D-optimal sensor placement for load identification in wind turbine rotor blades

Accurate load identification is a prerequisite for monitoring damage processes such as fatigue accumulation. This work approaches the load recognition problem in an inverse inferential setting by processing readings from a strain sensor grid mounted on the operating wind turbine rotor blade. The aeroelastic pressure field is considered an equivalent lumped load vector applied at given stations along the blade's length, and its magnitude is the quantity of inferential interest. The technical challenge of optical sensor placement is addressed through D-optimal designs that promise sensor architectures, that is, locations and features, which offer a minimal uncertainty propagation of the sensor readings to the load inferences. Synthetic data are generated through finite element simulations based on an actual composite material geometry to demonstrate and quantitatively assess the effectiveness of the process. D-optimal sensor grid designs are obtained through the employment of Genetic Algorithms. Further reduction of the involved epistemic uncertainty due to the problem's inherent ill-conditioning is assessed by evaluating sensor grids with increasing sensor numbers. The proposed inferential scheme presents a robust way to approach the inverse load identification problem.