A computational fluid dynamics-based surrogate wind turbine blade aerodynamic model for hybrid simulation
Abstract
Hybrid simulation (HS) is a promising technique for studying wind turbines due to the presence of scaling errors in wind tunnel testing. However, HS of wind-loaded structures is limited by the current practice of using lower-accuracy, "pre-calculated" aerodynamic loads, which uncouple the aerodynamic loading from the structural response. This thesis presents six stand-alone studies that collectively build towards a novel HS framework that employs a computational fluid dynamics (CFD) based surrogate model to generate higher-accuracy aerodynamic loads within the HS loop. An experimentally-validated residential wind turbine model equipped with an external damping system was used to illustrate the proposed framework.
Development of the proposed HS framework occurred in the following stages: firstly, the limitations of the existing HS framework were identified and quantified through a numerical case study of an industrial wind turbine. The aerodynamic surrogate model was developed through CFD simulations of airfoils to determine optimal test parameters, followed by the identification of an optimal convolutional neural network (CNN) architecture to act as the surrogate model. The test case for the HS was chosen based on a numerical study of semi-active tuned mass damper (STMD) systems for reducing turbine tower vibrations. A dynamic numerical model was developed of a 1.1 m residential wind turbine on a rotating base validated through a set of full-scale aeroelastic wind tunnel tests.
These components were ultimately combined for a series of twelve artificial HS to compare the effectiveness of the proposed surrogate model-based HS framework to the existing "pre-calculated" technique. A number of CFD simulations were performed to generate the training data for the CNN surrogate model, which was combined with the dynamic turbine model to act as the numerical HS substructure. This was paired with the optimized STMD acting as the virtual experimental substructure, including simulated equipment delays and measurement errors. The results of these HS indicate that the proposed framework has improved aerodynamic accuracy and aeroelastic fidelity compared to the existing technique, though it faces hurdles from computationally costly CFD data generation. This framework offers a promising tool for future HS of wind turbines and other wind engineering applications.