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Parametric sensitivity analysis and optimisation of a solar air heater with multiple rows of longitudinal vortex generators

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  • Dezan, Daniel J.
  • Rocha, André D.
  • Ferreira, Wallace G.

Abstract

This current research is focused on sensitivity analysis and surrogate-based optimization of solar air heater channel-types with three rows delta winglet pairs arranged in both Common-Flow-Down and Common-Flow-Up orientations at Reynolds numbers of 5,000 and 10,000. The performance of the channel is investigated by considering the variation of nine input parameters related to chord, height and angle of attack of each row of delta winglet pairs, in which the rows of the vortex generators are independent from each other. Sensitivity analysis is carried out by means of Morris, FAST and PAWN indices. From sensitivity analysis, it is highlighted that the friction factor is much more sensitive to the design variables than Nusselt number. The optimization procedure consists on the combination of the Anisotropic Kriging model and Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimized solutions indicated the highest performances of the channel are achieved when the vortex generators are not periodically distributed along the channel. With regard to net power ratio, Common-Flow-Up array performs better than Common-Flow-Down arrays, and further enhancement of this parameter is noted when the Reynolds number is increased. The main vortices generated by delta winglet pairs at Re = 10,000 propagate downstream closer to the absorber plate, and the corner vortices are weak and rapidly dissipated along the main flow direction, for both Common-Flow-Up and Common-Flow-Down arrangements.

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  • 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).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920300684
    DOI: 10.1016/j.apenergy.2020.114556
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    3. Ravanbakhsh, Mohammad & Gholizadeh, Mohammad & Rezapour, Mojtaba, 2023. "3E thermodynamic modeling and optimization a novel of ARS-CPVT with the effect of inserting a turbulator in the solar collector," Renewable Energy, Elsevier, vol. 209(C), pages 591-607.
    4. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    5. Hu, Mingke & Zhao, Bin & Suhendri, & Cao, Jingyu & Wang, Qiliang & Riffat, Saffa & Su, Yuehong & Pei, Gang, 2022. "Extending the operation of a solar air collector to night-time by integrating radiative sky cooling: A comparative experimental study," Energy, Elsevier, vol. 251(C).
    6. Suvanjan Bhattacharyya & Debraj Sarkar & Rahul Roy & Shramona Chakraborty & Varun Goel & Eydhah Almatrafi, 2021. "Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    7. Goel, Varun & Kumar, Rajneesh & Bhattacharyya, Suvanjan & Tyagi, V.V. & Abusorrah, Abdullah M., 2021. "A comprehensive parametric investigation of hemispherical cavities on thermal performance and flow-dynamics in the triangular-duct solar-assisted air-heater," Renewable Energy, Elsevier, vol. 173(C), pages 896-912.
    8. 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.

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