A study of Stall-Induced Vibrations using Surrogate-Based Optimization

Abstract

Stall-Induced Vibrations (SIV) are an important design consideration for wind turbine blade design, especially for large, modern, wind turbines with highly flexible blades. Their severity depends on both the inflow and the structural characteristics of the blades. Studying SIV has a high computational cost, because it requires high-fidelity aeroelastic simulations, and potentially a large number of input variables. In an effort to reduce the computational cost of the domain exploration, in this work we have adopted a Surrogate-Based Optimization (SBO) framework. This way, the combination of input variables that lead to SIV can be explored with the minimum number of aeroelastic simulations. The proposed SBO framework can use any type of surrogate model, and leverages Delaunay triangulation to iteratively select samples to refine the surrogate model. The occurrence and severity of SIV on the IEA 10MW turbine is studied in a five variables space consisting of: wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. A well-trained surrogate model is developed and used to predict the damping ratio of the first blade edgewise mode in the entire inflow space at a reduced computational cost. Sensitivity analysis of the predicted damping ratio shows that yaw angle is the most influential variable, while temperature is the least influential variable in terms of inflow conditions that can lead to the occurrence of SIV. Inflow conditions with a moderate yaw angle (around 10–25 deg), high wind speeds, and moderate to high negative veer are found to lead to severe SIV. This study should serve as a guiding tool to decide the scope of the more computationally expensive simulations such as high-fidelity CFD-based aeroelastic simulations which can provide a more accurate description of SIV.