On-line heat flux estimation of a nonlinear heat conduction system with complex geometry using a sequential inverse method and artificial neural network
On-line heat flux estimation of a nonlinear heat conduction system with complex geometry using a sequential inverse method and artificial neural network
复制标题
使用顺序逆方法和人工神经网络在线估计复杂几何非线性热传导系统的热通量
DOI：
10.1016/j.ijheatmasstransfer.2019.118491
复制
发表时间：
2019-11
影响因子：
5.2
通讯作者：
Zhouping Yin
中科院分区：
文献类型：
--
作者：
Shuwen Huang;Bo Tao;Jindang Li;Zhouping Yin
本文提出了一种由瞬态温度测量在线估计具有复杂几何形状的非线性热传导系统的时变表面热流密度的方法。该研究包括两个问题：正问题和反问题。对于正问题，提出了一种基于人工神经网络（ANN）的快速准确的方法，将任意已知热流密度非线性映射到相应的温度。训练数据通过离线有限元仿真获得。针对反问题，提出了一种自适应序贯Tikhonov正则化（ASTR）方法来估计边界热流，该方法不依赖于未来测量，在在线应用中显示出优越性.在在线过程的每一步，训练好的神经网络被ASTR程序调用，计算温度和灵敏度系数。耦合的方法，称为ASTR-ANN，进行了测试，在一个三维固体系统的非线性热性能和复杂的几何形状。与现有的几种非线性反演方法进行了比较，结果表明，所提出的ASTR-ANN方法的有效性。
This paper presents a method for on-line estimating a time-varying surface heat flux of a nonlinear heat conduction system with complex geometry from transient temperature measurements. The study consisted of two problems: the forward problem and the inverse problem. For the forward problem, a fast and accurate method based on artificial neural network (ANN) was developed to nonlinearly map any known heat flux to the corresponding temperatures. The training data was obtained by off-line finite element simulations. For the inverse problem, an adaptive sequential Tikhonov regularization (ASTR) method was proposed to estimate the boundary heat flux, which shows superiority in on-line applications due to its independence of future measurement. At each time step of the on-line process, the trained ANN was called by the ASTR procedure to calculate the temperature and sensitivity coefficient. The coupled method, referred to as ASTR-ANN, was tested numerically in a three-dimensional solid system with nonlinear thermal properties and complex geometry. Comparisons with several existing nonlinear inverse methods were also conducted, and the results demonstrated the validity of the proposed ASTR-ANN method.
登录
查看更多内容
影响因子：
2
作者：
J. Beck;B. Litkouhi;C. R. S. Clair
通讯作者：
J. Beck;B. Litkouhi;C. R. S. Clair
DOI：
10.1080/10407790.2017.1347004
发表时间：
2017-07
期刊：
Numerical Heat Transfer, Part B: Fundamentals
影响因子：
--
作者：
Hongchu Chen;J. Frankel;M. Keyhani
通讯作者：
Hongchu Chen;J. Frankel;M. Keyhani
影响因子：
4.5
作者：
Samarjeet Chanda;K. Muralidhar;Yogesh M. Nimdeo
通讯作者：
Samarjeet Chanda;K. Muralidhar;Yogesh M. Nimdeo
DOI：
10.1016/j.ijheatmasstransfer.2015.09.029
发表时间：
2016-02
影响因子：
5.2
作者：
P. Duda
通讯作者：
P. Duda
DOI：
10.1016/j.ijheatmasstransfer.2006.11.019
发表时间：
2007-06
影响因子：
5.2
作者：
S. Deng;Y. Hwang
通讯作者：
S. Deng;Y. Hwang