645\u0627 \u062f\u0631 \u0627\u06cc\u0646 \u0634\u0631\u0627\u06cc\u0637 \u0686\u0646\u062f \u0646\u06a9\u062a\u0647 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:\n\n1. **\u0627\u0633\u0646\u0627\u062f \u0648 \u0634\u0648\u0627\u0647\u062f**: \u0627\u06af\u0631 \u0637\u0631\u0641\u06cc\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a \u06a9\u062a\u0628\u06cc \u062a\u0648\u0627\u0641\u0642 \u06a9\u0631\u062f\u0647 \u0628\u0627\u0634\u0646\u062f \u06a9\u0647 \u0645\u062f\u062a \u0639\u0642\u062f \u0628\u0630\u0644 \u0634\u062f\u0647 \u06cc\u0627 \u0628\u0647 \u067e\u0627\u06cc\u0627\u0646 \u0631\u0633\u06cc\u062f\u0647 \u0627\u0633\u062a\u060c \u0627\u06cc\u0646 \u0627\u0633\u0646\u0627\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062f\u0631\u06a9 \u0645\u0639\u062a\u0628\u0631 \u062f\u0631 \u0645\u062d\u0627\u06a9\u0645 \u0642\u0627\u0646\u0648\u0646\u06cc \u0627\u0631\u0627\u0626\u0647 \u0634\u0648\u0646\u062f.\n\n2. **\u0634\u0647\u0627\u062f\u062a \u0634\u0647\u0648\u062f**: \u062d\u0636\u0648\u0631 \u0634\u0627\u0647\u062f\u0627\u0646 \u0645\u0639\u062a\u0628\u0631 \u06a9\u0647 \u0642\u0637\u0639 \u0631\u0627\u0628\u0637\u0647 \u06cc\u0627 \u0635\u062d\u0628\u062a\u200c\u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0628\u0630\u0644 \u0645\u062f\u062a \u0631\u0627 \u0634\u0646\u06cc\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0627\u062b\u0628\u0627\u062a \u0627\u062a\u0645\u0627\u0645 \u0639\u0642\u062f \u06a9\u0645\u06a9 \u06a9\u0646\u062f.\n\n3. **\u0631\u062c\u0648\u0639 \u0628\u0647 \u0645\u062d\u0627\u06a9\u0645 \u0642\u0636\u0627\u06cc\u06cc**: \u0627\u06af\u0631 \u0647\u06cc\u0686 \u06cc\u06a9 \u0627\u0632 \u0634\u0648\u0627\u0647\u062f \u06cc\u0627 \u0627\u0633\u0646\u0627\u062f \u06a9\u0627\u0641\u06cc \u0646\u0628\u0627\u0634\u062f\u060c \u0645\u0631\u0627\u062c\u0639\u0647 \u0628\u0647 \u062f\u0627\u062f\u06af\u0627\u0647 \u0648 \u062f\u0631\u062e\u0648\u0627\u0633\u062a \u0628\u0631\u0631\u0633\u06cc \u0645\u0648\u0636\u0648\u0639 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0631\u0627\u0647\u06cc \u0628\u0631\u0627\u06cc \u062d\u0644 \u0627\u062e\u062a\u0644\u0627\u0641 \u0628\u0627\u0634\u062f. \u062f\u0631 \u0627\u06cc\u0646 \u062d\u0627\u0644\u062a \u0642\u0627\u0636\u06cc \u0628\u0631 \u0627\u0633\u0627\u0633 \u0634\u0648\u0627\u0647\u062f \u0648 \u0645\u062f\u0627\u0631\u06a9\u06cc \u06a9\u0647 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u062a\u0635\u0645\u06cc\u0645\u200c\u06af\u06cc\u0631\u06cc \u062e\u0648\u0627\u0647\u062f \u06a9\u0631\u062f.\n\n4. **\u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u06a9\u06cc\u0644**: \u0645\u0634\u0627\u0648\u0631\u0647 \u0628\u0627 \u0648\u06a9\u06cc\u0644 \u0645\u062a\u062e\u0635\u0635 \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u062d\u0642\u0648\u0642 \u062e\u0627\u0646\u0648\u0627\u062f\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u06a9\u0645\u06a9 \u0645\u0648\u062b\u0631\u06cc \u062f\u0631 \u062c\u0647\u062a \u0627\u0631\u0627\u0626\u0647 \u0645\u0633\u062a\u0646\u062f\u0627\u062a \u0642\u0627\u0646\u0648\u0646\u06cc \u0648 \u067e\u06cc\u06af\u06cc\u0631\u06cc \u067e\u0631\u0648\u0646\u062f\u0647 \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u062f.\n\n\u062f\u0631 \u06a9\u0644\u060c \u0628\u0631\u0627\u06cc \u062d\u0636\u0648\u0631 \u062d\u0642\u0648\u0642\u06cc \u0645\u0624\u062b\u0631 \u062f\u0631 \u0686\u0646\u06cc\u0646 \u0645\u0648\u0627\u0631\u062f\u06cc\u060c \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f \u06a9\u0647 \u0627\u0641\u0631\u0627\u062f \u0627\u0632 \u0645\u0634\u0627\u0648\u0631\u0647 \u0648\u06a9\u0644\u0627 \u0628\u0647\u0631\u0647\u200c\u0645\u0646\u062f \u0634\u0648\u0646\u062f \u062a\u0627 \u062d\u0642\u0648\u0642\u0634\u0627\u0646 \u0628\u0647 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0634\u06a9\u0644 \u0645\u0645\u06a9\u0646 \u062d\u0641\u0638 \u0634\u0648\u062f.","behavior":"degenerate_repetition","conversation_hash":"d1f7ba959ec7e9bb3dd6ea866b076c91","conversation_redacted":false,"conversation_toxic":false,"country":"Iran","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Persian","model":"gpt-4o-2024-08-06","n_chars":1217,"n_turns_in_conversation":2,"n_words":240,"source_row_offset":52300,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:25 GMT","turn_id":"a50948c214c3bfa258a3ec53","turn_index":0,"user_text":"\u0627\u06af\u0631 \u0632\u0648\u062c \u0635\u0631\u06cc\u062d\u0627 \u0645\u062f\u062a \u0628\u0627\u0642\u06cc\u0645\u0627\u0646\u062f\u0647 \u0631\u0648 \u0628\u0630\u0644 \u0646\u06a9\u0646\u0647 \u0648 \u0642\u0637\u0639 \u0631\u0627\u0628\u0637\u0647 \u06cc \u0646\u0627\u0645\u0632\u062f\u06cc \u0627\u062a\u0641\u0627\u0642 \u0628\u06cc\u0641\u062a\u0647\u0627\u0641\u062a\u0631\u0627\u0642 \u0648 \u062c\u062f\u0627\u06cc\u06cc \u0686\u0646\u062f \u0633\u0627\u0644 \u0628\u0648\u062c\u0648\u062f \u0627\u0648\u0645\u062f\u0647 \u0628\u0627\u0634\u0647 \u0627\u06cc\u0646 \u0628\u0630\u0644 \u0631\u0648 \u0686\u0647 \u06a9\u0633\u06cc \u0648 \u0686\u062c\u0648\u0631\u06cc \u0628\u0627\u06cc\u062f \u0628\u0627 \u0642\u0637\u0639 \u0627\u0631\u062a\u0628\u0627\u0637 \u0686\u0646\u062f \u0633\u0627\u0644\u0647 \u062f\u0631 \u0639\u0642\u062f \u0645\u0648\u0642\u062a \u062b\u0627\u0628\u062a \u06a9\u0631\u062f"},{"assistant_initiates":false,"asst_text":"\u062f\u0631 \u0645\u0633\u0627\u0626\u0644 \u062d\u0642\u0648\u0642\u06cc\u060c \u0627\u0635\u0644 \u0628\u0631 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0647\u0631 \u06a9\u0633\u06cc \u06a9\u0647 \u0627\u062f\u0639\u0627\u06cc\u06cc \u062f\u0627\u0631\u062f\u060c \u0628\u0627\u06cc\u062f \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0627\u062b\u0628\u0627\u062a \u0622\u0646 \u0631\u0627 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f. \u062f\u0631 \u0645\u0648\u0631\u062f \u0628\u0630\u0644 \u0645\u062f\u062a \u062f\u0631 \u0639\u0642\u062f \u0645\u0648\u0642\u062a (\u0635\u06cc\u063a\u0647)\u060c \u0627\u06af\u0631 \u06cc\u06a9\u06cc \u0627\u0632 \u0637\u0631\u0641\u06cc\u0646 \u0627\u062f\u0639\u0627 \u06a9\u0646\u062f \u06a9\u0647 \u0639\u0642\u062f \u0628\u0627 \u0628\u0630\u0644 \u0645\u062f\u062a \u062e\u0627\u062a\u0645\u0647 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a\u060c \u0627\u06cc\u0646 \u0637\u0631\u0641 \u0628\u0627\u06cc\u062f \u0634\u0648\u0627\u0647\u062f \u0648 \u0627\u062f\u0644\u0647\u200c\u0627\u06cc \u0627\u0631\u0627\u0626\u0647 \u06a9\u0646\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u0627\u062f\u0639\u0627 \u0631\u0627 \u062b\u0627\u0628\u062a \u06a9\u0646\u062f. \u0628\u0647\u200c\u0639\u0628\u0627\u0631\u062a \u062f\u06cc\u06af\u0631:\n\n- **\u0627\u06af\u0631 \u0632\u0648\u062c (\u0645\u0631\u062f) \u0627\u062f\u0639\u0627 \u06a9\u0646\u062f \u06a9\u0647 \u0645\u062f\u062a \u0628\u0627\u0642\u06cc\u0645\u0627\u0646\u062f\u0647 \u0631\u0627 \u0628\u0630\u0644 \u06a9\u0631\u062f\u0647 \u0627\u0633\u062a**: \u0627\u0648 \u0628\u0627\u06cc\u062f \u0634\u0648\u0627\u0647\u062f \u0648 \u0645\u062f\u0627\u0631\u06a9\u06cc \u0627\u0631\u0627\u0626\u0647 \u062f\u0647\u062f \u06a9\u0647 \u0646\u0634\u0627\u0646\u200c\u062f\u0647\u0646\u062f\u0647 \u0627\u06cc\u0646 \u0628\u0630\u0644 \u0628\u0627\u0634\u062f\u060c \u0645\u0627\u0646\u0646\u062f \u0634\u0648\u0627\u0647\u062f \u06a9\u062a\u0628\u06cc\u060c \u067e\u06cc\u0627\u0645\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc\u060c \u06cc\u0627 \u0634\u0647\u0627\u062f\u062a \u0634\u0647\u0648\u062f.\n\n- **\u0627\u06af\u0631 \u0632\u0648\u062c\u0647 (\u0632\u0646) \u0627\u062f\u0639\u0627 \u06a9\u0646\u062f \u06a9\u0647 \u0632\u0648\u062c \u0645\u062f\u062a \u0631\u0627 \u0628\u0630\u0644 \u06a9\u0631\u062f\u0647 \u0627\u0633\u062a**: \u062f\u0631 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a\u060c \u0632\u0646 \u0628\u0627\u06cc\u062f \u0634\u0648\u0627\u0647\u062f \u0648 \u0645\u062f\u0627\u0631\u06a9\u06cc \u0645\u0628\u0646\u06cc \u0628\u0631 \u0648\u0642\u0648\u0639 \u0628\u0630\u0644 \u0627\u0631\u0627\u0626\u0647 \u06a9\u0646\u062f.\n\n\u062f\u0631 \u063a\u06cc\u0627\u0628 \u0645\u062f\u0627\u0631\u06a9 \u0645\u0639\u06cc\u0646\u060c \u0634\u0647\u0627\u062f\u062a \u0634\u0647\u0648\u062f \u06cc\u0627 \u0647\u0631 \u06af\u0648\u0646\u0647 \u0645\u062f\u0631\u06a9 \u062f\u06cc\u06af\u0631\u06cc \u06a9\u0647 \u0646\u0634\u0627\u0646\u200c\u062f\u0647\u0646\u062f\u0647 \u0642\u0637\u0639 \u0631\u0627\u0628\u0637\u0647 \u0648 \u062a\u0648\u0627\u0641\u0642 \u0628\u0631 \u0628\u0630\u0644 \u0628\u0627\u0634\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u062f\u0631 \u0645\u062d\u0627\u06a9\u0645 \u0642\u0636\u0627\u06cc\u06cc \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u06af\u06cc\u0631\u062f. \u062f\u0631 \u0647\u0631 \u0635\u0648\u0631\u062a\u060c \u0645\u0634\u0648\u0631\u062a \u0628\u0627 \u06cc\u06a9 \u0648\u06a9\u06cc\u0644 \u062d\u0642\u0648\u0642\u06cc \u0645\u062a\u062e\u0635\u0635 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0631\u0648\u0634\u0646\u200c\u062a\u0631 \u0634\u062f\u0646 \u0648\u0638\u0627\u06cc\u0641 \u0648 \u0645\u0633\u0626\u0648\u0644\u06cc\u062a\u200c\u0647\u0627\u06cc \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u0637\u0631\u0641\u06cc\u0646 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0648\u0627\u0631\u062f \u06a9\u0645\u06a9 \u06a9\u0646\u062f.","behavior":"degenerate_repetition","conversation_hash":"d1f7ba959ec7e9bb3dd6ea866b076c91","conversation_redacted":false,"conversation_toxic":false,"country":"Iran","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Persian","model":"gpt-4o-2024-08-06","n_chars":863,"n_turns_in_conversation":2,"n_words":179,"source_row_offset":52301,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:25 