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Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach
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  • Published: 02 March 2026

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach

  • Piyush Kumar Jain1,
  • Kawal Lal Kurrey2,
  • Vikas Pandey3,
  • Jitesh R. Shinde4,
  • Abhishek Narayan Tripathi5 &
  • …
  • Niraj Kumar Dewangan6 

Scientific Reports , Article number:  (2026) Cite this article

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Subjects

  • Energy science and technology
  • Engineering
  • Mathematics and computing
  • Physics

Abstract

The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Flat Plate Solar Air Heater (FPSAH) is extensively utilized in many applications requiring reasonable heat but struggles from inherent limitation in convective heat release and mediocre efficiency. In this study, the challenges are addressed with a novel means of dual mode augmented technique. This mode integrated absorber surface of the FPSAH system by introducing two radiation reflectors on either edge of the rectangular channel. Primarily these reflectors forward back the solar irradiance over the absorber plate, thus increasing the actual solar flux. Simultaneously, a W-shaped artificial rib roughness pattern is merged on the underneath (air-side) of the absorber plate. This coarseness is intended to persuade measured flow disorder inside the channel that disrupt the boundary layer development and may consequently augment convective heat transfer. Experimental testing is conducted with different combinations of roughened absorber surface and radiation reflectors. The performance enhancement is evaluated in terms of Nusselt number (Nu) and thermal efficiency of the FPSAH system. The maximum Nu achieved is 1.63 times higher using a set of radiation reflectors along with W-shaped roughness on the absorber surface compared to the plain configuration without radiation reflector. Finally, artificial neural network (ANN) and machine learning (ML) algorithms were used to predict Reynolds number in each set of experiments. A very good curve fitting was achieved by the Robust Regression algorithm with \(R^2 = 0.99\) for the testing dataset and \(R^2 = 0.94\) by the Random Forest Regression algorithm for ML.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

\(A_p\) :

Area of heated plate (m\(^2\))

\(A_o\) :

Cross-sectional area of orifice plate (m\(^2\))

\(C_p\) :

Specific heat of fluid (kJ kg\(^{-1}\) K\(^{-1}\))

\(C_d\) :

Orifice discharge coefficient

\(D_h\) :

Hydraulic diameter of rectangular duct (m)

p :

Roughness height (mm)

H :

Rectangular duct height (mm)

\(h_t\) :

Convective heat transfer coefficient (W m\(^{-2}\) K\(^{-1}\))

k :

Thermal conductivity of flowing air (W m\(^{-1}\) K\(^{-1}\))

L :

Absorber plate length (mm)

\(\dot{m}\) :

Mass flow rate of air (kg s\(^{-1}\))

q :

Roughness pitch (mm)

\(Q_u\) :

Useful heat gain (W)

\(T_f\) :

Mean air temperature (K)

\(T_i\) :

Inlet air temperature (K)

\(T_o\) :

Outlet air temperature (K)

\(T_p\) :

Average heated plate temperature (K)

V :

Air velocity (m s\(^{-1}\))

W :

Duct width (mm)

\(h/D_h\) :

Relative roughness height

Nu:

Nusselt number (rough plate)

Nu\(_s\) :

Nusselt number (smooth plate)

q/h :

Relative roughness pitch

Pr:

Prandtl number

Re:

Reynolds number

W/H :

Aspect ratio

\(\alpha\) :

Angle of attack (degree)

\(\rho _{\text {air}}\) :

Density of air (kg m\(^{-3}\))

\(\nu\) :

Kinematic viscosity (m\(^2\) s\(^{-1}\))

\(\eta _{th}\) :

Thermal efficiency

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.

Funding

Open access funding provided by Manipal Academy of Higher Education, Manipal. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Bansal Institute of Science & Technology, Bhopal, India

    Piyush Kumar Jain

  2. Department of Industrial and Production Engineering, School of Studies of Engineering & Technology, Bilaspur, India

    Kawal Lal Kurrey

  3. Department of Electrical Engineering, Babu Banarasi Das University, Lucknow, Uttar Pradesh, 226028, India

    Vikas Pandey

  4. Department of Electronics Engineering (VLSI Design & Technology), CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar (Aurangabad), Maharashtra, India

    Jitesh R. Shinde

  5. School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Abhishek Narayan Tripathi

  6. Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

    Niraj Kumar Dewangan

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  1. Piyush Kumar Jain
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  2. Kawal Lal Kurrey
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Contributions

P. K. Jain led the research work, conceptualized the study, and coordinated the overall project development. K. L. Kurrey contributed to the methodology design, simulation setup, and data analysis. V. Pandey carried out performance evaluation, experimental validation, and assisted in result interpretation. J. R. Shinde supported algorithm/model development and contributed to system implementation. A. N. Tripathi contributed to conceptualization, manuscript preparation, and formatting according to journal guidelines. N. K. Dewangan provided supervision, technical guidance, methodology refinement, and final manuscript review and approval. All authors reviewed and approved the final version of the manuscript.

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Correspondence to Niraj Kumar Dewangan.

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Jain, P.K., Kurrey, K.L., Pandey, V. et al. Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41922-4

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  • Received: 29 December 2025

  • Accepted: 23 February 2026

  • Published: 02 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-41922-4

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Keywords

  • Solar air heater
  • Radiation reflectors
  • W-shaped roughness
  • Artificial neural network
  • Machine learning
  • Thermal efficiency
  • Nusselt number
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