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6.陈滢依 CWP2023_Turbine interaction models and validation_DNV(换pdf格式公开).pdf

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1、Turbine interaction models of different fidelity and their validations CWP 2023Yingyi Chen Renewable Advisory,Energy System17th Oct 2023DNV 17 OCTOBER 2023Turbine Interaction Models DNV 17 OCTOBER 2023Turbine interaction modelling history198019902000201020203Jensen/Katic(Park)Ainslie(Eddy viscosity)

2、DNV CFD turbine interaction modelWind tunnels+first wind farm validations10%power overpredictions at Horns Rev!(80 turbines)Large wind farm correction modelsDTU FugaBlockage effect!DNV BEET Blockage correction modelconcern about long range wind farm wakes between clusters growsJan 2022 Orsted releas

3、e TurbOParkAnsys CFD Wind-Modellermodel fidelityLESNon-linear RANS EngineeringLinearised RANSCommercial scale wind farms WakeBlasterWind farm design software released:WindFarmerWindProDNV 17 OCTOBER 2023Cost-fidelity relationship for turbine interaction models4Source:DNVDNV 17 OCTOBER 2023WRF-CFD va

4、lidation results DNV 17 OCTOBER 2023Cost-fidelity relationship for turbine interaction models6Source:DNVDNV 17 OCTOBER 2023Engagement with EnBW7Two successful measurement campaigns with EnBW dual-scanning lidar SCADA (met mast data)Combination of Wind farm blockage Cluster wakesCombination of WRF an

5、d CFD modelling Validation of Blockage(WESC 2021,WindEurope 2021)Blockage and cluster wakes(WESC 2023)Results publishedDNV 17 OCTOBER 2023 Focus on directions where the measurements along the lidar lines are only blockage affected Example from direction 210 Wind speed on the plateau of the thrust cu

6、rve Example on the right:%change in wind speed at hub height(for unstable conditions)Wind speed reduction(through blockage)seen upstream of the wind farm Measurements along lines A and BExample 1:BlockageSouth-Westerly wind directions 8Direction 210ABDNV 17 OCTOBER 2023Line West B and West A,All sta

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本文主要探讨了风力发电场中涡轮机相互作用模型的不同逼真度及其验证。介绍了从1980年代至今,涡轮机相互作用模型的演变,包括Jensen/Katic (Park)模型、Ainslie (Eddyviscosity)模型,以及DNV CFD模型等。文中提到,高逼真度的模型如CFD.ML,在预测风力发电场的能量生产方面表现更好,但计算成本较高。通过与EnBW的合作,作者通过双扫描激光雷达、SCADA系统和气象塔数据,验证了CFD.ML模型的准确性。研究结果表明,CFD.ML在预测风力发电场的内部尾流和集群尾流方面表现良好,但仍有改进空间。此外,文章还比较了经典涡轮机相互作用模型和下一代模型的性能,指出CFD.ML有潜力成为下一代快速响应的涡轮机相互作用模型。
"风力农场互动模型的未来在哪里?" "如何平衡风力农场互动模型的计算成本和精确度?" "风力农场中的 blockage 问题如何解决?"
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