Improving the thermal insulation performance of buildings is crucial for enhancing energy efficiency and reducing heating and cooling demands. Vacuum glazing (VG) has emerged as a promising solution due to its reduced heat transfer compared to conventional single-glazing systems. The purpose of this study is to investigate and optimize the key parameters affecting the thermal insulation performance of vacuum glazing.
This study presents an integrated numerical simulation and response surface methodology (RSM) framework for analyzing the various factors influencing the insulation performance of the VG and for developing a high-accuracy predictive model, which is used as the objective function for analyzing the thermal transmittance (U-value) of the VG. The RSM is used to evaluate the significance of key factors affecting the U-value, including glass emissivity, air cavity thermal conductivity, and the thermal conductivity of VG components (edge sealing, pillars, and glass panes). Furthermore, a regression model with a coefficient of determination up to 0.996 was successfully developed to estimate the center of panel U-value, enabling accurate predictions based on the influencing factors.
The results show that air thermal conductivity under vacuum conditions, which is linked to the vacuum pressure, is the most influential factor. The optimal center of panel U-value of 0.649 W/(m²·K) is achieved under specific conditions: glass emissivity of 0.05, gas thermal conductivity of 1.2 × 10-5 W/(m·K), glass thermal conductivity of 1 W/(m·K), edge sealing thermal conductivity of 7.47 W/(m·K), and pillar thermal conductivity of 1.1 W/(m·K) for a 40 cm × 40 cm VG sample.
This study contributes to the application of RSM in evaluating and optimizing VG thermal performance by systematically analyzing the significance of multiple design factors and their interactions. Unlike conventional numerical or experimental studies focusing on VG performance in cold climates or heating applications, this work provides a broader assessment of diverse VG parameters using a robust statistical approach. It also develops a predictive model for U-value estimation, offering a data-driven optimization framework for VG design.