GMT","turn_id":"e019f32b6c97321ab41182e6","turn_index":1,"user_text":"\u0627\u06cc\u0646 \u0628\u0630\u0644 \u0631\u0627 \u0686\u0647 \u06a9\u0633\u06cc \u0628\u0627\u06cc\u062f \u062b\u0627\u0628\u062a \u06a9\u0646\u062f"},{"assistant_initiates":false,"asst_text":"Timoth\u00e9e Chalamet's portrayal of Paul Atreides in \"Dune\" emerged as a defining moment in his acting career. Set in a vast desert landscape filled with political intrigue and power struggles, \"Dune\" presented Timoth\u00e9e with the complex task of bringing Paul, a young nobleman, to life amidst an ensemble of seasoned actors. Under the meticulous direction of Denis Villeneuve, Timoth\u00e9e's performance showcased his ability to capture the emotional depth and evolving strength of his character. Representing someone who not only navigates familial responsibilities but also the weight of destiny allowed Timoth\u00e9e to explore a range of emotions, from vulnerability to determination. Working alongside notable actors such as Rebecca Ferguson, Oscar Isaac, and Zendaya, he demonstrated his capacity to hold his own while sharing the screen with A-list talent. The production of \"Dune\" was vast, employing stunning visuals and intricate world-building, all of which required Timoth\u00e9e to adapt to the demands of a major epic science fiction narrative. His role as Paul Atreides required intensive preparation, both physically and mentally, embracing the film\u2019s themes of leadership, survival, and inner transformation. Timoth\u00e9e\u2019s dedication to portraying the character's journey involved understanding the nuances of the source material and collaborating with the creative vision that Villeneuve brought to the adaptation. His performance in \"Dune\" solidified his place as a leading young actor in Hollywood, further expanding his repertoire and establishing him as a talent capable of commanding significant roles in high-stakes projects. Within this role, he combined youthful charisma with mature intensity, marking a significant milestone and opening new doors for challenging future endeavors. Reply finished","behavior":"degenerate_repetition","conversation_hash":"85f5c8eeff08a4aaf99df3ea28869644","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":4546,"n_turns_in_conversation":1,"n_words":668,"source_row_offset":52302,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:25 GMT","turn_id":"328037af7c38378228719b1e","turn_index":0,"user_text":"Create a long detailed text about actor Timoth\u00e9e Chalamet with the following summary. Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Dune] Summary: [Timoth\u00e9e's most significant role to date came in 'Dune,' where he played Paul Atreides, working with an ensemble cast and under the direction of Denis Villeneuve, marking a major milestone in his career.]\n\n Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks.\n In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.\n Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response. \n Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. \n The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. \n Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. \n It is very important to provide only the final output without any additional comments or remarks for example: 'Certainly!' Here's the revised text.