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In this research, we developed a graph-based framework to represent variousaspects of optimal thermal management system design, with the aim of rapidlyand efficiently identifying optimal design candidates. Initially, thegraph-based framework is utilized to generate diverse thermal management systemarchitectures. The dynamics of these system architectures are modeled undervarious loading conditions, and an open-loop optimal controller is employed todetermine each system's optimal performance. These modeled cases constitute thedataset, with the corresponding optimal performance values serving as thelabels for the data. In the subsequent step, a Graph Neural Network (GNN) modelis trained on 30% of the labeled data to predict the systems' performance,effectively addressing a regression problem. Utilizing this trained model, weestimate the performance values for the remaining 70% of the data, which servesas the test set. In the third step, the predicted performance values areemployed to rank the test data, facilitating prioritized evaluation of thedesign scenarios. Specifically, a small subset of the test data with thehighest estimated ranks undergoes evaluation via the open-loop optimal controlsolver. This targeted approach concentrates on evaluating higher-ranked designsidentified by the GNN, replacing the exhaustive search (enumeration-based) ofall design cases. The results demonstrate a significant average reduction ofover 92% in the number of system dynamic modeling and optimal control analysesrequired to identify optimal design scenarios.

Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction