Deep learning-driven topology optimization for heat dissipation of integrated electrical components using dual temperature gradient learning and MMC method

Abstract

Highly integrated electrical components produce intensive heat while in use, which will seriously impact their performance if not properly designed. In this study, an end-to-end heat dissipation structure topology optimization prediction framework considering physical mechanisms was established by using the convolutional neural network (CNN) and the moving morphable components (MMC) method. Aiming at the sparsity of physical field matrix caused by the initial component distribution in MMC method, a CNN model was established taking the temperature gradient information of both homogeneous material and initial component layout as input. Compared with other seven input forms, the CNN model in this study considers both the initial component layout and the physical field information of the structure, which can predict the topology configuration of heat dissipation structure more accurately. In addition, an improved penalty mean square error (PMSE) function was proposed by introducing a penalty factor, which improved the prediction ability of the CNN model on the structural boundary and ensured more accurate and efficient structural heat dissipation performance. Several 2D and 3D numerical examples verified the effectiveness of the proposed framework and the dual temperature gradient input model. The overall framework provides a new method for the innovative and efficient heat dissipation structure topology optimization in packaging structure of electronic equipment.