Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.
Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. The majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration are particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, the sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.
Recent research developments have indicated that substantial reduction of both the fatigue and ultimate loads can be achieved by adopting trailing edge (TE) flap control strategies. Their aeroelastic tools employ blade element momentum (BEM) aerodynamic models enhanced with a sectional 2D treatment of the TE flap, neglecting the 3D effect of the trailed vorticity in the vicinity of the moving flap. In the present paper, a cross comparison of the BEM‐based models used in the aeroelastic analysis tools against higher fidelity, free‐wake lifting line, and fully resolved CFD models is performed, with the aim to highlight limitations of the first. A second level of comparison assesses the differences among tools of the same level of fidelity from different research groups. Moreover, a number of engineering‐based correction models that are used in conjunction with BEM and account for the complex 3D trailed vorticity effect are assessed. Simulations of a stiff rotor configuration of the DTU 10 MW Reference Wind Turbine are performed for a prescribed, harmonic TE flap motion, and aerodynamic loads are compared at the sectional and rotor‐integrated level. For the studied stiff rotor with the chosen flaps configuration, the results of the code‐to‐code comparisons indicate that low‐fidelity BEM tools consistently predict 1P variations of the rotor thrust due to the TE flap motion, but fail to reproduce the details of the load distributions especially in the vicinity of the flap section. BEM‐based corrected models, which account for 3D‐induced velocity effects, provide load distribution predictions closer to higher fidelity free‐wake and CFD models.
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