Three-Layer Glass Thickness Optimization and Solar Heat Gain Minimization Based on Quantum Genetic Algorithm

Abstract

This study focuses on improving the thermal performance of external windows. Building operations account for 21% of total societal energy consumption, and heating through external windows can account for 40-50% of the air conditioning cooling load. Although three-layer glass windows have great potential, their performance hinges on the micro configuration of layer thicknesses, and an empirical equal-thickness design is challenging to balance the trade-off between lighting and insulation. Traditional genetic algorithms (GAs) are prone to premature convergence when solving high-dimensional, nonlinear optimization problems. This study focuses on the 300-2500 nm solar spectrum and, for the first time, applies an improved quantum genetic algorithm (QGA) to the collaborative optimization of three-layer glass window thicknesses ( l1 , l2 ,l3 ). The core innovation lies in: Based on the Fabry-Perot interference principle, an analytical formula for the single-layer equivalent model was adopted as the optical transmission model. This method has the advantages of clear physical meaning and high computational efficiency compared to the general transmission matrix method (TMM). Simulation experiments show that the optimized thickness combination ( l1 mm = 6.08 , l2 mm = 6.08 , l3 mm = 8.37 ) successfully achieves spectral-selective control. The average transmittance in the visible light region remains at 0.7957, while the transmittance in the near-infrared region is suppressed to 0.7860. Compared with traditional design, the energy-saving potential is significant. This study provides new algorithmic tools and theoretical support for the design of high-performance energy-saving windows.