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

Traditional thermal‑system design usually copies old configurations, which makes it hard to innovate when new, very complex problems appear. The paper introduces a step‑by‑step framework that pulls useful design knowledge straight from optimization data for system configurations. It looks at multi‑split, fluid‑based thermal‑management systems and uses several classification‑machine‑learning methods to study the optimization results. By training classifiers on these data, the method uncovers the patterns and rules that define the best system layouts. The framework is tested on four case studies that get progressively more complex. The extracted rules help designers choose better options for future thermal systems. Because the original dataset and the exact classifier settings aren’t fully described,

the implementation guide suggests: – building representative fluid‑network simulations, – creating synthetic optimization datasets, and – trying standard classifiers such as decision trees or support‑vector machines to link design variables with performance metrics.

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