Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow

Matias Sessarego, Ju Feng, Néstor Ramos-García and Sergio González Horcas

Renewable Energy, 2020, vol. 146, issue C, 1524-1535

Abstract: This article describes the application of neural networks for the design optimization of a curved wind turbine blade using an aero-elastic simulator with synthetic inflow turbulence. A vortex particle method where the wind turbine blades are represented by lifting-line theory is used, while the wind turbine structural dynamics are modeled using a finite-element multi-body based approach. A neural network together with a gradient-based optimizer allows to quickly design a new curved wind turbine blade in a complex aero-elastic wind-turbine simulation scenario. The blade design found from the neural network has increased pre-bend and sweep compared to the straight blade design. It produces approximately 1% more power on average with a slight increase of mean thrust on the rotor of 0.02% compared to the straight one. This study demonstrates that neural networks can be effective for designing wind turbine rotor blades involving complex aero-elastic simulation scenarios with turbulent inflow conditions. Further work may improve the performance of the neural network's predictive capabilities as well as the optimized design.

Keywords: Wind turbine blade design; Neural network; Optimization; Vortex particle method; Aerodynamics; Aero-elasticity (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:146:y:2020:i:c:p:1524-1535

DOI: 10.1016/j.renene.2019.07.046

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