Multi-Objective Numerical Design of Robust and Color-Compatible Passive Daytime Radiative Cooling Coatings

Abstract

Passive daytime radiative cooling (PDRC) offers a promising route for reducing building cooling demand, yet practical deployment requires balancing cooling performance with visual compatibility and environmental robustness. In this study, a fully simulation-based framework is developed to design coating-oriented PDRC structures under simultaneous constraints of solar reflectance, mid-infrared emissivity, color compatibility, and degradation tolerance. Spectral performance is evaluated over the solar (0.3-2.5 μm) and mid-infrared (5-25 μm) ranges, and a multi-objective optimization procedure is employed to identify Pareto-optimal solutions that balance cooling performance, color compatibility, and degradation tolerance rather than a single idealized design. The representative solutions achieved solar-weighted reflectance values of 0.80-0.95, atmospheric-window emissivity values of 0.88-0.98, and ideal-condition net radiative cooling power values of 60-85 W/m². Among the selected reference designs, D1, D2, and D3 yielded net cooling powers of approximately 85, 70, and 60 W/m², respectively, reflecting different trade-off regions within the Pareto front. Under the degradation scenarios considered, these designs retained approximately 76%, 71%, and 67% of their initial cooling capability. The results show that coating-compatible PDRC designs can preserve meaningful daytime cooling performance while satisfying additional non-thermal constraints, and that robustness to humidity- and soiling-related degradation can be incorporated directly at the design stage. The proposed simulation-based multi-objective design methodology offers a transferable approach for developing radiative cooling coatings that account for aesthetic constraints and degradation-related performance losses.

Keywords

• Passive daytime radiative cooling
• energy-efficient thermal management
• color-compatible coatings
• environmental robustness
• multi-objective numerical optimization
• spectral optical design

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