A Vortex Identification Method Based on Extreme Learning Machine
Vortex identification and visualization are important means to understand the underlying physical mechanism of the flow field. Local vortex identification methods need to combine with the manual selection of the appropriate threshold, which leads to poor robustness. Global vortex identification meth...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8865001 |
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| _version_ | 1832554070358884352 |
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| author | Jun Wang Lei Guo Yueqing Wang Liang Deng Fang Wang Tong Li |
| author_facet | Jun Wang Lei Guo Yueqing Wang Liang Deng Fang Wang Tong Li |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | Vortex identification and visualization are important means to understand the underlying physical mechanism of the flow field. Local vortex identification methods need to combine with the manual selection of the appropriate threshold, which leads to poor robustness. Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. It cannot be extended and is suitable for flow fields of different sizes. Recently, proposed deep learning methods have long network training time and high computational complexity. Aiming at the above problems, we present a novel vortex identification method based on the Convolutional Neural Networks-Extreme Learning Machine (CNN-ELM). This method transforms the vortex identification problem into a binary classification problem, and can quickly, objectively, and robustly identify vortices from the flow field. A large number of experiments prove the effectiveness of our method, which can improve or supplement the shortcomings of existing methods. |
| format | Article |
| id | doaj-art-2557e9d6e7de432c974438c79e067872 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-2557e9d6e7de432c974438c79e0678722025-02-03T05:52:26ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/88650018865001A Vortex Identification Method Based on Extreme Learning MachineJun Wang0Lei Guo1Yueqing Wang2Liang Deng3Fang Wang4Tong Li5University of Electronic Science and Technology of China, Chengdu, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaComputational Aerodynamics Institute at China Aerodynamics Research and Development Center, Mianyang, ChinaComputational Aerodynamics Institute at China Aerodynamics Research and Development Center, Mianyang, ChinaComputational Aerodynamics Institute at China Aerodynamics Research and Development Center, Mianyang, ChinaUniversity of Electronic Science and Technology of China, Chengdu, ChinaVortex identification and visualization are important means to understand the underlying physical mechanism of the flow field. Local vortex identification methods need to combine with the manual selection of the appropriate threshold, which leads to poor robustness. Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. It cannot be extended and is suitable for flow fields of different sizes. Recently, proposed deep learning methods have long network training time and high computational complexity. Aiming at the above problems, we present a novel vortex identification method based on the Convolutional Neural Networks-Extreme Learning Machine (CNN-ELM). This method transforms the vortex identification problem into a binary classification problem, and can quickly, objectively, and robustly identify vortices from the flow field. A large number of experiments prove the effectiveness of our method, which can improve or supplement the shortcomings of existing methods.http://dx.doi.org/10.1155/2020/8865001 |
| spellingShingle | Jun Wang Lei Guo Yueqing Wang Liang Deng Fang Wang Tong Li A Vortex Identification Method Based on Extreme Learning Machine International Journal of Aerospace Engineering |
| title | A Vortex Identification Method Based on Extreme Learning Machine |
| title_full | A Vortex Identification Method Based on Extreme Learning Machine |
| title_fullStr | A Vortex Identification Method Based on Extreme Learning Machine |
| title_full_unstemmed | A Vortex Identification Method Based on Extreme Learning Machine |
| title_short | A Vortex Identification Method Based on Extreme Learning Machine |
| title_sort | vortex identification method based on extreme learning machine |
| url | http://dx.doi.org/10.1155/2020/8865001 |
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