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OFFSHORE WIND ACCELERATOR: WAKE MODELLING USING CFD

OFFSHORE WIND ACCELERATOR: WAKE MODELLING USING CFD. C. Montavon, ANSYS UK S.-Y. Hui, Dong Energy J. Graham, RWE Npower Renewables D. Malins, Scottish Power Renewables P. Housley, SSE Renewables E. Dahl, Statoil P. de Villiers, The Carbon Trust B. Gribben, Frazer-Nash Consultancy.

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OFFSHORE WIND ACCELERATOR: WAKE MODELLING USING CFD

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  1. OFFSHORE WIND ACCELERATOR: WAKE MODELLING USING CFD C. Montavon, ANSYS UK S.-Y. Hui, Dong Energy J. Graham, RWE Npower Renewables D. Malins, Scottish Power Renewables P. Housley, SSE Renewables E. Dahl, Statoil P. de Villiers, The Carbon Trust B. Gribben, Frazer-Nash Consultancy

  2. Contents • Offshore Wind Accelerator Stage I • Wake modelling in ANSYS CFD • Results from blind simulations for Horns Rev and North Hoyle • Sensitivity • Turbulence model assumptions • Current understanding of limitations • Other turbine spacing/single wake analyses

  3. Offshore Wind Accelerator is a collaboration to reduce costs • Objective: Reduce cost of energy by 10% through collaborative RD&D • Initially 5 developers + Carbon Trust • 46% of licensed capacity in UK waters (~22GW) • Launched Oct 2008, 1.5 year commitment • Budget of £1.5m • Focusing on technologies for • Round 2 extensions • Round 3 • Scottish Territorial Waters • This work was carried out under Stage I • Stage II is now underway • Three more developers • Commitment to 2014 • Much larger budget 60% (30GW) of licensed capacity in UK waters

  4. OWA focuses on strengthening economics of offshore wind Stage 1 (Oct ’08 to Apr ’10) examined 4 research areas Offshore wind returns CAPEX OPEX Yield Financing costs Foundations Access Electrical systems Wake effects Four technology areas, selected on basis of detailed analysis of over 70 technical barriers

  5. Accuracy of models benchmarked vs actual data • “Case studies” formed basis of benchmarking • Under-prediction of wake effects in many scenarios • The results enabled OWA to commission specific improvements to three packages and to develop one entirely new model • Sophistication of engineering and CFD models has been increased to add greater realism and increase accuracy of predictions ANSYS CFD

  6. Simple wake model in ANSYS CFD • Wind turbine orientation parallel to wind direction at inlet • Use mesh adaption during solution to resolve rotor disk • Wind turbine represented by momentum sink (constant thrust per volume at disk location) • Upstream wind speed in momentum sink obtained from actuator disk model and simulated wind speed at disk • Validated for single wake cases (Vindeby, Nibe) and onshore (Blacklaw) 1 1. C. Montavon, I. Jones, C. Staples, C. Strachan, I. Gutierrez, 2009, Practical issues in the use of CFD for modelling wind farms, http://www.ewec2009proceedings.info/allfiles2/70_EWEC2009presentation.pdf

  7. 20 km Horns Rev Wind farm characteristics • 8x10 WT • Diameter of 80m • Hub height of 70m • Wind turbine spacing: 7 diameters • Domain size: • 10 km radius • 1.0 km height • Thrust curve: Vestas V80 • ABL boundary layer profiles at inlet (Richards and Hoxey)

  8. Results at hub height Uref = 10 m/s at 70m, z0 = 0.0001m, upstream TI = 6% Wind direction: sector 285 Horizontal velocity Turbulence intensity

  9. Horns RevNormalised power down a row • Simulations by step of 1 degree, sector 270 – 285, averaged for three different bin sizes. • Reasonably good prediction • Tendency for over-estimation of array losses • Good prediction of slope down the row • Consistent for various bin sizes Upwind data from “Wake Measurements Used in the Model Evaluation”. K.S. Hansen, R. Barthelmie, D. Cabezon and E. Politis. Upwind Wp8: Flow; Deliverable D8.1 Data. 18 June 2008.

  10. North Hoyle • Same setup as Horns Rev, for different layout (6x5 array). • Wind direction 260 • Reference wind speed of 10m/s at hub height (67m) • Upstream TI of 7% • Vestas V80 • Wind turbine spacing: • 4.4 D in 350 degree direction • 10 D in 260 degree direction

  11. North HoyleNormalised power down a row Uref = 10 m/s at 67m, z0 = 0.0001m, upstream TI = 7% Wind direction: sector 260 • Very good agreement with power data for both bin sizes • Absolutely blind test case!

  12. Typical convergence/resource requirements • Based on Horns Rev • 1.4 M Nodes in final mesh • 42 mins/run, start to finish, including I/O, partitioning and adaption • 16 cores, Intel Xeon (2 dual processor quad core systems, 16 MBytes/system • Typically 60 iterations on final adaption step for convergence, 110 in total • Very tight convergence criterion, rms residuals < 1E-6 (1E-5 would reduce iterations to 47) • Total time less than 12 hrs start to finish for 15 simulations

  13. So far… • Good prediction of array efficiency rsp. normalised power down a row for Horns Rev (7D spacing) and North Hoyle (10D spacing) • How robust are the results to changes in • mesh resolution • turbulence model setup • turbine spacing  Details in paper !

  14. Cases presented • K-e turbulence model • Uref = 10 m/s at 70m • Variation on turbulence setup • Case A – Reference model, using high roughness value to provide required turbulence intensity at inlet • Case B – Modified Cμ to provide required inlet TI while using a roughness value more appropriate for sea • Case C – As Case B with modified turbulence decay rate 1 • 1. Rados et al, CFD modelling issues of wind turbine wakes under stable atmospheric conditions, http://www.ewec2009proceedings.info/allfiles2/564_EWEC2009presentation.pdf

  15. Array efficiency: comparing cases A, B and C • Case A and C showing best agreement with production data1 • Strong effect of change of turbulence model constants • Are these changes in the model constants required because of incomplete representation of the physics of the atmosphere? • Stability conditions? • Large scale transient? • Interpretation of tke? •  more validation required to justify choice of constants • Barthelmie et al , Modelling the impact of wakes on power output at Nysted and Horns Rev, http://www.ewec2009proceedings.info/allfiles2/301_EWEC2009presentation.pdf.

  16. Nibe - single wake Normalised wind speed at 2.5D, 4.0D, 7.5D k-e SST k-e : good at distances > 6D, over optimistic at smaller distances SST: excellent at distances ~3D, over pessimistic at larger distances

  17. Conclusions • Simple actuator disk model and associated framework based on ANSYS CFD • WindModeller • Provided good (or encouraging) results for array efficiency and power down row of turbine from blind tests of Horns Rev and North Hoyle. (spacing of 7D and 10 D) • Results sensitive to turbulence model assumptions • Affordable tool for detailed final wind farm layout analysis • Potential for new insights into 3D effects afforded by this approach is clear

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