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

Research output: Contribution to journalJournal article – Annual report year: 2020Researchpeer-review

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Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow. / Sessarego, Matias; Feng, Ju; Ramos García, Néstor; Horcas, Sergio González.

In: Renewable Energy, Vol. 146, 2020, p. 1524-1535.

Research output: Contribution to journalJournal article – Annual report year: 2020Researchpeer-review

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@article{14206d8d7052490291f2521086f029f2,
title = "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow",
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 = "Aero-elasticity, Aerodynamics, Neural network, Optimization, Vortex particle method, Wind turbine blade design",
author = "Matias Sessarego and Ju Feng and {Ramos Garc{\'i}a}, N{\'e}stor and Horcas, {Sergio Gonz{\'a}lez}",
year = "2020",
doi = "10.1016/j.renene.2019.07.046",
language = "English",
volume = "146",
pages = "1524--1535",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow

AU - Sessarego, Matias

AU - Feng, Ju

AU - Ramos García, Néstor

AU - Horcas, Sergio González

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Aero-elasticity

KW - Aerodynamics

KW - Neural network

KW - Optimization

KW - Vortex particle method

KW - Wind turbine blade design

U2 - 10.1016/j.renene.2019.07.046

DO - 10.1016/j.renene.2019.07.046

M3 - Journal article

VL - 146

SP - 1524

EP - 1535

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -