基于PINN的混凝土热学参数反演方法  

Method for inversion of concrete thermal parameters based on PINN

作　　者：李如尧 张磊[1] 庞博 旦真旺加 郑磊 刘英杰[3] LI Ruyao;ZHANG Lei;PANG Bo;DANZHEN Wangjia;ZHENG Lei;LIU Yingjie(State Key Laboratory of Basin Water Cycle Simulation and Regulation,China Institute of Water Resources and Hydropower Research,Beijing 100038,China;Huaneng Tibet Yarlung Zangbo River Hydropower Development Investment Co.,Ltd.,Lhasa 850000,China;School of Water Conservancy and Transportation,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京100038 [2]华能西藏雅鲁藏布江水电开发投资有限公司,西藏拉萨850000 [3]郑州大学水利与交通学院,河南郑州450001

出　　处：《水利水电科技进展》2025年第2期90-97,105,共9页Advances in Science and Technology of Water Resources

基　　金：国家重点研发计划项目(2018YFC0406703);国家自然科学基金项目(51779277);流域水循环模拟与调控国家重点实验室项目(SKL2020ZY10,SS0112B102016);华能集团科技项目(HNKJ22-HF87)。

摘　　要：针对传统混凝土热学参数反演方法流程复杂、计算效率低的问题,提出了一种基于物理信息神经网络(PINN)的反演方法,其中待反演混凝土热学参数优化时的学习率根据待反演参数的精度要求确定。基于PINN反演方法,通过仿真试验反演了无水管冷却和有水管冷却的混凝土热学参数,分析了反演结果的误差,并将PINN反演方法与传统有限元计算结合遗传算法的反演方法进行了对比。结果表明:基于PINN进行混凝土热学参数反演分析具有良好的精度、鲁棒性和泛化能力;根据待反演参数的精度要求确定学习率,能够在保证反演精度的前提下有效提高计算效率;PINN反演方法相比传统的反演方法具备计算框架简单、计算效率高的优势。Aiming at the problems of complex procedures and low computational efficiency of traditional inversion methods of concrete thermal parameters,an inversion method based on physics-informed neural networks(PINN)is proposed.In this method,the learning rate for optimizing the concrete thermal parameters to be inverted is determined according to the accuracy requirements of the parameters to be inverted.Based on the PINN inversion method,the thermal parameters of concrete without and with water pipe cooling were inverted through simulation tests,and the errors of the inversion results were analyzed.The PINN inversion method was compared with the inversion method of traditional finite element calculation combined with genetic algorithm.The results show that the PINN-based method for inversion of concrete thermal parameters has high accuracy,robustness and generalization ability.Using the improved method to determine the learning rate based on the accuracy requirements of the parameters to be inverted can effectively improve the calculation efficiency while ensuring the inversion accuracy.Compared with the traditional inversion method,the PINN inversion method has advantages in terms of a simpler computational framework and higher computational efficiency.

关 键 词：混凝土温度场 热学参数 待反演参数 物理信息神经网络 学习率 

分 类 号：TU528[建筑科学—建筑技术科学] TP183[自动化与计算机技术—控制理论与控制工程]