Optimizing Energy Transmission of Multi- Layer Glass by Fish School Algorithm
DOI:
https://doi.org/10.61173/drt5mn04Keywords:
Energy-saving optimization, Fish swarm algorithm, Solar radiation transmittance, Building energy conservationAbstract
In cold northern regions, winter heating energy consumption is high, with exterior window heat loss accounting for 50% of total building heat loss. Threelayer glass is the mainstream exterior window material here, yet most existing studies focus on double-layer glass or traditional algorithms, lacking exploration of three-layer glass thickness collaborative optimization and fish swarm algorithm application. This study aims to maximize the indoor solar incident energy in northern winters. A three-layer glass solar radiation transmittance calculation model is constructed for the 300-2000nm solar band to clarify the mapping relationship between incident energy and thickness parameters. The fish swarm algorithm is introduced, taking the thickness parameter combination of “three layers of glass + two layers of air” as the optimization object. The iterative optimization of foraging, clustering, and tail-following is achieved through the MATLAB platform. The results show that the algorithm has good convergence. The optimal parameter combination is L1 = 6.86mm, L2 = 6.52mm, L3 = 5.81mm, Lair1 = 11.58mm, and Lair2 = 6.63mm. At this time, the near-infrared band transmittance is 0.8 to 0.95, the visible light band transmittance is stable at 0.7 to 0.85, and the ultraviolet band transmittance is effectively controlled. This study provides quantitative parameters for the energysaving design of three-layer glass windows in northern winters and offers a new idea for the multi-parameter collaborative optimization of building windows.
References
[1] Li R X. Energy efficiency analysis of building windows and doors in cold regions. Jiangxi Building Materials, 2021, (6): 143–144.
[2] Xia Y, Li P Y, Liu Z Q. Selection and evaluation of energysaving technologies for exterior windows in severe cold regions. Low Temperature Building Technology, 2019, 41(12): 15– 18+24.
[3] Yu J H, Tian L W, Yang C Z. Analysis of the optimal thickness of roof insulation layer for residential buildings in hot-summer and cold-winter regions. Journal of Central South University (Science and Technology), 2012, 43(4): 1545–1550.
[4] Huang C H, Ye Y J. Economic optimization of the insulation layer thickness for exterior walls of energy-saving buildings. Building Energy Efficiency, Ventilation and Air Conditioning, 2005, (6): 73–76.
[5] Nyers J, Kajtar L, Tomic S, et al. Investment–savings method for energy–economic optimization of external wall thermal insulation thickness. Energy and Buildings, 2015, 86: 268–274.
[6] Zhang Y, Mu J G. Optimization study of insulation thickness for building exterior walls and parametric relationships of design parameters. Building Science, 2019, 35(12): 113–125.
[7] Liu Y L, Liu J S. Analysis of detection methods for optical and thermal properties of architectural glass. Foshan Ceramics, 2024, 34(5): 59–61.
[8] Wang T P, Wang L B. A steady heat transfer model of hollow double glazing under entire-wavelength heat radiation. Energy and Buildings, 2014, 81: 72–83.
[9] Cuce E, Young C H, Riffat S B. Thermal performance investigation of heat-insulation solar glass: A comparative experimental study. Energy and Buildings, 2015, 86: 595–600.
[10] Zhou Y M, Wang F S, Li Z W, et al. Analysis of visiblelight parameters of multi-layer glass structures. Building Energy Efficiency, Ventilation and Air Conditioning, 2017, 36(1): 18–22.
[11] Wang H X, Huang Z, Qian B, et al. Route planning for infrared inspection of power lines by unmanned aerial vehicles based on improved artificial fish swarm algorithm. Microcomputer Applications, 2022, 38(8): 35–38.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
