Investigation of Heat Transfer and Flow Characteristics in Solar Air Heaters with a Spherical Turbulator Using Machine Learning Models - Bisht, Vijay Singh; Singh, Desh Bandhu; Kumar, Pushpendra; Bhandari, Prabhakar; Tipu, Rupesh Kumar; Singh, Sandeep; Bist, Ankur Singh (Archives of Thermodynamics, 2026)
Solar air heaters are attractive for low-carbon thermal applications, but their performance is constrained by weak convective heat transfer in the near-wall region. In this work, a single-pass rectangular solar air heater duct equipped with spherical turbulators is investigated numerically and then accelerated using machine-learning surrogate models for rapid prediction of thermal–hydraulic responses. Five turbulator arrangements (V-, M-, W-shaped, inclined, and arc-shaped) were evaluated at three discrete spacing levels (pitch ratio P/D = 3, 6, 9) with a constant sphere diameter of 25 mm over Re = 3500–23 500, consistent throughout all the simulations. The computational fluid dynamics model employed a constant heat flux of 1000 W/m² and standard pressure–velocity coupling/discretisation practices, and was validated against established previous work. A leakage-safe machine-learning dataset (675 samples, 36 variables) was constructed from computational fluid dynamics outputs and physics-informed engineered features; models were trained using geometry-grouped cross-validation to ensure generalisation across turbulator arrangements and spacing levels. Among candidate regressors, histogram gradient boosting provided the best Nu surrogate (OOF RMSE = 3.299, R² = 0.9908, MAPE = 2.44%), while ridge regression yielded the most accurate friction factor surrogate (OOF RMSE = 4.70×10⁻⁴, R² = 0.9886, MAPE = 1.12%). The combined computational fluid dynamics and machine-learning framework enables fast, reliable evaluation of solar air heater thermo-hydraulic performance for configuration screening and optimisation within the validated operating envelope.