With the increasing size of wind turbines in terms of their dimensions and capacity, structural design optimization for their blades is becoming all the more important. This study suggests an improved optimization framework. Blade optimization is performed in two stages: the ply lay-up pattern of the spar cap in the initial blade configuration based on the existing configuration, followed by the cross-sectional design optimization at several spanwise locations. The genetic algorithm is adopted and, in terms of the structural integrity evaluation, the final configuration results are found to be safe; in addition, the number of plies in the spar cap rapidly decreases through the two-stage design optimization procedure. As a result, the blade is lighter, and the lighter blade also reduces the applied load on the wind turbine. This will have an excellent effect that leads to a reduction in the weight of the entire turbine system.

Within the framework of blade aerodynamic design, the maximum aerodynamic efficiency, power production, and minimum thrust force are the targets to obtain. This paper describes an improved optimization framework for blade aerodynamic design under realistic conditions, while considering multiple design parameters. The relationship between the objective function and the design parameters, such as the chord length, maximum chord, and twist angle, were obtained by using the second-order response surface methodology (RSM). Moreover, the identified parameters were organized to optimize the aerodynamic design of the blades. Furthermore, the initial and optimized blade geometries were compared and showed that the performance of the optimized blade improved significantly. In fact, the efficiency was increased by approximately 10%, although its thrust was not varied. In addition, to demonstrate the improvement in the resulting optimized blades, the annual energy production (AEP) was estimated when installed in a specific regional location. The result showed a significant improvement when compared to the baseline blades. This result will be extended to a new perspective approach for a more robust optimal design of a wind turbine blade.

The design and manufacturing of an active twist rotor blade for vibration reduction in helicopters are presented. The rotor blade is integrally twisted by direct strain actuation through embedded piezoelectric fiber composite actuators distributed along the span of the blade. Highlights of the analysis formulation used to design this type of active blade are presented. The requirements for the prototype blade, along with the final design results are also presented. Detailed aspects of its manufacturing are described. Experimental structural characteristics of the prototype blade compare well with design goals, and bench actuation tests characterize its basic actuation performance. The design and manufacturing processes permit the realization of an active blade that satisfies a given set of design requirements. This is used to later develop a fully active rotor blade system.

An improved nonintrusive parametric model order reduction (pMOR) approach is proposed for the flow field interpolation regarding fluid–structure interaction (FSI) objects. Flow field computation using computational fluid dynamics (CFD) requires excessive computational time and memory. Nonintrusive and data-driven MOR schemes have been proposed to overcome such limitations. The present methodology is implemented by both proper orthogonal decomposition (POD) and a modified Nouveau variational autoencoder (mNVAE). POD attempts to reduce the number of degrees of freedom (DOFs) on the precomputed series of the full-order model parametric result. The reduced DOF yields parametrically independent reduced bases and dependent coefficients. Then, mNVAE is employed for the interpolation of POD coefficients, which will be combined with POD modes for parametrically interpolated flow field generation. The present approach is assessed on the benchmark problem of a two-dimensional plunging airfoil and the highly nonlinear FSI phenomenon of the limit cycle oscillation. The comparison was executed against other POD-based generative neural network approaches. The proposed methodology demonstrates applicability on highly nonlinear FSI objects with improved accuracy and efficiency.