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<p>This study explores the impact of aspect ratio (AR) optimization on the thermal performance of conical solar stills to enhance freshwater production. Recognizing that conical geometry significantly influences solar absorption and evaporation efficiency, the research aims to identify the optimal AR that maximizes energy utilization and water yield under seasonal environmental conditions. A comprehensive thermodynamic model, based on energy and exergy balances, is developed to evaluate the effects of varying AR and conical angles on system performance. Numerical simulations reveal that increasing AR improves solar absorption, thermal gradients, and evaporation rates, with an optimal AR near 3.0 resulting in up to 140% enhancement in energy efficiency, 175% in exergy efficiency, and a 137% increase in the gained output ratio (GOR) compared to winter baselines. To support the identification of this optimum, artificial intelligence (AI) tools, viz. genetic algorithms (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs), are employed as secondary aids to efficiently map nonlinear thermal behavior and confirm the best-performing configurations. The results underscore the critical role of geometric design, particularly AR, in driving the efficiency of passive solar desalination systems and offer a validated, data-supported pathway for scalable, sustainable freshwater generation.</p>

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Optimization of aspect ratio for enhanced thermal and exergy efficiency in conical solar distillers: a thermodynamic and AI-driven study for sustainable freshwater production

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • M. A. Elazab,
  • Mohamed Kamel Elshaarawy,
  • H. A. Dahab

摘要

This study explores the impact of aspect ratio (AR) optimization on the thermal performance of conical solar stills to enhance freshwater production. Recognizing that conical geometry significantly influences solar absorption and evaporation efficiency, the research aims to identify the optimal AR that maximizes energy utilization and water yield under seasonal environmental conditions. A comprehensive thermodynamic model, based on energy and exergy balances, is developed to evaluate the effects of varying AR and conical angles on system performance. Numerical simulations reveal that increasing AR improves solar absorption, thermal gradients, and evaporation rates, with an optimal AR near 3.0 resulting in up to 140% enhancement in energy efficiency, 175% in exergy efficiency, and a 137% increase in the gained output ratio (GOR) compared to winter baselines. To support the identification of this optimum, artificial intelligence (AI) tools, viz. genetic algorithms (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs), are employed as secondary aids to efficiently map nonlinear thermal behavior and confirm the best-performing configurations. The results underscore the critical role of geometric design, particularly AR, in driving the efficiency of passive solar desalination systems and offer a validated, data-supported pathway for scalable, sustainable freshwater generation.