Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN
This work addresses heat sink design optimization for engineering applications, but it is incremental as it adapts existing neural network techniques to a specific domain.
The study tackled the problem of designing Plate Fin Heat Sinks (PFHS) by shifting from analysis-based to image-based models for heat transfer reconstruction, using a Reconstructive Neural Network (ReConNN) to achieve more detailed and meaningful results for optimization.
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.