Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN

The heat transfer performance of Plate Fin Heat Sink (PFHS) has been investigated experimentally and extensively. Commonly, the objective function of PFHS design is based on the responses of simulations. Compared with existing studies, the purpose of this work is to transfer from image-based model to analysis-based model for heat sink designs. It means that the sequential optimization should be based on images instead of responses. 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. 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.