Advancing Fluid-Based Thermal Management Systems Design: Leveraging
  Graph Neural Networks for Graph Regression and Efficient Enumeration
  Reduction
Saeid Bayat Nastaran Shahmansouri Satya RT Peddada Alex Tessier Adrian Butscher James T Allison
In this research, we developed a graph-based framework to represent various
aspects of optimal thermal management system design, with the aim of rapidly
and efficiently identifying optimal design candidates. Initially, the
graph-based framework is utilized to generate diverse thermal management system
architectures. The dynamics of these system architectures are modeled under
various loading conditions, and an open-loop optimal controller is employed to
determine each system's optimal performance. These modeled cases constitute the
dataset, with the corresponding optimal performance values serving as the
labels for the data. In the subsequent step, a Graph Neural Network (GNN) model
is trained on 30% of the labeled data to predict the systems' performance,
effectively addressing a regression problem. Utilizing this trained model, we
estimate the performance values for the remaining 70% of the data, which serves
as the test set. In the third step, the predicted performance values are
employed to rank the test data, facilitating prioritized evaluation of the
design scenarios. Specifically, a small subset of the test data with the
highest estimated ranks undergoes evaluation via the open-loop optimal control
solver. This targeted approach concentrates on evaluating higher-ranked designs
identified by the GNN, replacing the exhaustive search (enumeration-based) of
all design cases. The results demonstrate a significant average reduction of
over 92% in the number of system dynamic modeling and optimal control analyses
required to identify optimal design scenarios.