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As mechanical systems become more complex and technological advancesaccelerate, the traditional reliance on heritage designs for engineeringendeavors is being diminished in its effectiveness. Considering the dynamicnature of the design industry where new challenges are continually emerging,alternative sources of knowledge need to be sought to guide future designefforts. One promising avenue lies in the analysis of design optimization data,which has the potential to offer valuable insights and overcome the limitationsof heritage designs. This paper presents a step toward extracting knowledgefrom optimization data in multi-split fluid-based thermal management systemsusing different classification machine learning methods, so that designers canuse it to guide decisions in future design efforts. This approach offersseveral advantages over traditional design heritage methods, includingapplicability in cases where there is no design heritage and the ability toderive optimal designs. We showcase our framework through four case studieswith varying levels of complexity. These studies demonstrate its effectivenessin enhancing the design of complex thermal management systems. Our results showthat the knowledge extracted from the configuration design optimization dataprovides a good basis for more general design of complex thermal managementsystems. It is shown that the objective value of the estimated optimalconfiguration closely approximates the true optimal configuration with lessthan 1 percent error, achieved using basic features based on the system heatloads without involving the corresponding optimal open loop control (OLOC)features. This eliminates the need to solve the OLOC problem, leading toreduced computation costs.

Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems