Integrated Topology Optimization and AI Strategies for Liquid Cold Plate Thermal Management

Efficient thermal management of high-power electronic devices is a critical challenge in today’s rapidly evolving technology landscape. This paper presents an integrated multi-objective optimization framework that combines topology optimization with artificial intelligence (AI) techniques for the design of liquid cold plates (LCP). By employing a two-dimensional three-layer numerical model to simulate the coupled thermal-fluid dynamics, the proposed framework optimizes key performance indicators-thermal resistance, pressure drop, and temperature uniformity-through a weight-adjusted strategy. In addition, the integration of deep learning methods (convolutional neural networks, reinforcement learning, and generative adversarial networks) enables rapid evaluation and global exploration of the design space, significantly reducing design iteration time. Under various flow conditions (50,250, and $500 \mathrm{~mL} / \mathrm{min}$), the optimized designs demonstrate substantial improvements in thermal performance and energy efficiency when compared with traditional computational fluid dynamics (CFD)-based optimization methods. The findings of this study present a novel and efficient methodology for thermal management in highpower electronics, data centers, and other high-heat-flux applications. The results confirm that the integrated approach not only improves cooling performance but also streamlines the design process, making it a promising solution for advanced liquid cold plate thermal management.