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Optimization of Heat Reflective Triple Glazing Thickness Using the Artificial Fish Swarm Algorithm
- 1 City University of Macau
- 2 Dongguan University of Technology
- 3 Shanghai Ocean University
* Author to whom correspondence should be addressed.
Abstract
The significance of this research lies in its potential to improve energy efficiency in buildings, particularly in regions prone to high temperatures. By optimizing the thermal performance of heat-reflective glass, buildings can achieve better temperature regulation, leading to reduced dependency on air conditioning systems and lower energy costs. This research specifically targets the optimization of glass thickness, a critical factor influencing the overall effectiveness of heat-reflective glazing. The focus is on minimizing the transmitted light intensity for sunlight within the wavelength range of 300 to 2000 nm, which is vertically incident on the glass. The optimized glass thickness aims to maximize the thermal reflective properties while ensuring adequate natural light within indoor spaces. This study employs the Artificial Fish Swarm Algorithm to explore the optimal thickness of heat-reflective triple glazing. The effectiveness of this optimization method is assessed by comparing its results with experimental findings obtained through a systematic traversal of glazing thicknesses. The results highlight the potential of the Artificial Fish Swarm Algorithm to significantly enhance the thermal performance of heat-reflective glass, offering a practical solution for energy-efficient building design in hot climates.
Keywords
Glass, Artificial Fish Swarm Algorithm, Heat-Reflection, Light Intensity.
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Cite this article
Huang,J.;Liu,J.;Shen,H. (2024). Optimization of Heat Reflective Triple Glazing Thickness Using the Artificial Fish Swarm Algorithm. Applied and Computational Engineering,94,40-48.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
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