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<p>Solar stills provide a sustainable approach to freshwater production, particularly in arid and water-scarce regions. However, their efficiency is often hindered by inconsistent solar radiation, thermal losses, and suboptimal design configurations. Computational Fluid Dynamics (CFD) has been instrumental in analyzing and optimizing solar still performance, offering in-depth insights into heat and mass transfer mechanisms. In parallel, Artificial Intelligence (AI) is emerging as a transformative tool for enhancing predictive modeling, design optimization, and real-time performance monitoring. This review presents a comprehensive analysis of CFD-based numerical studies aimed at improving solar still efficiency while exploring the potential of AI integration. Key topics include thermal energy management, system configurations, and parametric optimization, alongside a critical evaluation of CFD validation techniques and AI-driven predictive models. The review also highlights existing challenges, such as computational complexity, data availability, and the integration of AI with physics-based simulations. Future research opportunities are identified to further enhance solar still technology through hybrid CFD-AI approaches, driving innovation toward more efficient and scalable desalination solutions.</p>

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Solar still performance improvement: CFD insights and AI integration challenges

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • T. Chekifi,
  • A. Belaid,
  • M. Boukraa,
  • R. Khelifi,
  • M. Guermoui

摘要

Solar stills provide a sustainable approach to freshwater production, particularly in arid and water-scarce regions. However, their efficiency is often hindered by inconsistent solar radiation, thermal losses, and suboptimal design configurations. Computational Fluid Dynamics (CFD) has been instrumental in analyzing and optimizing solar still performance, offering in-depth insights into heat and mass transfer mechanisms. In parallel, Artificial Intelligence (AI) is emerging as a transformative tool for enhancing predictive modeling, design optimization, and real-time performance monitoring. This review presents a comprehensive analysis of CFD-based numerical studies aimed at improving solar still efficiency while exploring the potential of AI integration. Key topics include thermal energy management, system configurations, and parametric optimization, alongside a critical evaluation of CFD validation techniques and AI-driven predictive models. The review also highlights existing challenges, such as computational complexity, data availability, and the integration of AI with physics-based simulations. Future research opportunities are identified to further enhance solar still technology through hybrid CFD-AI approaches, driving innovation toward more efficient and scalable desalination solutions.