\n \n Please provide the text directly without any labels such as \"Conclusion:\", \"Introduction:\", or \"Transition:\". Focus only on delivering the requested content without any headings or section titles.\n Please generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. \n The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. \n Please respond to my question, and always conclude your reply with the words \"Reply finished\" to indicate that your response is complete. \n Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}\n Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you."},{"assistant_initiates":false,"asst_text":"Sure, here's the romanized version of the song \"UN\" (2012):\n\n```\nOft\u0101dim ro doore tok\u0101r\u0101ro\nT\u0101 ki b\u0101yad beshonavim az ham bedo bey\u0101r\u0101ro?\nT\u0101 ki miyoon behshtam\u016bn az man hey asr\u0101r va az to hey enq\u0101r va?\nSokuttut beshkan, nagoo harf nad\u0101ram, nakon in k\u0101ro!\nYe rahami kon be man ke v\u0101seye to shodam y\u0101r va\nL\u0101l\u0101y\u012b bekhon vasam nemunam shab\u0101 bid\u0101r va!\nLabkhand bezan man bezanam v\u0101seye to gitar va\nEspand bar\u0101t d\u016bd konam door konam dardo bel\u0101h\u0101ro\n\n\u0100kh\u0101 raft\u0101r\u0101sh mano divoonam karde\nAge didinash, behash begid bargareshe\nSarde, sar por sarde darde\nNisti bebin\u012b naboodet ch\u0113k\u0101ram karde\n\n\u0100kh\u0101 raft\u0101r\u0101sh mano divoonam karde\nAge didinash, behash begid bargareshe\nSarde, sar por sarde darde\nNisti bebin\u012b naboodet ch\u0113k\u0101ram karde\n\nHey sakht nakon v\u0101seye moon in r\u0101ro\nT\u0101 ki b\u0101yad gerye konam p\u0101k konam cheshm\u012b ter o t\u0101ro?\nT\u0101 ki b\u0101yad harf bezanam ch\u012bz\u012b nagi gush nad\u012b harf\u0101ro?\nNak kon in k\u0101ro\nBiy\u0101 az roye doosh man in dafare ro to bebar in b\u0101r o\nYe rahami kon be man ke v\u0101seye to shodam y\u0101r va\nL\u0101l\u0101y\u012b bekhon vasam nemunam shab\u0101 bid\u0101r va!\nLabkhand bezan man bezanam v\u0101seye to gitar va\nEspand bar\u0101t d\u016bd konam door konam dardo bel\u0101h\u0101ro\n\n\u0100kh\u0101 raft\u0101r\u0101sh mano divoonam karde\nAge didinash, behash begid bargareshe\nSarde, sar por sarde darde\nNisti bebin\u012b naboodet ch\u0113k\u0101ram karde\n\n\u0100kh\u0101 raft\u0101r\u0101sh mano divoonam karde\nAge didinash, behash begid bargareshe\nSarde, sar por sarde darde\nNisti bebin\u012b naboodet ch\u0113k\u0101ram karde\n```","behavior":"degenerate_repetition","conversation_hash":"8045459b84d28c60107edeeb5f31b2bb","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Persian","model":"o1-mini-2024-09-12","n_chars":2491,"n_turns_in_conversation":1,"n_words":462,"source_row_offset":52303,"source_shard":"turns-00052.parquet","timestamp":"Mon, 18 Nov 2024 10:27:34 GMT","turn_id":"29d17c7e13643811f5bbe018","turn_index":0,"user_text":"\u0627\u0641\u062a\u0627\u062f\u06cc\u0645 \u0631\u0648 \u062f\u0648\u0631 \u062a\u06a9\u0631\u0627\u0631\u0648\n\u062a\u0627 \u06a9\u06cc \u0628\u0627\u06cc\u062f \u0628\u0634\u0646\u0648\u06cc\u0645 \u0627\u0632 \u0647\u0645 \u0628\u062f\u0648\u0628\u06cc\u0631\u0627\u0631\u0648\u061f\n\u062a\u0627 \u06a9\u06cc \u0645\u06cc\u0648\u0646 \u0628\u062d\u062b\u0627\u0645\u0648\u0646 \u0627\u0632 \u0645\u0646 \u0647\u06cc \u0627\u0635\u0631\u0627\u0631 \u0648 \u0627\u0632 \u062a\u0648 \u0647\u06cc \u0627\u0646\u06a9\u0627\u0631 \u0648\u061f\n\u0633\u06a9\u0648\u062a\u062a\u0648 \u0628\u0634\u06a9\u0646\u060c \u0646\u06af\u0648 \u062d\u0631\u0641 \u0646\u062f\u0627\u0631\u0645\u060c \u0646\u06a9\u0646 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u0648!\n\u06cc\u0647 \u0631\u062d\u0645\u06cc \u06a9\u0646 \u0628\u0647 \u0645\u0646 \u06a9\u0647 \u0648\u0627\u0633\u0647 \u062a\u0648 \u0634\u062f\u0645 \u06cc\u0627\u0631 \u0648\n\u0644\u0627\u0644\u0627\u06cc\u06cc \u0628\u062e\u0648\u0646 \u0648\u0627\u0633\u0645 \u0646\u0645\u0648\u0646\u0645 \u0634\u0628\u0627 \u0628\u06cc\u062f\u0627\u0631 \u0648!\n\u0644\u0628\u062e\u0646\u062f \u0628\u0632\u0646 \u0645\u0646 \u0628\u0632\u0646\u0645 \u0648\u0627\u0633\u0647 \u062a\u0648 \u06af\u06cc\u062a\u0627\u0631 \u0648\n\u0627\u0633\u067e\u0646\u062f \u0628\u0631\u0627\u062a \u062f\u0648\u062f \u06a9\u0646\u0645 \u062f\u0648\u0631 \u06a9\u0646\u0645 \u062f\u0631\u062f\u0648\u0628\u0644\u0627\u0647\u0627\u0631\u0648\n \n\u0622\u062e\u0647 \u0631\u0641\u062a\u0627\u0631\u0627\u0634 \u0645\u0646\u0648 \u062f\u06cc\u0648\u0648\u0646\u0647\u200c\u0645 \u06a9\u0631\u062f\u0647\n\u0627\u06af\u0647 \u062f\u06cc\u062f\u06cc\u0646\u0634\u060c \u0628\u0647\u0634 \u0628\u06af\u06cc\u062f \u0628\u0631\u06af\u0631\u062f\u0647\n\u0633\u0631\u062f\u0647\u060c \u0633\u0631 \u067e\u0631 \u0633\u0631\u062f\u0631\u062f\u0647\n\u0646\u06cc\u0633\u062a\u06cc \u0628\u0628\u06cc\u0646\u06cc \u0646\u0628\u0648\u062f\u062a \u0686\u06cc\u06a9\u0627\u0631\u0645 \u06a9\u0631\u062f\u0647\n \n\u0622\u062e\u0647 \u0631\u0641\u062a\u0627\u0631\u0627\u0634 \u0645\u0646\u0648 \u062f\u06cc\u0648\u0648\u0646\u0647\u200c\u0645 \u06a9\u0631\u062f\u0647\n\u0627\u06af\u0647 \u062f\u06cc\u062f\u06cc\u0646\u0634\u060c \u0628\u0647\u0634 \u0628\u06af\u06cc\u062f \u0628\u0631\u06af\u0631\u062f\u0647\n\u0633\u0631\u062f\u0647\u060c \u0633\u0631 \u067e\u0631 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\u062f\u06cc\u062f\u06cc\u0646\u0634\u060c \u0628\u0647\u0634 \u0628\u06af\u06cc\u062f \u0628\u0631\u06af\u0631\u062f\u0647\n\u0633\u0631\u062f\u0647\u060c \u0633\u0631 \u067e\u0631 \u0633\u0631\u062f\u0631\u062f\u0647\n\u0646\u06cc\u0633\u062a\u06cc \u0628\u0628\u06cc\u0646\u06cc \u0646\u0628\u0648\u062f\u062a \u0686\u06cc\u06a9\u0627\u0631\u0645 \u06a9\u0631\u062f\u0647\n \n\u0622\u062e\u0647 \u0631\u0641\u062a\u0627\u0631\u0627\u0634 \u0645\u0646\u0648 \u062f\u06cc\u0648\u0648\u0646\u0647\u200c\u0645 \u06a9\u0631\u062f\u0647\n\u0627\u06af\u0647 \u062f\u06cc\u062f\u06cc\u0646\u0634\u060c \u0628\u0647\u0634 \u0628\u06af\u06cc\u062f \u0628\u0631\u06af\u0631\u062f\u0647\n\u0633\u0631\u062f\u0647\u060c \u0633\u0631 \u067e\u0631 \u0633\u0631\u062f\u0631\u062f\u0647\n\u0646\u06cc\u0633\u062a\u06cc \u0628\u0628\u06cc\u0646\u06cc \u0646\u0628\u0648\u062f\u062a \u0686\u06cc\u06a9\u0627\u0631\u0645 \u06a9\u0631\u062f\u0647\n\nUN (2012) Romanized version"}],"shard":{"index":52,"name":"turns-00052.parquet","rows":53077}}