Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions


  • R.H.J. Willden University of Oxford
  • Xiaosheng Chen University of Oxford
  • S.W. Tucker Harvey
  • H. Edwards
  • C.R. Vogel University of Oxford
  • K. Bhavsar
  • T. Allsop
  • J. Gilbert
  • H. Mullings
  • M. Ghobrial
  • P. Ouro
  • D. Apsley
  • T. Stallard
  • I. Benson
  • A. Young
  • P. Schmitt
  • F. Zilic de Arcos
  • M.-A. Dufour
  • C. Choma Bex
  • G. Pinon
  • A.I. Evans
  • M. Togneri
  • I. Masters
  • L.H. da Silva Ignacio
  • C.A.R. Duarte
  • F.J. Souza
  • S. Gambuzza
  • Y. Liu
  • I.M. Viola
  • M. Rentschler
  • T. Gomes
  • G. Vaz
  • R. Azcueta
  • H. Ward
  • F. Salvatore
  • Z. Sarichloo
  • D. Calcagni
  • T.T. Tran
  • H. Ross
  • M. Oliveira
  • R. Puraca
  • B.S. Carmo




tidal turbine, CFD, benchmarking test


This paper presents the first blind prediction stage of the Tidal Turbine Benchmarking Project being 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 (3.1%) levels, 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 use a range of engineering modelling approaches to simulate the performance and loads of the turbine. 26 submissions were received from 12 groups from across academia and industry using solution techniques ranging from blade resolved computational fluid dynamics through actuator line, boundary integral element methods, vortex methods to engineering Blade Element Momentum methods.

The comparisons between experiments and blind predictions were extremely positive 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 data set against which researchers and design engineers can test their models and implementations to ensure robustness in their processes, helping to reduce uncertainty and provide increased confidence in engineering processes. Furthermore, the data set provides the basis by which modellers can evaluate and refine approaches.



How to Cite

R. Willden, “Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions”, Proc. EWTEC, vol. 15, Sep. 2023.