  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.
