Kriging-assisted multi-objective optimization of a wind turbine blade based on fluid-structure interaction analysis

Abstract

This study presents a surrogate-assisted multi-objective  framework for a GE 1.5XLE horizontal-axis wind turbine (HAWT) blade based on a one-way fluid-structure interaction (FSI) analysis. The objective is to identify optimal trade-offs between structural weight reduction and dynamic performance by simultaneously minimizing blade mass and maximizing the fundamental natural frequency. Aerodynamic loads are first computed using computational fluid dynamics (CFD) and subsequently transferred to a structural finite element analysis (FEA) model through a one-way FSI coupling. To reduce the computational cost associated with repeated high-fidelity simulations, a Kriging metamodel is constructed using a latin hypercube sampling (LHS) design of experiments. The resulting surrogate model demonstrated high predictive accuracy, with a maximum prediction error below 0.4 %. A multi-objective genetic algorithm (MOGA) is then employed to generate the Pareto optimal set of solutions and to identify the best compromise designs under stress and deformation constraints. The optimal design achieves an approximately 10% reduction in total mass and a 9.1% increase in fundamental frequency compared with the baseline blade, while satisfying all structural requirements. Independent high-fidelity numerical verification confirms the reliability of the proposed framework, with deviations of less than 0.2 %. The proposed FSI-Kriging-MOGA methodology provides an efficient and reliable computational tool for the aeroelastic optimization of wind turbine blades and can be extended to other complex fluid-structure interaction systems.