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Computational parametric analysis of the design of cross-flow turbines under constraints

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  • Leguizamón, Sebastián
  • Avellan, François

Abstract

The cross-flow turbine is an attractive technology for small-scale hydropower generation thanks to its low capital cost and relatively high efficiency even under partial discharge operating conditions. It has been proposed that this kind of turbine can be manufactured from standard steel pipe sections given its simple geometry, an alternative that would further decrease the turbine’s capital cost compared to highly engineered cross-flow designs. This article explores the trade-offs encountered during the design of cross-flow turbines under the constrains imposed by the discrete set of dimensions available for commercial steel pipes. First, the computational model is presented, analyzed in terms of its convergence behavior, and validated with experimental data. Then, a parametric analysis is performed to understand the relative importance of the design variables and their optimum value in regard to the turbine efficiency. Based on the design guidelines derived from the parametric analysis, an example cross-flow turbine design is presented and thoroughly characterized, demonstrating that it is possible to engineer a cross-flow turbine of competitive efficiency out of commercial steel pipes. These guidelines may encourage the use of cross-flow turbines as an appropriate technology for off-grid regions.

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  • Leguizamón, Sebastián & Avellan, François, 2020. "Computational parametric analysis of the design of cross-flow turbines under constraints," Renewable Energy, Elsevier, vol. 159(C), pages 300-311.
    • Handle: RePEc:eee:renene:v:159:y:2020:i:c:p:300-311
    DOI: 10.1016/j.renene.2020.03.187
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    References listed on IDEAS

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    1. Sen, Rohit & Bhattacharyya, Subhes C., 2014. "Off-grid electricity generation with renewable energy technologies in India: An application of HOMER," Renewable Energy, Elsevier, vol. 62(C), pages 388-398.
    2. Delgado, J. & Ferreira, J.P. & Covas, D.I.C. & Avellan, F., 2019. "Variable speed operation of centrifugal pumps running as turbines. Experimental investigation," Renewable Energy, Elsevier, vol. 142(C), pages 437-450.
    3. Vincenzo Sammartano & Costanza Aricò & Armando Carravetta & Oreste Fecarotta & Tullio Tucciarelli, 2013. "Banki-Michell Optimal Design by Computational Fluid Dynamics Testing and Hydrodynamic Analysis," Energies, MDPI, vol. 6(5), pages 1-24, April.
    4. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
    5. Tan, Xu & Engeda, Abraham, 2016. "Performance of centrifugal pumps running in reverse as turbine: Part Ⅱ- systematic specific speed and specific diameter based performance prediction," Renewable Energy, Elsevier, vol. 99(C), pages 188-197.
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    Cited by:

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    2. Hashem, Islam & Zhu, Baoshan, 2021. "Metamodeling-based parametric optimization of a bio-inspired Savonius-type hydrokinetic turbine," Renewable Energy, Elsevier, vol. 180(C), pages 560-576.
    3. Krzysztof Kołodziejczyk & Radosław Ptak, 2022. "Numerical Investigations of the Vertical Axis Wind Turbine with Guide Vane," Energies, MDPI, vol. 15(22), pages 1-14, November.
    4. Mehr, Goodarz & Durali, Mohammad & Khakrand, Mohammad Hadi & Hoghooghi, Hadi, 2021. "A novel design and performance optimization methodology for hydraulic Cross-Flow turbines using successive numerical simulations," Renewable Energy, Elsevier, vol. 169(C), pages 1402-1421.

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