Image-based reconstruction for a 3D-PFHS heat transfer problem by ReConNN

Abstract

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. Nomenclature --variableL length of the heat sink r second moment estimation

The Influence of Geometry, Surface Texture, and Cooling Method on the Efficiency of Heat Dissipation through the Heat Sink—A Review

“…The Reconstructive Neural Network model has been suggested for the reconstruction of heat transfer processes to construct more objective and more information-included models. The research team states that these models can be further applied to research or some experiments that use high-speed photography to catch some dynamic characteristics and reconstruct the model through these photos [59].…”

“…Heat sinks have found particular use in electronics [9][10][11]15,21,26,[34][35][36][37][47][48][49][50][51][52]58]. However, they are also used in various fields of industry, including the cooling of photovoltaic systems [16], battery thermal management systems [17], platform inertial navigation systems [18] in space engineering applications [19], the cooling of semiconductor chips [22], in air dryers [26], lighting systems [8], and thermoelectric generators [59].…”

“…Materials 2023, 16, 5348 2 of 20 photovoltaic systems [16], battery thermal management systems [17], platform inertial navigation systems [18] in space engineering applications [19], the cooling of semiconductor chips [22], in air dryers [26], lighting systems [8], and thermoelectric generators [59].…”

“…Currently, advanced numerical methods based primarily on the finite element method or genetic algorithms are used for this purpose [4,7,9,10,13,14,19,20,[29][30][31][32][33][35][36][37][41][42][43][44][45][48][49][50]53,54]. Moreover, several other reviews of heat sinks have also been published so far [59,[63][64][65][66].…”

Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques

“…Gradient-based methods include the Levenberg-Marquardt method [12,[14][15][16], the function specification method [17], and the conjugate gradient method [18][19][20]. Stochastic-based methods encompass the Bayesian method [21], the fuzzy inference method [22], and the deep neural network algorithms [23]. Additionally, for transient heat transfer phenomena, the choice of time domain for utilizing measurements plays a crucial role in classifying the solution method.…”