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Turbulence modeling and machine learning for performance optimization of solar air heaters: State-of-the-art and future directions

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  • Naved, Mohd
  • Dewan, Anupam

Abstract

Solar air heaters (SAHs) offer a sustainable and cost-effective solution for low-grade thermal applications such as space heating, crop drying, and industrial preheating, providing a viable alternative to fossil fuel-based systems. However, their overall performance is strongly governed by the turbulent heat transfer mechanisms, particularly when surface roughness elements or jet impingement are used to increase convective heat transfer between air and absorber plate. Accurately predicting and optimizing such turbulent processes remains challenging using conventional computational approaches. Conventional RANS models exhibit computational efficiency, yet they fail to accurately capture flow separation, reattachment, and anisotropy near walls. High-fidelity methods such as LES, DES, and DNS are too expensive for design optimization and real-time control. Recent advancements in machine learning (ML) have provided data-driven and physics-informed frameworks that improve turbulence modeling, expedite CFD solvers, and facilitate predictive optimization through hybrid ML-Physics models. Innovative concepts, such as digital twin and real-time machine learning-based control systems provide adaptive monitoring and optimization of SAHs in dynamic environments, directly enhancing sustainable energy management and supporting the UN Sustainable Development Goals (SDGs). This review exclusively examines the deficiencies in turbulence modeling research, transitioning from RANS-based methods to Hybrid-ML approaches, emphasizing the use of Physics-Informed Neural Networks (PINNs), data-driven closures, and evolutionary algorithms. By emphasizing the cross-disciplinary convergence of fluid mechanics, computational modeling, and artificial intelligence, the integration of turbulence modeling and machine learning represents a significant advancement in solar air heating solutions that are intelligent, energy-efficient, and scalable.

Suggested Citation

  • Naved, Mohd & Dewan, Anupam, 2026. "Turbulence modeling and machine learning for performance optimization of solar air heaters: State-of-the-art and future directions," Applied Energy, Elsevier, vol. 407(C).
    • Handle: RePEc:eee:appene:v:407:y:2026:i:c:s0306261925020513
    DOI: 10.1016/j.apenergy.2025.127321
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