Abstract
This study proposes a novel approach to explore the solution space of a wind turbine rotor design process. The goal is to offer to blade designers the possibility to select an optimal rotor for given market conditions, assessing trade-offs and alternatives in a matter of minutes. The design process consists of sequential aerodynamic-structural optimizations, where the two loops are linked via a blade-loading parameter that can be varied by the user. A design study is performed starting from the IEA Wind Task 37 land-based reference wind turbine. The solution space is characterized for rotor diameters in the range of 130–160 m, rated generator power values in the range of 3.0–6.0 MW, and tip-speed ratios in the range of 7.5–12.5. Results are discussed highlighting optimal design decisions and trade-offs.
Highlights
Growing competition among wind turbine manufacturers is putting pressure on turbine designers to devise tailored solutions for different markets while reducing development times for new products, which are pushed to market with increasing frequency
Design Approach The wind turbine blade design process is inherently aerostructural, and an optimal design solution consists of a trade-off between the two disciplines
The framework, which runs in a matter of minutes, is first described and later exercised on a sweep of designs for rotor diameters between 130 and 160 m, rated power values between 3.0 and 6.0 MW, and tip-speed ratios between 7.5 and 12.5
Summary
Growing competition among wind turbine manufacturers is putting pressure on turbine designers to devise tailored solutions for different markets while reducing development times for new products, which are pushed to market with increasing frequency. Studies have simulated the loading originating from steady-state storm wind [1, 2], steady-state rated wind [3, 4], steady-state condition mimicking a wind gust [5], and a simplified dynamic aeroservoelastic simulation mimicking loads from a full design load basis [6]. These frameworks suffer the risk of not representing the loads correctly and alternative approaches embed the actual design load cases into nested optimization algorithms [7]. The latter family of approaches runs into significantly higher computational costs, with run times in the order of 102 hours for single-objective studies
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