基于域相似度测量转移学习 (TL-PINN) 的物理信息神经网络,用于预测核反应堆中过渡的情况
Konstantinos Prantikos1,2, Stylianos Chatzidakis1, Lefteri H Tsoukalas1
1School of Nuclear Engineering, Purdue University, West Lafayette, IN, 47906, USA.
Scientific reports
|October 6, 2023
概括
转移学习显著加快了使用物理信息的神经网络 (PINNs) 的核反应器瞬态 (RT) 建模. 这种方法可以将训练代减少多达100倍,从而增强实时安全监控.
科学领域:
- 核工程 核工程是指核工程.
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 准确和快速预测核反应堆的短暂状态 (RT) 对于提高反应堆的安全性和效率至关重要.
- 基于物理学的神经网络 (PINNs) 为RT建模提供了一个有希望的替代方案,但它们的训练耗时.
- 应用PINNs在RT监控中用于运营商支持需要接近实时的性能.
研究的目的:
- 调查转移学习 (TL-PINN) 的有效性,以加快PINN在核反应堆过渡模拟中的培训.
- 量化TL-PINN与传统PINN相比实现的性能增长.
- 建立TL-PINN表现与不同RT状态的相似性之间的相关性.
主要方法:
- 开发了一种用于物理信息神经网络 (TL-PINN) 的转移学习方法.
- 利用一个点运动方程 (PKEs) 模型与六个中子前体组,参数化为普渡大学反应堆一 (PUR-1).
- 通过使用豪斯多夫和弗雷切距离生成各种RT,并描述它们的相似性.
主要成果:
- 在对单个RT进行预训练后,TL-PINN在预测不同RT状态时表现出高达两倍的加速.
- 传统PINN和TL-PINN模型对中子密度预测的平均误差小于1%.
- 在TL-PINN性能加速和RT相似性措施之间建立了相关性.
结论:
- 转移学习显著提高了基于PINN的RT建模的效率,使得预测更快.
- 在核反应堆监控应用中,TL-PINN为实现近实时性能提供了可行的解决方案.
- 开发的相关性可以指导TL-PINNs在不同反应堆过渡场景中的有效应用.
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