An aero-structure-acoustics evaluation framework of wind turbine blade cross-section based on Gradient Boosting regression tree

Abstract

Under the rapid development of wind turbines, the rotor size has substantially increased in recent years. To meet the key design criteria, finding a trade-off between aerodynamic, structure and noise (ASN) impact becomes a challenging problem. The blade cross-section is the basic element of the blade, its outer contour is the airfoil profile that produces aerodynamic loads as well as noise, and the inner part is the supporting composite material that provides enough stiffness to balance the loads. Modifying local blade sections can adjust the rotors’ overall performance. However, in the work of blade-combined ASN optimization, the iterative process using traditional numerical simulation methods becomes extremely heavy. In this study, a sustainable database is created based on a large number of calculations of aerodynamic, structural and noise attributes at various cross-sections. Then a platform named AFML (Airfoil machine learning) for simultaneously predicting the comprehensive performance of the cross-section is constructed by using the integrated Gradient Boosting Regression Tree algorithm. Results show that the prediction accuracy of AFML is acceptable even for unseen inflow conditions. By calling the pre-trained AFML, ASN data of the cross-section can be immediately obtained, and the blade shape and inner structure can be updated quickly.