Structural optimization design of sinusoidal wavy plate fin heat sink with crosscut by Bayesian optimization

The optimal structure of a sinusoidal wavy plate fin heat sink with crosscut (SWHS-WC) is determined by an efficient intelligent optimization method based on Bayesian Optimization (BO) algorithm. The comprehensive thermal performance factor (TPF) is the objective function, which is associated with the heat transfer and the pressure drop. The parametric modeling, meshing, and numerical calculation are integrated to optimize three structural parameters, including the fin amplitude, period, and phase shift angle of the heat sink, in an iterative and automated way. The result shows that the TPF of the SWHS-WC with the optimized structure is increased by 17.6%, due to the significant reduction in the pressure drop penalty, and the corresponding synergy angle between the pressure gradient and the velocity decreases by about 6.2. Compared to the evolutionary algorithms, the BO algorithm demonstrates a great advantage in calculation efficiency, which can be used to find the global optimum design at a much smaller computational cost.