  The heat transfer performance of Plate Fin Heat Sink (PFHS) has been
investigated experimentally and extensively. Commonly, the objective function
of the PFHS design is based on the responses of simulations. Compared with
existing studies, the purpose of this study is to transfer from analysis-based
model to image-based one for heat sink designs. Compared with the popular
objective function based on maximum, mean, variance values etc., more
information should be involved in image-based and thus a more objective model
should be constructed. It means that the sequential optimization should be
based on images instead of responses and more reasonable solutions should be
obtained. Therefore, an image-based reconstruction model of a heat transfer
process for a 3D-PFHS is established. Unlike image recognition, such procedure
cannot be implemented by existing recognition algorithms (e.g. Convolutional
Neural Network) directly. Therefore, a Reconstructive Neural Network (ReConNN),
integrated supervised learning and unsupervised learning techniques, is
suggested and improved to achieve higher accuracy. According to the
experimental results, the heat transfer process can be observed more detailed
and clearly, and the reconstructed results are meaningful for the further
optimizations.
