Thermal resistance optimization of ultra-thin vapor chamber based on data-driven model and metaheuristic algorithm

Abstract

The ultra-thin vapor chamber(UTVC) is extensively utilized across various fields due to its excellent heat dissipation performance and good temperature uniformity. The data-driven modeling approach is well suited to predict the thermal resistance of the UTVC due to its great flexibility and accuracy. In this paper, a novel approach is proposed to optimize the UTVC thermal resistance, which combines the radial basis function neural network(RBFNN) model with an improved adaptive differential fish swarm evolution algorithm(ADFEA). The mean square error of the RBFNN model was 0.00016 on the training set and 0.00027 on the test sets, which indicates that the model is able to accurately predict the thermal resistance of the UTVC. The data are obtained from experiments on a mesh wick UTVC with dimensions of 124 × 14 × 1 mm. A novel optimization algorithm, ADFEA, has been designed to enhance optimization capability and convergence accuracy. This algorithm combines differential algorithm and artificial fish swarm algorithm, incorporating a parameter adaptation mechanism. The optimal operating parameters of the UTVC are obtained by ADFEA optimization and the accuracy of the optimized results is verified by experiment. The proposed optimization method provides new insights for the design and optimization of UTVC.