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.
翻译:Plate fin Heat Sink(PFHS)的热传输性能已经进行了实验和广泛的调查。一般而言,PFHS设计的目标功能是以模拟反应为基础的。与现有的研究相比,本研究的目的是将基于分析的模型转变为基于图像的热汇设计模型。与基于最大、中值、差异值等的广受欢迎的目标功能相比,更多的信息应当包含在基于图像的基础上,因此应当构建一个更客观的模式。这意味着顺序优化应当以图像为基础,而不是以回应为基础,并应当获得更合理的解决方案。因此,3D-PFHS设计设计的目标功能基于图像的重建模型将建立为3D-PFHS的热传输过程;与图像识别不同,这种程序不能直接通过现有的识别算法(例如革命神经网络)实施。因此,建议并改进一个重新构建神经网络(Reconstruction Neam),即综合监管的学习和不受监督的学习技术,以便实现更高的准确性。根据实验结果,可以更详细和更明确地看到热传输过程,并且重建的结果对进一步的优化是有意义的。