Comment on wes-2022-79

Abstract

Stall Induced Vibrations (SIV) are an important design consideration for wind turbine blade design, especially for large, modern wind turbines which require long and flexible blades. Their severity depends on the inflow conditions, and structural characteristics of the blade, and the study of the parameter space that leads to SIV has a high computational cost due to the aeroelastic simulations involved, and the cost increases drastically with increasing number of variables to be studied. Given the computationally expensive nature of the problem, in this work, a Surrogate-Based Optimization (SBO) framework which uses surrogate models as an alternative to the high fidelity simulations is proposed to explore the behaviour of SIV. The proposed framework is not specific to any type of surrogate model and uses Delaunay Triangulation to iteratively select samples to refine the surrogate model. The framework is demonstrated to study the occurrence and severity of SIV on the IEA 10MW turbine in a five variable inflow space consisting of wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. Using the proposed framework, 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 occurrence of SIV. Inflow conditions with a moderate yaw angle (around 15–20 deg), high wind speeds, and moderate to high negative veer are found to lead to severe SIV. This framework is expected to 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.