TY - GEN
T1 - ADVANCING FLUID-BASED THERMAL MANAGEMENT SYSTEMS DESIGN
T2 - ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2024
AU - Bayat, Saeid
AU - Shahmansouri, Nastaran
AU - Peddada, Satya R.T.
AU - Tessier, Alexander
AU - Butscher, Adrian
AU - Allison, James T.
N1 - Publisher Copyright:
Copyright © 2024 by ASME.
PY - 2024
Y1 - 2024
N2 - This study introduces a graph-based framework developed for representing 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 11,134 (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 26,195 (70%) of the data, which serves as the test set. The reason for larger number of test points was to ensure that the model is capable of predicting performance across all the diversity of inputs. 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 an 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.
AB - This study introduces a graph-based framework developed for representing 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 11,134 (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 26,195 (70%) of the data, which serves as the test set. The reason for larger number of test points was to ensure that the model is capable of predicting performance across all the diversity of inputs. 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 an 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.
KW - Design Synthesis
KW - Graph Neural Network
KW - Graph Regression
KW - Machine Learning
KW - Open Loop Optimal Control
KW - System Architecture
KW - Thermal Management System Design
UR - http://www.scopus.com/inward/record.url?scp=85210884919&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210884919&partnerID=8YFLogxK
U2 - 10.1115/DETC2024-143660
DO - 10.1115/DETC2024-143660
M3 - Conference contribution
AN - SCOPUS:85210884919
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 50th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
Y2 - 25 August 2024 through 28 August 2024
ER -