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As modern electronic devices are increasingly miniaturized and integrated,their performance relies more heavily on effective thermal management.Two-phase cooling methods enhanced by porous surfaces, which capitalize onthin-film evaporation atop structured porous surfaces, are emerging aspotential solutions. In such porous structures, the optimum heat dissipationcapacity relies on two competing objectives that depend on mass and heattransfer. The computational costs of evaluating these objectives, the highdimensionality of the design space which a voxelated microstructurerepresentation, and the manufacturability constraints hinder the optimizationprocess for thermal management. We address these challenges by developing adata-driven framework for designing optimal porous microstructures for coolingapplications. In our framework we leverage spectral density functions (SDFs) toencode the design space via a handful of interpretable variables and, in turn,efficiently search it. We develop physics-based formulas to quantify thethermofluidic properties and feasibility of candidate designs via offlinesimulations. To decrease the reliance on expensive simulations, we generatemulti-fidelity data and build emulators to find Pareto-optimal designs. Weapply our approach to a canonical problem on evaporator wick design and obtainfin-like topologies in the optimal microstructures which are alsocharacteristics often observed in industrial applications.

Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications