Syawitri, T.P., Yao, Y.F., Chandra, B. and Yao, J. (2021) Comparison study of URANS and hybrid RANS-LES models on predicting vertical axis wind turbine performance at low, medium and high tip speed ratio ranges. Renewable Energy, 168 . pp. 247-269. ISSN 0960-1481
Full content URL: https://doi.org/10.1016/j.renene.2020.12.045
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Item Type: | Article |
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Item Status: | Live Archive |
Abstract
Comparison study of unsteady Reynolds-averaged Navier-Stokes (URANS) and hybrid RANS-LES models is carried out for predicting the performance of three-straight-bladed vertical axis wind turbine operating within tip speed ratios (TSRs) ranges of (1.44–3.3). The evaluation is focused on power coefficient, moment coefficient and vortex structure generation, growth and transportation predictions. It was found that URANS turbulence modelling is sufficient for averaged power coefficient prediction and specific range of TSR evaluation, and it also benefits from short simulation run time. To further evaluate flow field details and to understand the underlying flow physics such as dynamic stall behaviour, hybrid RANS-LES turbulence modelling is necessary. Comparing between hybrid models adopted, stress-blended eddy simulation based on transition shear-stress transport (TSST) model has achieved an overall better performance, such as a reduction of simulation discrepancy by 50% in low TSRs range compared to TSST turbulence model. In both medium and high TSRs ranges, the modelling discrepancies are less than 3% compared to TSST turbulence models at about 25% extra computational time. Furthermore, in high TSRs range, hybrid RANS-LES models are able to predict the appearance of vortex shedding at high azimuthal angles (θ ≥ 180°) for which URANS models failed to capture.
Keywords: | Vertical axis wind turbine, Computational fluid dynamics, Turbulence model assessment, Hybrid RANS-LES, Stress-blended eddy simulation |
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Subjects: | H Engineering > H141 Fluid Mechanics H Engineering > H221 Energy Resources J Technologies > J910 Energy Technologies H Engineering > H321 Turbine Technology |
Divisions: | College of Science > School of Engineering |
ID Code: | 43523 |
Deposited On: | 12 Jan 2021 10:28 |
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