基于物理信息神经网络的传热过程物理场代理模型的构建

陆至彬; 瞿景辉; 刘桦; 何畅; 张冰剑; 陈清林

doi:10.11949/0438-1157.20201879

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基于物理信息神经网络的传热过程物理场代理模型的构建

过程系统工程 | 更新时间：2021-07-15

- 基于物理信息神经网络的传热过程物理场代理模型的构建
- Surrogate modeling for physical fields of heat transfer processes based on physics-informed neural network
- 化工学报 2021年72卷第3期页码：1496-1503
- 作者机构：
  
  1.中山大学材料科学与工程学院，广东广州 510275
  2.中山大学化学工程与技术学院，广东珠海 519082
  3.广东省石化过程节能工程技术研究中心，广东广州 510275
- 作者简介：
  
  陆至彬（1998—），男，硕士研究生，luzhb6@mail2.sysu.edu.cn
  何畅（1985—），男，博士，副教授，hechang6@mail.sysu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(51776228);中央高校基本科研业务费专项资金(20lgpy01)
- DOI：10.11949/0438-1157.20201879
  中图分类号： TQ;021.3
- 收稿：2020-12-20，
  
  修回：2020-12-26，
  
  纸质出版：2021-03-05
- 稿件说明：
移动端阅览
陆至彬, 瞿景辉, 刘桦, 何畅, 张冰剑, 陈清林. 基于物理信息神经网络的传热过程物理场代理模型的构建[J]. 化工学报, 2021, 72(3): 1496-1503

LU Zhibin, QU Jinghui, LIU Hua, HE Chang, ZHANG Bingjian, CHEN Qinglin. Surrogate modeling for physical fields of heat transfer processes based on physics-informed neural network[J]. CIESC Journal, 2021, 72(3): 1496-1503
陆至彬, 瞿景辉, 刘桦, 何畅, 张冰剑, 陈清林. 基于物理信息神经网络的传热过程物理场代理模型的构建[J]. 化工学报, 2021, 72(3): 1496-1503 DOI： 10.11949/0438-1157.20201879.

LU Zhibin, QU Jinghui, LIU Hua, HE Chang, ZHANG Bingjian, CHEN Qinglin. Surrogate modeling for physical fields of heat transfer processes based on physics-informed neural network[J]. CIESC Journal, 2021, 72(3): 1496-1503 DOI： 10.11949/0438-1157.20201879.

摘要

物理信息的神经网络（PINN）通过构建结构化的深度神经网络体系，可以有效地耦合基于物理定律的非线性偏微分方程组（如Navier-Stokes方程），能够在较少量的边界数据条件下解决监督学习问题。但是，PINN训练效果与边界条件的设置方式密切相关。本工作以具有内热源的二维稳态导热方程和平板间二维稳态对流传热方程为案例，基于软边界和硬边界两种设定方法构建PINN。将训练所得到的代理模型预测温度场输出，并将其与软件模拟结果进行验证分析，结果表明硬边界PINN代理模型预测能力较优。

Abstract

By constructing structured deep neural network architecture

physics-informed neural networks (PINN) can be trained to solve supervised learning tasks with limited amount of boundary data while effectively integrating any given laws of physics described by general nonlinear partial differential equations (

Navier-Stokes equation). However

the effect of PINN training is closely related to how the boundary conditions are set. In this work

two 2-D steady-state heat transfer problems

namely heat conduction model with internal heat source and convection heat transfer equation between plates are taken as examples. Two surrogate models are trained based on PINN by using two setting methods of soft boundary and hard boundary. The trained surrogate models are used to predict the output of temperature fields

which are verified and compared with the simulated data. The comparison results show that the prediction ability of PINN based on hard boundary is superior to the rival.

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