06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7

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Author: search.filters.author.Cheng, Po WenSubject: search.filters.subject.620

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    Wind turbine rotors in surge motion : new insights into unsteady aerodynamics of floating offshore wind turbines (FOWTs) from experiments and simulations
    (2024) Schulz, Christian W.; Netzband, Stefan; Özinan, Umut; Cheng, Po Wen; Abdel-Maksoud, Moustafa
    An accurate prediction of the unsteady loads acting on floating offshore wind turbines (FOWTs) under consideration of wave excitation is crucial for a resource-efficient turbine design. Despite a considerable number of simulation studies in this area, it is still not fully understood which unsteady aerodynamic phenomena have a notable influence on the loads acting on a wind turbine rotor in motion. In the present study, investigations are carried out to evaluate the most relevant unsteady aerodynamic phenomena for a wind turbine rotor in surge motion. As a result, inflow conditions are determined for which a significant influence of these phenomena on the rotor loads can be expected. The experimental and numerical investigations are conducted on a two-bladed wind turbine rotor subjected to a tower-top surge motion. A specialised wind tunnel test rig has been developed to measure the aerodynamic torque response of the rotor subjected to surge motions with moderate frequencies. The torque measurements are compared to two free-vortex-wake (FVW) methods, namely a panel method and a lifting-line method. Unsteady contributions that cannot be captured using quasi-steady modelling have not been detected in either the measurements or the simulations in the covered region of motions ranging from a rotor reduced frequency of 0.55 to 1.09 and with motion velocity amplitudes of up to 9 % of the wind speed. The surge motion frequencies were limited to a moderate range (5 to 10 Hz) due to vibrations occurring in the experiments. Therefore, a numerical study with an extended range of motion frequencies using the panel and the lifting-line method was performed. The results from both FVW methods reveal significant unsteady contributions of the surge motions to the torque and thrust response that have not been reported in the recent literature. Furthermore, the results show the presence of the returning wake effect, which is known from helicopter aerodynamics. Additional simulations of the UNAFLOW scale model and the IEA 15 MW rotor demonstrate that the occurrence of the returning wake effect is independent from the turbine but determined by the ratio of 3P and surge motion frequency. In the case of the IEA 15 MW rotor, a notable impact of the returning wake effect was found at surge motion frequencies in the range of typical wave periods. Finally, a comparison with OpenFAST simulations reveals notable differences in the modelling of the unsteady aerodynamic behaviour in comparison to the FVW methods.
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    Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
    (2024) Gräfe, Moritz; Pettas, Vasilis; Dimitrov, Nikolay; Cheng, Po Wen
    Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.