A fast inversion approach for the identification of highly transient surface heat flux based on the generative adversarial network

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The efficient identification of surface heat flux during highly transient pool boiling process is an essential task for engineering production and manufacture, which can be mathematically defined as nonlinear inverse heat transfer problems (IHTP) of Neumann boundary conditions. Conventional regularization-based inversion approaches for these types of problems are computationally expensive since packs of nonlinear partial differential equations (PDE) constrained optimization problems in complex computational domains need to be solved. In this paper, an operator-learning method is proposed for the efficient solution of complex three-dimensional (3D) transient nonlinear IHTP. FluxNet, a generated adversarial network composed of a discrete wavelet transform (DWT) layer, a “generator” and a “discriminator”, is constructed to significantly improve the computational efficiency, reaching sub-second for the first time. The DWT layer is capable of extracting detailed information of the temperature distribution while the “generator” approximates the inverse operator of the governing PDE system with its convolution kernels. A minimax optimization strategy is adopted through adversarial training between the “generator” and the “discriminator” to encourage deeper understanding of data distribution. Compared with other types of deep learning methods, the proposed method shows its superiority when dealing with highly transient experimental data and micro-nano porous structured geometry. Based on the developed sub-second light-weight numerical solver with operator learning, the development of advanced real-time soft sensor techniques for a class of 3D transient IHTP in practical engineering applications is possible.