Estimation of in-plane thermal conductivity of copper clad board by inverse analysis using artificial neural networks
29 Aug 2018-Vol. 396, Iss: 1, pp 012054
About: The article was published on 2018-08-29 and is currently open access. It has received None citations till now. The article focuses on the topics: Thermal conductivity & Artificial neural network.
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TL;DR: In this paper, the authors deal with determination of the thermal conductivity tensor for orthotropic media and more specifically for multilayers with isotropic thermal characteristics in the planes parallel to the layers.
47 citations
TL;DR: In this article, the inverse heat conduction problem (IHCP) has been identified as a good candidate for solution using artificial neural networks (ANNs) and the ability of A...
Abstract: The inverse heat conduction problem (IHCP) has been identified as a good candidate for solution using artificial neural networks (ANNs) [5, 9]. Reasons that have been cited include the ability of A...
44 citations
TL;DR: In this article, an inverse analysis is applied to estimate simultaneously the constant thermal conductivity coefficients for orthotropic materials by using both the conjugate gradient and Levenberg-Marquardt methods.
44 citations
TL;DR: In this paper, a statistical procedure called nonlinear estimation can be used to simultaneously determine several properties appearing in certain partial differential equations if appropriate measurements are made, such as thermal conductivity and specific heat given transient temperature measurements.
36 citations
TL;DR: In this paper, the principal thermal conductivities (kx,ky, and kz) of an anisotropic composite medium using an inverse heat transfer analysis were determined using a two-layer feed forward back propagation artificial neural network (ANN) trained using the Levenberg-Marquardt algorithm.
Abstract: This paper reports the results of an experimental study to determine the principal thermal conductivities (kx,ky, and kz) of an anisotropic composite medium using an inverse heat transfer analysis. The direct problem consists of solving the three dimensional heat conduction equation in an orthotropic composite medium with the finite difference method to generate the required temperature distribution for known thermal conductivities. The measurement technique involves dissipating a known heat flux at the central region of a square sample and allowing it to conductively transfer the heat to an aluminium cold plate sink via a square copper ring. At steady state, temperatures at 28 (19 are used for retrievals due to symmetry) discrete locations are logged and used for parameter estimation. The entire measurement process is conducted in a vacuum environment. The inverse heat conduction problem (IHCP) for retrieving the orthotropic thermal conductivity tensor(parameter estimation) is then solved using a two layer feed forward back propagation artificial neural network (ANN) trained using the Levenberg–Marquardt algorithm (LMA), with temperatures as input and thermal conductivity values kx,ky, and kz as the output. The method is first validated against a stainless steel(SS-304) sample of known thermal properties followed by the determination of the orthotropic conductivities of the honeycomb composite material.
18 citations