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<p>The ideal heat pipe solar absorber tilt and azimuth angles to maximize solar energy absorption and thereby improve the whole efficiency of the system. This research presents a research framework that integrates theoretical modelling and optimization methods to establish angular configurations for the heat pipe solar collectors from climatic variations. The analysis of solar insolation, seasonal variations, and geographical locality, among others, is done to enhance efficiency. In this research, the Binary Light Spectrum Optimizer (BLSO) algorithm has been utilized and showed the best performance in contrast to other traditional techniques such as differential evolution, genetic algorithms, and particle swarm optimization, with an improvement in efficiency by 5–8% over previous methods. The results revealed that BLSO promotes quick convergence and increased efficiency, with dynamic adjustments to seasonal and geographical variations. This research provides an intelligent framework for optimizing solar absorber configurations, leading to the enhancement of sustainable, high-performance solar energy systems, thus reducing dependence on non-renewable sources.</p>

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An investigational approach to determining the optimal tilt and azimuth angles for heat pipe solar collectors

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • V K Nithin

摘要

The ideal heat pipe solar absorber tilt and azimuth angles to maximize solar energy absorption and thereby improve the whole efficiency of the system. This research presents a research framework that integrates theoretical modelling and optimization methods to establish angular configurations for the heat pipe solar collectors from climatic variations. The analysis of solar insolation, seasonal variations, and geographical locality, among others, is done to enhance efficiency. In this research, the Binary Light Spectrum Optimizer (BLSO) algorithm has been utilized and showed the best performance in contrast to other traditional techniques such as differential evolution, genetic algorithms, and particle swarm optimization, with an improvement in efficiency by 5–8% over previous methods. The results revealed that BLSO promotes quick convergence and increased efficiency, with dynamic adjustments to seasonal and geographical variations. This research provides an intelligent framework for optimizing solar absorber configurations, leading to the enhancement of sustainable, high-performance solar energy systems, thus reducing dependence on non-renewable sources.