Abstract
Vacuum Insulation Panels (VIPs) have emerged as a forefront solution in energy-efficient building materials. Expanded perlite (EP) stands out for its unique combination of low density, cost-effectiveness, and excellent thermal insulating properties among the myriad materials employed in VIPs. This study presents an integrated model utilizing analytical methods and finite element analysis (FEA) to simulate the heat transfer and predict the thermal conductivity of EP powder VIPs across varying gas pressures. It introduces a procedure to generate representative elementary areas (REAs) adaptable to various material characteristics; in comparing the simulation results to measurement values, the proposed model demonstrates reliable predictive performance from 0.0001 to 1 atm. The proposed model efficiently handles rapid thermal conductivity changes near atmospheric pressure, resolving distortion issues in other works. Based on the model results of REAs reflecting various material characteristics, we found that reducing the non-flake ratio of particles and decreasing the thickness of flake particles obstruct the heat transfer across all pressure ranges. When the thermal conductivity of the absolute solid is relatively high, it is advisable for the industry to prioritize applying finer grinding; conversely, efforts should be directed towards reducing the thickness of flake particles.
Supported by European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 869898.
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Data Availability Statement
The processed data and program required to reproduce these findings are available to download from https://gitlab.com/leo.ziyanfu/integrated-multi-scale-model-of-thermal-conductivity-for-expanded-perlite-powder-vacuum-insulation-panels.git.
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Fu, Z., Corker, J., Fan, M. (2024). Integrated Multi-scale Model of Thermal Conductivity for Expanded Perlite Powder Vacuum Insulation Panels. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_29
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