Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies
With the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simu...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Fluids |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-5521/10/5/112 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126442869817344 |
|---|---|
| author | Shiyu Yang Mingming Zhang Yu Feng Haikun Jia Na Zhao Qingwei Chen |
| author_facet | Shiyu Yang Mingming Zhang Yu Feng Haikun Jia Na Zhao Qingwei Chen |
| author_sort | Shiyu Yang |
| collection | DOAJ |
| description | With the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simultaneously. To tackle this challenge, this research focuses on the low accuracy issues of the traditional Blade Element Momentum theory (BEM) in predicting the aerodynamic performance of wind turbine blades. Consequently, a correction framework is proposed, to integrate the Computational Fluid Dynamics (CFD) method with the Multilayer Perceptron (MLP) neural network. In this approach, the CFD method is used to predict the airflow characteristics around the blades, and the MLP neural network is employed to model the intricate functional relationships between multiple influencing factors and key aerodynamic parameters. This process results in high-precision predictive functions for key aerodynamic parameters, which are then used to correct the traditional BEM. When this correction framework is applied to the rotor of the IEA 15 MW wind turbine, the effectiveness of MLP in predicting key aerodynamic parameters is demonstrated. The research findings suggest that this framework can enhance the accuracy of BEM aerodynamic load predictions to a level comparable to that of RANS. |
| format | Article |
| id | doaj-art-aa3da25296724bde87d8d6db64658b98 |
| institution | OA Journals |
| issn | 2311-5521 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fluids |
| spelling | doaj-art-aa3da25296724bde87d8d6db64658b982025-08-20T02:33:55ZengMDPI AGFluids2311-55212025-04-0110511210.3390/fluids10050112Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence TechnologiesShiyu Yang0Mingming Zhang1Yu Feng2Haikun Jia3Na Zhao4Qingwei Chen5School of Robotics and Advanced Manufacture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Robotics and Advanced Manufacture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaSchool of Robotics and Advanced Manufacture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, ChinaNational Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing 100192, ChinaEconomic & Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, ChinaWith the development of the wind power industry, wind turbine blades are increasingly adopting ultra-large-scale designs. However, as the size of blades continues to increase, existing aerodynamic calculation methods struggle to achieve both relatively high computational accuracy and efficiency simultaneously. To tackle this challenge, this research focuses on the low accuracy issues of the traditional Blade Element Momentum theory (BEM) in predicting the aerodynamic performance of wind turbine blades. Consequently, a correction framework is proposed, to integrate the Computational Fluid Dynamics (CFD) method with the Multilayer Perceptron (MLP) neural network. In this approach, the CFD method is used to predict the airflow characteristics around the blades, and the MLP neural network is employed to model the intricate functional relationships between multiple influencing factors and key aerodynamic parameters. This process results in high-precision predictive functions for key aerodynamic parameters, which are then used to correct the traditional BEM. When this correction framework is applied to the rotor of the IEA 15 MW wind turbine, the effectiveness of MLP in predicting key aerodynamic parameters is demonstrated. The research findings suggest that this framework can enhance the accuracy of BEM aerodynamic load predictions to a level comparable to that of RANS.https://www.mdpi.com/2311-5521/10/5/112CFDBEMneural network |
| spellingShingle | Shiyu Yang Mingming Zhang Yu Feng Haikun Jia Na Zhao Qingwei Chen Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies Fluids CFD BEM neural network |
| title | Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies |
| title_full | Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies |
| title_fullStr | Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies |
| title_full_unstemmed | Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies |
| title_short | Research on the Improvement of BEM Method for Ultra-Large Wind Turbine Blades Based on CFD and Artificial Intelligence Technologies |
| title_sort | research on the improvement of bem method for ultra large wind turbine blades based on cfd and artificial intelligence technologies |
| topic | CFD BEM neural network |
| url | https://www.mdpi.com/2311-5521/10/5/112 |
| work_keys_str_mv | AT shiyuyang researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies AT mingmingzhang researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies AT yufeng researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies AT haikunjia researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies AT nazhao researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies AT qingweichen researchontheimprovementofbemmethodforultralargewindturbinebladesbasedoncfdandartificialintelligencetechnologies |