CFD analysis of dynamic stall on vertical axis wind turbines using Scale-Adaptive Simulation (SAS): comparison against URANS and hybrid RANS/LES

Abdolrahim Rezaeiha (Corresponding author), Hamid Montazeri, Bert Blocken

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96 Citaten (Scopus)
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Samenvatting

The Scale-Adaptive Simulation (SAS) approach has emerged as an improved unsteady Reynolds-Averaged Navier-Stokes (URANS) formulation to bridge the gap between the less accurate commonly used URANS and the computationally expensive hybrid RANS/LES for highly separated unsteady flows, e.g. dynamic stall. However, while the SAS has been successfully used at several occasions, it has not yet been tested for the complex case of dynamic stall. Therefore, the present study analyzes the SAS predictions of dynamic stall on a vertical axis wind turbine at a chord Reynolds number of 5 × 104 and a reduced frequency of 0.125. The analysis is based on comparison of the SAS predictions of the blade aerodynamics and the turbine power performance against the corresponding URANS and hybrid RANS/LES predictions. The results show that the SAS predictions are closer to hybrid RANS/LES than URANS with respect to: (i) the instant of the bursting of the laminar separation bubble (LSB), the leading-edge suction collapse, the formation of the dynamic stall vortex (DSV) and the trailing-edge vortex (TEV) and the shedding of the TEV; (ii) the size and strength of the TEV; (iii) the DSV-TEV interaction; (iv) the drag prediction during the downstroke. On the other hand, both URANS and SAS fail to corroborate with hybrid RANS/LES with respect to: (i) the instant of the formation of the LSB and the shedding of the DSV (the stall angle); (ii) the drag jump at the stall angle; (iii) the lift values during the downstroke; and (iv) the chordwise extent of the LSB.
Originele taal-2Engels
Pagina's (van-tot)1282-1298
Aantal pagina's17
TijdschriftEnergy Conversion and Management
Volume196
DOI's
StatusGepubliceerd - 5 jul. 2019

Financiering

The authors would like to acknowledge support from the European Commission's Framework Program Horizon 2020, through the Marie Curie Innovative Training Network (ITN) AEOLUS4FUTURE - Efficient harvesting of the wind energy (H2020-MSCA-ITN-2014: Grant agreement no. 643167) and the TU1304 COST ACTION “WINERCOST”. The authors gratefully acknowledge the partnership with ANSYS CFD. This work was sponsored by NWO Exacte Wetenschappen (Physical Sciences) for the use of supercomputer facilities, with financial support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organization for Scientific Research, NWO). The 2nd author is currently a postdoctoral fellow of the Research Foundation – Flanders ( FWO ) and is grateful for its financial support (project FWO 12M5319N ).

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