In recent years, sites with low annual average wind speeds have begun to be considered for the development of new wind farms. The majority of design methods for a wind turbine operating at low wind speed is to increase the blade length or hub height compared to a wind turbine operating in high wind speed sites. The cost of the rotor and the tower is a considerable portion of the overall wind turbine cost. This study investigates a method to trade-off the blade length and hub height during the wind turbine optimization at low wind speeds. A cost and scaling model is implemented to evaluate the cost of energy. The procedure optimizes the blades’ aero-structural performance considering blade length and the hub height simultaneously. The blade element momentum (BEM) code is used to evaluate blade aerodynamic performance and classical laminate theory (CLT) is applied to estimate the stiffness and mass per unit length of each blade section. The particle swarm optimization (PSO) algorithm is applied to determine the optimal wind turbine with the minimum cost of energy (COE). The results show that increasing rotor diameter is less efficient than increasing the hub height for a low wind speed turbine and the COE reduces 16.14% and 17.54% under two design schemes through the optimization.

This paper presents a methodology for lightweight and high‐strength design of an excavator bucket under uncertain loading. Uncertain loads are obtained by using the Monte Carlo simulation based on the existing soil‐bucket interaction model in which the soil parameters are variable. And the well‐known 3‐sigma methodology is used for the quantification of the uncertain loads. Excavator bucket modelling is finished by using ANSYS Parameter Design Language (APDL). A multiobjective optimization model aiming to decrease the maximum von Mises stress and to reduce the weight of the bucket is established on the foundations of the uncertain load and the parametric geometry model. The structural shape and topology of the bucket are then designed by using the mixed variable genetic algorithm to solve the established optimization problem. The results show that the presented method can be effectively and efficiently applied for the optimization design of the excavator bucket and that the optimized bucket signifies obvious decreases in the weight and the stress compared with the initial reference model. The proposed methodology for structure optimization design considering uncertain loads not only provides the technical means for the design and development of high‐performance bucket but also lays a preliminary theoretical foundation for the optimization design integrated machine‐environment interaction.