Open AccessQuantification of the Dynamic-stall Model Uncertainty in the Performance Prediction of Vertical Axis Wind Turbines
Author(s) -
Giacomo Persico,
Andrea G. Sanvito,
Vincenzo Dossena
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1618/5/052071
Subject(s) - stall (fluid mechanics) , aerodynamics , airfoil , wind power , computer science , robustness (evolution) , probabilistic logic , vertical axis , torque , uncertainty quantification , control theory (sociology) , mechanics , engineering , physics , biochemistry , chemistry , control (management) , artificial intelligence , engineering drawing , electrical engineering , gene , thermodynamics , machine learning
Low-fidelity predictions for vertical-axis wind turbines are affected by uncertainty due to the complexity of the rotor aerodynamics. In particular, in the most common operating conditions the blades undergo periodic excursions beyond the static stall limit, activating dynamic-stall effects. In this study we show how advanced dynamic-stall models, implemented in the frame of the Blade-Element-Momentum theory, are able to upgrade significantly the prediction of low-fidelity tools, both in deterministic and probabilistic terms. In particular, an uncertainty quantification is performed to investigate the epistemic uncertainty of the Strickland dynamic-stall model, introducing a large variability on the empirical parameters appearing in the formulation. The resulting variability in the power coefficient and torque exchange, compared to corresponding wind-tunnel and high-fidelity CFD values, remains relatively limited and, in the conditions around peak efficiency, it is comparable with the measurement uncertainty of the experiment. As a further relevant conclusion, the model uncertainty does not alter the general outcome of the deterministic model, thus demonstrating the robustness of the DMST predictions obtained in the present study.