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Flow temperature field reconstruction based on physics-informed neural network

文献类型：期刊论文

作者Yang, Yubao2; Xu, Yang2; Wang HP(王洪平)1; Liu, Yiping2; Wang, Jinjun2
刊名PHYSICS OF FLUIDS
出版日期2025-09-01
卷号37期号:9页码:19
ISSN号1070-6631
DOI10.1063/5.0284621
通讯作者Wang, Hongping(hpwang@imech.ac.cn)
英文摘要Flow temperature fields are more challenging to measure than the velocity fields. Accurate reconstruction of temperature distributions from the velocity data is crucial in heat transfer problems, especially for experimental data analysis. We apply a physics-informed neural network (PINN) to accurately infer temperature fields at arbitrary space-time points within the measurement domain, using only the velocity data and initial temperature field. The PINN minimizes a composite loss function that incorporates residuals from both the Navier-Stokes and energy equations. We validate this approach using three representative two-dimensional heat transfer cases: steady flow around a heated cylinder, synthetic jet impinging on a heated wall, and Rayleigh-B & eacute;nard convection. Recognizing that the energy and momentum equations are decoupled under incompressible conditions, we introduce an alternative network architecture in which the temperature is predicted via a dedicated subnetwork. This modification improves reconstruction accuracy by approximately 10%.
分类号一类/力学重要期刊
资助项目National Natural Science Foundation of China[12322212] ; National Natural Science Foundation of China[12172030] ; National Natural Science Foundation of China[11902019] ; Fundamental Research Funds for the Central Universities[YWF-1113] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB0620102]
WOS研究方向Mechanics ; Physics
语种英语
WOS记录号WOS:001569333900001
资助机构National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Strategic Priority Research Program of Chinese Academy of Sciences
其他责任者王洪平
源URL[http://dspace.imech.ac.cn/handle/311007/103880]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
2.Beihang Univ, Fluid Mech Key Lab Educ Minist, Beijing 100191, Peoples R China;
推荐引用方式
GB/T 7714		 Yang, Yubao,Xu, Yang,Wang HP,et al. Flow temperature field reconstruction based on physics-informed neural network[J]. PHYSICS OF FLUIDS,2025,37(9):19.
APA Yang, Yubao,Xu, Yang,王洪平,Liu, Yiping,&Wang, Jinjun.(2025).Flow temperature field reconstruction based on physics-informed neural network.PHYSICS OF FLUIDS,37(9),19.
MLA Yang, Yubao,et al."Flow temperature field reconstruction based on physics-informed neural network".PHYSICS OF FLUIDS 37.9(2025):19.

入库方式： OAI收割

来源：力学研究所

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