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使用人工神经网络对VVER反应堆燃料核化物成分变化的建模
Prince Asabi Boakye1, Alexey Goryunov Germanovich2
1Tomsk Polytechnic University Research Institute of Nuclear Physics, Tomsk, Russia.
Heliyon
|February 21, 2024
概括
这项研究引入了一个自适应的神经模糊推理系统 (ANFIS) 模型,用于预测VVER反应堆燃料中的核化物度变化. 该ANFIS模型提供了准确和高效的预测,对于反应堆安全系统至关重要.
科学领域:
- 核工程 核工程是指核工程.
- 计算科学 计算科学
背景情况:
- 监测VVER反应堆燃料中的核度变化对于安全运行至关重要.
- 无法控制的变化可能导致反应堆不安全的条件.
研究的目的:
- 开发一种基于计算机的模型,使用自适应性神经模糊推理系统 (ANFIS) 来预测VVER反应堆燃料中的核化物度变化.
- 确保准确和及时的监测,以提高反应堆安全.
主要方法:
- 利用了自适应神经模糊推断系统 (ANFIS),一种人工智能技术.
- 使用MATLAB Simulink用于初始计算建模和数据生成.
- 与Simulink数据和国际原子能机构的CAIN代码对比验证的ANFIS模型预测.
主要成果:
- ANFIS模型与Simulink模拟和CAIN代码都显示出良好的一致性.
- 在训练数据中达到约0.98%的根平均平方误差 (RMSE),在测试数据中达到1.25%.
- 该模型在预测核化物度方面被证明是有效,精确和时间效率高的.
结论:
- 开发的ANFIS模型是预测VVER反应堆燃料中的核化物度的可靠工具.
- 这种预测能力对于加强反应堆安全系统至关重要.
- 安菲斯模型为核燃料分析提供了有效,精确和快速的解决方案.
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