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Deep learning-based prediction of thermal-hydraulic and exergy performance in a novel bidirectional wavy absorber solar air heater

Author

Listed:
  • Li, Dejuan
  • Bai, Zhiqing
  • Samad, Sarminah
  • Abed, Azher M.
  • Li, Yujie
  • Alhumaid, Saleh
  • Dutta, Ashit Kumar
  • Alkhalaf, Salem
  • Khan, Mohammad Nadeem
  • Ali, H. Elhosiny

Abstract

The present work investigates the thermal and exergy performance of a novel bidirectional wavy absorber solar air heater (BWA-SAH) with sinusoidal corrugations in both the transverse and longitudinal directions to enhance heat transfer through increased surface area and turbulence. As compared to conventional studies on unidirectional wavy geometries, the present study extensively examines the impact of significant geometric parameters longitudinal and transversal wavelengths ((Wll/dh), (Wlt/dh), wave amplitudes (Awl/dh), (Awt/dh), and Reynolds number on performance parameters such as Nu number, friction factor, thermohydraulic performance parameter (THPP), and exergy efficiency. A complete numerical investigation is conducted with the assistance of CFD simulations confirmed and accelerated by a trained deep neural network (DNN) model of 243 simulations. The DNN delivers R2 > 0.98 for all performance measures and enables fast prediction with notable speedup compared to CFD simulations. Key findings reveal that smaller longitudinal wavelengths (Wll/dh = 1.35) enhance heat transfer (Nu increases by ∼35 %) but significantly enhance pressure drop (f increases by ∼84 %). Similarly, higher longitudinal amplitudes (Awl/dh = 0.270) raise Nu by ∼20 % but (f by ∼100 %, highlighting a significant trade-off. A higher Reynolds number enhances heat transfer (Nu increases by 108 % as Re rises from 5000 to 15,000), but it reduces the THPP by approximately 11 % and exergy efficiency by about 53 % due to increased irreversibilities. The study confirms that intermediate longitudinal wavelengths (Wll/dh = 2.25) and amplitudes (Awl/dh = 0.16) optimize THPP and exergy efficiency, and low Re (<10,000) minimizes pressure drop penalties. Coupling CFD simulations and DNN modeling provides a robust platform for rapid design optimization, significantly advancing the design of energy-efficient and sustainable solar air heaters. This research paves the way for more affordable and eco-friendly solar thermal systems, further driving global efforts in renewable energy deployment.

Suggested Citation

  • Li, Dejuan & Bai, Zhiqing & Samad, Sarminah & Abed, Azher M. & Li, Yujie & Alhumaid, Saleh & Dutta, Ashit Kumar & Alkhalaf, Salem & Khan, Mohammad Nadeem & Ali, H. Elhosiny, 2026. "Deep learning-based prediction of thermal-hydraulic and exergy performance in a novel bidirectional wavy absorber solar air heater," Renewable Energy, Elsevier, vol. 256(PE).
  • Handle: RePEc:eee:renene:v:256:y:2026:i:pe:s0960148125018567
    DOI: 10.1016/j.renene.2025.124192
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    References listed on IDEAS

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