A Modeling Framework to Analyze Active Adaptive Aerostructures

Wind turbine performance is evaluated by levelized cost of energy (LCOE). The LCOE has dropped over the years due to advancements in wind energy design and control technology. One promising area of technology involves morphing blades. Specifically, active transformation of the blade twist angle has been shown to improve aerodynamic performance. The performance of this capability is being studied through computer modeling and experimental work. To advance this technology, data is needed to build a data-driven model that will enable design and control. The work in this paper examines different model and experimental data. Techniques for evaluating data are presented as well. Comparisons are made between the experimental data, low-fidelity BEM modeling, and high-fidelity CFD modeling work to evaluate the comparative benefits of each modeling method. A Buckingham’s Pi theorem scaling approach is shown to illustrate the connection between the models and full-scale turbines. Also presented is a procedure to examine the accuracy of the data. Determined experimental uncertainty is thus included in the models presented. The overarching goal is to develop a framework that combines decision-based data curation with data-driven modeling to support design and control of adaptive aerostructures.