Fast power output prediction for a single row of ducted cross-flow water turbines using a BEM-RANS approach. (April 2016)

• Record Type: Journal Article
• Authors: Dominguez, Favio; Achard, Jean-Luc; Zanette, Jerônimo; Corre, Christophe
• Is Part Of: Renewable energy. Volume 89(2016)
• Page Start: 658
• Page End: 670
• Publication Date: 2016-04
• Subjects: Cross flow ducted hydrokinetic turbine -- Numerical simulation -- Blade element momentum -- Array layout power prediction; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.12.042
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A numerical model is proposed to efficiently compute the power produced by a row of Vertical Axis Water Turbines (VAWTs) deployed in parallel for various water flow conditions. As the computational cost of the unsteady Reynolds Averaged Navier Stokes (URANS) approach is high, a coupled Blade Element Momentum (BEM)/Reynolds Averaged Navier–Stokes (RANS) approach is developed, restricted to a 2D approximation. The HARVEST hydrokinetic devices considered in this study are made of twin contra-rotating VAWTs of ducted H -Darrieus type rotors. Momentum source terms are derived for such rotors from URANS simulations taking into account the presence of fairings and also incorporating the optimal tip speed ratio (TSR), thanks to a procedure based on local flow conditions upstream of the rotor path and on the mass flow through each rotor. Highlights: A novel BEM-RANS method is introduced for a vertical axis water turbine (VAWT). Blade source terms are defined from a database of precursor URANS simulations. BEM-RANS hydrodynamic curves are specifically designed for VAWT turbines working at their optimal tip speed ratio. The power output of a set of turbines can be estimated using a single BEM-RANS calculation. The cost reduction offered by the BEM-RANS method gives access to the analysis of arrays including a large number of VAWTs.