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Application of Response Surface Methodology and Artificial Neural Network to Optimize the Curved Trapezoidal Winglet Geometry for Enhancing the Performance of a Fin-and-Tube Heat Exchanger

Author

Listed:
  • Rishikesh Sharma

    (Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India)

  • Dipti Prasad Mishra

    (Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India)

  • Marek Wasilewski

    (Faculty of Production Engineering and Logistics, Opole University of Technology, 76 Proszkowska St., 45-758 Opole, Poland)

  • Lakhbir Singh Brar

    (Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India)

Abstract

The present work aims at optimizing the geometry of curved trapezoidal winglets to enhance heat transfer rates (expressed as Colburn factor, j ) and minimize pressure losses (expressed as friction factor, f ). A fin-and-tube heat exchanger was analyzed with winglets mounted on the alternate tube and on either side of the fins. Multi-objective optimization was performed using the genetic algorithm (GA) to maximize j and minimize f . Two surrogate models, viz. response surface methodology (RSM) and artificial neural network (ANN), were considered as inputs to GA. To reduce the number of runs, a sensitivity analysis was first performed to select the most influential geometrical parameters for optimization. The values of j and f in the design of the experiments table were computed using CFD. The Pareto front points elucidated a significant improvement compared with the reference model along with a broad choice for the designers, not only for the design condition but also for the off-design inlet condition.

Suggested Citation

  • Rishikesh Sharma & Dipti Prasad Mishra & Marek Wasilewski & Lakhbir Singh Brar, 2023. "Application of Response Surface Methodology and Artificial Neural Network to Optimize the Curved Trapezoidal Winglet Geometry for Enhancing the Performance of a Fin-and-Tube Heat Exchanger," Energies, MDPI, vol. 16(10), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4209-:d:1151528
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    References listed on IDEAS

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    1. Lei Chai & Savvas A. Tassou, 2018. "A Review of Airside Heat Transfer Augmentation with Vortex Generators on Heat Transfer Surface," Energies, MDPI, vol. 11(10), pages 1-45, October.
    2. Gholap, A.K. & Khan, J.A., 2007. "Design and multi-objective optimization of heat exchangers for refrigerators," Applied Energy, Elsevier, vol. 84(12), pages 1226-1239, December.
    3. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
    4. Dezan, Daniel J. & Rocha, André D. & Ferreira, Wallace G., 2020. "Parametric sensitivity analysis and optimisation of a solar air heater with multiple rows of longitudinal vortex generators," Applied Energy, Elsevier, vol. 263(C).
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    Cited by:

    1. Grzegorz Ligus & Barbara Wasilewska, 2023. "Maldistribution of a Thermal Fluid along the U-Tube with a Different Bending Radius—CFD and PIV Investigation," Energies, MDPI, vol. 16(15), pages 1-18, July.
    2. Caihang Liang & Rui Zhang & Chaojian Mao & Yanfang Dong & Xiong Yao & Weipeng Hu & Zhenxing Li, 2024. "Design and Optimization of Cross-Corrugated Triangular Ducts with Trapezoidal Baffles Based on Response Surface Methodology and CFD," Energies, MDPI, vol. 17(10), pages 1-23, May.

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