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A numerical study on the feasibility of electricity production using an optimized wind delivery system (Invelox) integrated with a Horizontal axis wind turbine (HAWT)

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  • Ghorani, Mohammad Mahdi
  • Karimi, Behrooz
  • Mirghavami, Seyed Mohammad
  • Saboohi, Zoheir

Abstract

For the first time, the geometry of the Invelox wind delivery system is optimized in this paper using a multi-objective surrogate-based optimization method. The feasibility of electricity production by installing a Horizontal Axis Wind Turbine (HAWT) inside the throat of the optimal Invelox is also investigated using 3D CFD simulations. The Advanced Latin Hypercube Sampling (ALHS) method is used to construct the sample space after selecting geometrical design variables. Then, the objective functions at the training points are calculated by the Computational Fluid Dynamics (CFD) models. Kriging (KRG) surrogate models have been fitted to the outputs of sample space and coupled with the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to obtain the optimal Invelox geometry. According to the results, the net mass flow rate of air entering the Invelox and wind power at the Venturi's throat have increased by 64.7% and 279.9%, respectively. The third optimization target has also been satisfied by eliminating backflow at the diffuser outlet. Comparing the primary and optimal Invelox reveals that the wind speed ratio has risen from 1.6 to 2.4. Lastly, a HAWT has been designed for the optimal Invelox, and its performance in both ducted and bare modes has been evaluated. It has been found that installing a turbine inside the throat, increases the pressure drop across the ducting system, adversely affecting the overall performance of the Invelox and wind turbine.

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  • Ghorani, Mohammad Mahdi & Karimi, Behrooz & Mirghavami, Seyed Mohammad & Saboohi, Zoheir, 2023. "A numerical study on the feasibility of electricity production using an optimized wind delivery system (Invelox) integrated with a Horizontal axis wind turbine (HAWT)," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000373
    DOI: 10.1016/j.energy.2023.126643
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    References listed on IDEAS

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    Cited by:

    1. Ngwarai Shambira & Golden Makaka & Patrick Mukumba, 2024. "Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    2. Patricio A. Corbalán & Luciano E. Chiang, 2024. "Fast Power Coefficient vs. Tip–Speed Ratio Curves for Small Wind Turbines with Single-Variable Measurements following a Single Test Run," Energies, MDPI, vol. 17(5), pages 1-23, March.
    3. Ashkan Safari & Hamed Kheirandish Gharehbagh & Morteza Nazari Heris, 2023. "DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm," Energies, MDPI, vol. 16(19), pages 1-22, September.
    4. Ainura Dyusembaeva & Nazgul Tanasheva & Ardak Tussypbayeva & Asem Bakhtybekova & Zhibek Kutumova & Sholpan Kyzdarbekova & Almat Mukhamedrakhim, 2024. "Numerical Simulation to Investigate the Effect of Adding a Fixed Blade to a Magnus Wind Turbine," Energies, MDPI, vol. 17(16), pages 1-18, August.

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