Generative Design of Liquid-Cooled Cold Plates via Enhanced Cross-Attention Conditional Diffusion Model

Abstract Generative design has emerged as an effective approach for exploring complex engineering design spaces beyond the limitations of conventional optimization, particularly for thermal management systems with high-dimensional geometric variability; however, its application to liquid-cooled cold plates remains challenging under asymmetric thermal loading conditions. To address this issue, this study proposes an enhanced cross-attention conditional diffusion framework in which physics-based conditional variables are injected into the U-Net denoiser via multi-scale cross-attention and feature-wise linear modulation, enabling condition-aware geometry generation throughout the diffusion process and thereby preserving high-fidelity geometric and physical consistency. Training data are obtained using multi-objective topology optimization to generate a diverse set of cold plate geometries together with their corresponding pressure drop Δp and temperature difference T under asymmetric thermal loading. This dataset is then used to train the proposed diffusion model generating new cold plate geometries under prescribed physical conditions. Promising design results are efficiently identified using a ResNet18-based surrogate model for rapid thermal–hydraulic performance evaluation, avoiding reliance on extensive full-order simulations. Overall, the proposed framework integrates physics-based optimization with generative modeling to provide an efficient and effective approach for designing high-performance liquid-cooled cold plates in asymmetric thermal environments.