基于LSTM神经网络的BEAVRS核心中子传输计算
Changan Ren1,2, Li He3, Jichong Lei1
1School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China.
Scientific reports
|September 6, 2023
概括
这项研究应用了深度学习,特别是长短期记忆 (LSTM) 算法,来模拟核反应堆模拟. 人工智能模型准确地预测了反应堆核心的行为,展示了机器学习.
科学领域:
- 核工程 核工程是指核工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 人工智能和大数据的进步正在改变各种行业.
- 核工程可以从人工智能中受益,用于复杂的模拟.
研究的目的:
- 使用深度学习建模BEAVRS (反应堆模拟评估和验证的基准) 核心第一周期负载.
- 在核反应堆模拟中评估长短期记忆 (LSTM) 算法的有效性.
主要方法:
- 使用了LSTM深度学习算法.
- 使用DRAGON和DONJON模拟了BEAVRS核心.
- 按时间顺序安排训练和测试数据集.
- 通过调整超参数优化了LSTM模型.
主要成果:
- 在2.5 pcm (10^-5) 范围内实现有效乘法因子 (keff) 的最大预测误差.
- 达到0.5266 pcm以下的平均预测误差.
- 证明了机器学习在解决运输方程中的成功应用.
结论:
- 开发的LSTM模型在预测核反应堆核心行为方面表现出很高的准确性.
- 超参数调整对于优化核工程中AI模型性能至关重要.
- 机器学习,特别是LSTM,是核运输方程建模的可行工具.
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