Stability classification for CFD simulations in complex terrain

1. Stability classification for CFD simulations in complex terrain Dr. Carolin Schmitta Dr. Cathérine Meiβnerb Andrea Vignarolib ajuwi Wind GmbH, Energie-Allee 1, 55286 Wörrstadt, Germany b WindSim AS, Fjordgaten 15, N-3125 Tønsberg, Norway EWEA 2013 Annual Event, 4-7th February 2013, Vienna

2. Outline • General Aspects of Atmospheric Stability • Effects of Stability on Windfields in Theory and Measurements • Stability Parameters in Measurement and MERRA Data • Examples from different Sites • Benefits for CFD Modelling • Conclusions und Outlook

3. General AspectsofAtmosphericStability • AtmosphericStability • The resistance of the atmosphere to vertical motion is dependant on the different stratification parameters. • ApplicationandImportancefor Wind Energy • Modification of wind profile, shear and turbulence up to greater hub heights and flow circulation in complex terrain influence energy yield and also power curve performance. Simulations don’t fit measurements • Integration in CFD Modelling • Historically elevation and roughness has been adjusted to compensate for incomplete models but often stability is the real issue.

4. EffectsofStability on Windfields • Vertical Windprofiles Theoretical Measurement

5. EffectsofStability on Windfields Neutral Stable • Horizontal Effects Streamlines in CFD Modelling Neutral Stable Vertical Speed in CFD Modelling Model Theoretical

6. Challenges for Wind Energy Applications • Equations/Meteorological relations are known, but: • How is reality and how can this be captured by measurement and model methods? • What influences have to be taken into account and what data and parameters are necessary to reproduce profiles more accurate? • Atmospheric Stability can be determined by various methods, depending on datasets • Temperature Gradient,, Richardson,MOL, PasquillClasses ... • It is important to establish proper models capable of reproducing reality, otherwise various tuning options will not improving modeling capability and understanding of the flow behavior.

7. Data forStability Determination • Measurement Limitations: • Met Tower: No flux measurements; sensor accuracy/mounting for gradient method; short time period not representative. • Lidar: No temperature gradient measured; short time period; approaches like Pasquill not yet validated. Model Data Possibilities: MERRA data is free available in the internet. MOL can be calculated by the variables given in the data set. Modern-ERA Retrospective Analysis for Research and Applications, http://gmao.gsfc.nasa.gov/merra/) The question is which of the four surrounding MERRA data points should be used to assess the stability parameters or if an inverse distance weighting can be used. EWC Wind Potential Analysis provide downscaled MERRA data with auxiliary information.

8. Examplesfrom different Sites • Site A - Complex, forestSite B - Medium, noforest • Site C - Flat, forest Site D - Complex, water, noforest All sites with one or more met towers, A and B with additional Lidar measurements

9. Site A- Stabilitydistribution • Application of different Stability Classifications  Monthly Distribution • Measurement Data • MERRA Data • Similar average ratio of stable cases over the year • Ratio neutral/instable depends on classification scheme • Monthly variability smoothed by MERRA

10. Site A- Quality Around 10% fewer stable cases in MERRA Good correlation of speed and temperature data but not for wind direction Main stable directions SE/NW not enough represented in MERRA

11. Site B- Stability Distribution and Quality • Measurement Data • MERRA Data Good agreement Mast Gradient and MERRA Gradient/MOL

12. Site C- Stability Distribution and Quality • Measurement Data • MERRA Data MERRA gives more instable cases and fewer neutral ones, stable distribution ok.

13. Site D- Stability Distribution and Quality • Measurement Data • MERRA Data Too much instable cases and around 10% fewer stable cases. Site generally shows more neutral stratification.

14. ComparisonTemperature Cycle C and D Influence of land/sea surface distribution can influence the quality of diurnal temperature cycle in MERRA in comparison to only land surface.  More instable cases are produced Site C Site D

15. All Sites- longterm MERRA MOL • A- Complex, forest B- Medium, noforest • C- Flat forest D- Complex, noforest  Stability values and distribution (sectorwise) can be used to CFD model setup

16. Benefits for CFD Modelling • Results Site D: Sectorwise Wind Profiles

17. Conclusions and Outlook • Local different stability regimes can be shown to significantly influence windflowbehaviour. MERRA has proven to be a valuable data set for the determination of the monthly overall stability conditions of a site as long as the surrounding grid points are representative for the site. • For complex terrain and in coastal sites with land/sea mixing, the model wind direction distribution can be misleading. Often the use of measured wind direction might improve the results. • Application of MERRA MOL helps with proper setup of CFD Modelling and results in better representation of wind profiles. Automatic stability classification from measurements and MERRA data download will be part of WindSim during 2013.

18. ThankyouverymuchforyourattentionAcknowledgements: • Contact: • Dr. Carolin Schmitt • juwi Wind GmbH • Energie-Allee 1 • 55286 Wörrstadt • carolin.schmitt@juwi.de www.juwi.de

21. Determination andApplicationof Parameters • How stability classification can be calulated • Temperature gradient To – gamma*z • Monin-Obukhovlength • Richardson • … many more

22. MERRA Data MERRA dataisfreeavailable in theinternet. The MOL is not givendirectly but canbecalculatedbythe variables given in thedataset. k= karman g = Gravity cpl/cp = heatcapacity (dry/wet) Tv = virtualtemperature tt = temperature spfh = specifichumidity shtfl = surfaceheatflux lml = lowestmodellevel The questioniswhichofthefoursuroundingMerradatapointsshouldbeusedtoassessthestabilityorif an inverse distanceweightingcanbeused.