3 research outputs found
Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction
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.Comment: 13 pages, 17 figure
Multi-split configuration design for fluid-based thermal management systems
High power density systems require efficient cooling to maintain their
thermal performance. Despite this, as systems get larger and more complex,
human practice and insight may not suffice to determine the desired thermal
management system designs. To this end, a framework for automatic architecture
exploration is presented in this article for a class of single-phase,
multi-split cooling systems. For this class of systems, heat generation devices
are clustered based on their spatial information, and flow-split are added only
when required and at the location of heat devices. To generate different
architectures, candidate architectures are represented as graphs. From these
graphs, dynamic physics models are created automatically using a graph-based
thermal modeling framework. Then, an optimal fluid flow distribution problem is
solved by addressing temperature constraints in the presence of exogenous heat
loads to achieve optimal performance. The focus in this work is on the design
of general multi-split heat management systems. The architectures discussed
here can be used for various applications in the domain of configuration
design. The multi-split algorithm can produce configurations where splitting
can occur at any of the vertices. The results presented include 3 categories of
cases and are discussed in detail.Comment: 11 pages, 18 figure
Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems
As mechanical systems become more complex and technological advances
accelerate, the traditional reliance on heritage designs for engineering
endeavors is being diminished in its effectiveness. Considering the dynamic
nature of the design industry where new challenges are continually emerging,
alternative sources of knowledge need to be sought to guide future design
efforts. One promising avenue lies in the analysis of design optimization data,
which has the potential to offer valuable insights and overcome the limitations
of heritage designs. This paper presents a step toward extracting knowledge
from optimization data in multi-split fluid-based thermal management systems
using different classification machine learning methods, so that designers can
use it to guide decisions in future design efforts. This approach offers
several advantages over traditional design heritage methods, including
applicability in cases where there is no design heritage and the ability to
derive optimal designs. We showcase our framework through four case studies
with varying levels of complexity. These studies demonstrate its effectiveness
in enhancing the design of complex thermal management systems. Our results show
that the knowledge extracted from the configuration design optimization data
provides a good basis for more general design of complex thermal management
systems. It is shown that the objective value of the estimated optimal
configuration closely approximates the true optimal configuration with less
than 1 percent error, achieved using basic features based on the system heat
loads without involving the corresponding optimal open loop control (OLOC)
features. This eliminates the need to solve the OLOC problem, leading to
reduced computation costs.Comment: 13 pages, 20 figure