TY - CONF
TI - Tidal Turbine Benchmarking Project: Stage I – Steady Flow Blind Predictions Preprint
AU - Wilden, R
AU - Chen, S
AU - Harvey, S
AU - Edwards, H
AU - Vogel, C
AU - Bhavsar, K
AU - Allsop, T
AU - Gilbert, J
AU - Mullings, H
AU - Ghobrial, M
AU - Ouro, P
AU - Apsley, D
AU - Stallard, T
AU - Benson, I
AU - Young, A
AU - Schmitt, P
AU - Zilic de Arcos, F
AU - Dufour, M
AU - Bex, C
AU - Pinon, G
AU - Evans, I
AU - Togneri, M
AU - Masters, I
AU - Ignacio, L
AU - Duarte, C
AU - Souza, F
AU - Gambuzza, S
AU - Liu, Y
AU - Viola, I
AU - Rentschler, M
AU - Gomes, T
AU - Vaz, G
AU - Azcueta, R
AU - Ward, H
AU - Salvatore, F
AU - Sarichloo, Z
AU - Calcagni, D
AU - Tran, T
AU - Ross, H
AU - Oliveira, M
AU - Puraca, R
AU - Carmo, B
T2 - 15th European Wave and Tidal Energy Conference (EWTEC 2023)
AB - This paper presents the first blind prediction stage of the Tidal Turbine Benchmarking Project conducted and funded by the UK’s EPSRC and Supergen ORE Hub. In this first stage, only steady flow conditions, at low and elevated turbulence levels (3.1%), were considered. Prior to the blind prediction stage, a large laboratory-scale experiment was conducted in which a highly instrumented 1.6m diameter tidal rotor was towed through a large towing tank in well-defined flow conditions with and without an upstream turbulence grid.Details of the test campaign and rotor design were released as part of this community blind prediction exercise. Participants were invited to simulate turbine performance and loads using appropriate methods. 26 submissions were received from 12 groups across academia and industry using techniques ranging from blade resolved Computational Fluid Dynamics through Actuator Line, Boundary Integral Equation Model, Vortex methods to engineering Blade Element Momentum methods.The comparisons between experiments and blind predictions were very positive, not only helping to provide validation and uncertainty estimates for the models, but also validating the experimental tests themselves. The exercise demonstrated that the experimental turbine data provides a robust dataset against which researchers and engineers can test their models and implementations, helping to reduce uncertainty and provide increased confidence in engineering processes, as well as a basis against which modellers can evaluate and refine approaches.
DA - 2023/09//
PY - 2023
SP - 13
UR - https://www.nrel.gov/docs/fy23osti/86715.pdf
LA - English
KW - Current
KW - Axial Flow Turbine
KW - Lab Data
KW - Performance
ER -