Multi-Objective Generative Design for Energy-Efficient Heat Sinks: Integrating Constructal Theory with Manufacturing Constraints for Industrial Thermal Management

Abstract

Thermal management inefficiencies contribute significantly to global energy consumption, with data centers alone using 200+ TWh annually for cooling. While constructal theory predicts optimal branching geometries that minimize thermal resistance, these designs typically violate fundamental manufacturing constraints, preventing industrial implementation. This study presents a Physics-Informed Graph Diffusion Network (PI- GDN) that simultaneously optimizes thermal performance, manufacturing feasibility, production cost, and material efficiency. The framework encodes heat transfer physics and process-specific constraints for metal additive manufacturing, investment casting, and die casting into a unified multi-objective optimization. Computational validation across 500 generated designs demonstrates Pareto-optimal solutions spanning R_th = 0.24–0.39 K/W at 0.7–1.3× baseline manufacturing costs. Experimental testing of 12 fabricated prototypes confirms <4% deviation from computational predictions, with best-performing designs achieving R_th = 0.247 K/W — a 42% reduction compared to conventional extruded-fin heat sinks. A data center case study demonstrates 1.3 GWh/year energy savings across 10,000 servers, yielding a net 5-year benefit of \$1.21M. The framework includes an intelligent manufacturing method recommender that selects optimal production processes based on volume and performance targets, enabling automated design-to-production workflows.