Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL
To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide p...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8336 |
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| author | Qingsong Liu Gan Ren Dingfu Zhou Bo Liu Zida Li |
| author_facet | Qingsong Liu Gan Ren Dingfu Zhou Bo Liu Zida Li |
| author_sort | Qingsong Liu |
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| description | To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulations of the ship airwake within the glide path region are carried out using the Poly-Hexcore grid and the detached eddy simulation (DES)/the Reynolds-averaged Navier–Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different inflow conditions as a sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, and 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz). |
| format | Article |
| id | doaj-art-36f73101d0ba4bce8fd82ad0070f207a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-36f73101d0ba4bce8fd82ad0070f207a2025-08-20T04:00:49ZengMDPI AGApplied Sciences2076-34172025-07-011515833610.3390/app15158336Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWLQingsong Liu0Gan Ren1Dingfu Zhou2Bo Liu3Zida Li4Lidar and Device Laboratory, Southwest Institute of Technical Physics, Chengdu 610041, ChinaLidar Imaging Detection Technology and Equipment Airworthiness Testing Laboratory, Civil Aviation Flight University of China, Guanghan 618307, ChinaLidar and Device Laboratory, Southwest Institute of Technical Physics, Chengdu 610041, ChinaInstitute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, ChinaLidar Imaging Detection Technology and Equipment Airworthiness Testing Laboratory, Civil Aviation Flight University of China, Guanghan 618307, ChinaTo meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulations of the ship airwake within the glide path region are carried out using the Poly-Hexcore grid and the detached eddy simulation (DES)/the Reynolds-averaged Navier–Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different inflow conditions as a sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, and 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz).https://www.mdpi.com/2076-3417/15/15/8336ship airwakehigh spatiotemporal resolutionBP neural networkCFDDWL |
| spellingShingle | Qingsong Liu Gan Ren Dingfu Zhou Bo Liu Zida Li Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL Applied Sciences ship airwake high spatiotemporal resolution BP neural network CFD DWL |
| title | Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL |
| title_full | Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL |
| title_fullStr | Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL |
| title_full_unstemmed | Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL |
| title_short | Rapid Prediction of High-Resolution 3D Ship Airwake in the Glide Path Based on CFD, BP Neural Network, and DWL |
| title_sort | rapid prediction of high resolution 3d ship airwake in the glide path based on cfd bp neural network and dwl |
| topic | ship airwake high spatiotemporal resolution BP neural network CFD DWL |
| url | https://www.mdpi.com/2076-3417/15/15/8336 |
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