Tidal turbine blade design optimization based on coupled deep learning and blade element momentum theory

The practical design optimization of blade structures is crucial for enhancing the power capture capability of tidal turbines. However, the significant computational costs required for directly optimizing turbine blades through numerical simulations limit the practical application of blade structure...

Full description

Saved in:
Bibliographic Details
Published in:Physics of fluids (1994) 2024-05, Vol.36 (5)
Main Authors: Li, Changming, Liang, Bingchen, Yuan, Peng, Liu, Bin, Zhao, Ming, Zhang, Qin, Tan, Junzhe, Liu, Jiahua
Format: Article
Language:English
Subjects:
Artificial neural networks
Coefficients
Deep learning
Design optimization
Genetic algorithms
Hydrofoils
Machine learning
Mathematical models
Momentum theory
Performance prediction
Sorting algorithms
Turbine blades
Turbines
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The practical design optimization of blade structures is crucial for enhancing the power capture capability of tidal turbines. However, the significant computational costs required for directly optimizing turbine blades through numerical simulations limit the practical application of blade structure optimization. This paper proposes a framework for tidal turbine blade design optimization based on deep learning (DL) and blade element momentum (BEM). This framework employs control points to parameterize the three-dimensional geometric shape of the blades, uses convolutional neural networks to predict the hydrodynamic performance of each hydrofoil section, and couples BEM to forecast the performance of tidal turbine blades. The multi-objective non-dominated sorting genetic algorithm II is employed to optimize the geometric parameters of turbine blades to maximize the power coefficient and minimize the thrust coefficient, aiming to obtain the optimal trade-off solution. The results indicate that the prediction of the DL-BEM model agrees well with experimental data, significantly improving optimization efficiency. The optimized tidal turbine blades exhibit excellent power coefficients and reduced thrust coefficients, achieving a more balanced structural solution. The proposed optimization framework based on DL accurately and rapidly predicts the performance of tidal turbines, facilitating the design optimization of high-performance tidal turbine blades.
ISSN:1070-6631
1089-7666
DOI:10.1063/5.0197830