| Citation: | Yang Jinheng, Li Yingkun, Wu Yan, et al. Rapid prediction of flow field in rocket engine nozzles based on U-Net[J]. Journal of Aerospace Power, 2026, 41(7):20250158 doi: 10.13224/j.cnki.jasp.20250158 |
A prediction model of the flow field in a rocket engine nozzle based on the U-net architecture was proposed to solve the problem of the time-consuming computation of the flow field in the nozzle using conventional computational fluid dynamics (CFD) methods. By changing the temperature and pressure of the nozzle inlet gas over a certain range,
| [1] |
何景轩, 任全彬, 董新刚, 等. 固体动力设计中的几个主要关键基础问题[J]. 固体火箭技术, 2022(2): 181-188. He Jingxuan, Ren Quanbin, Dong Xingang, et al. Some fundamental problems on design of solid propulsion systems[J]. Journal of Solid Rocket Technology, 2022(2): 181-188. (in Chinese
He Jingxuan, Ren Quanbin, Dong Xingang, et al. Some fundamental problems on design of solid propulsion systems[J]. Journal of Solid Rocket Technology, 2022(2): 181-188. (in Chinese)
|
| [2] |
代无劫, 于勇. 基于RBF代理优化的固体火箭发动机喷管型面设计[J]. 固体火箭技术, 2024, 47(2): 188-198. Dai Wujie, Yu Yong. Nozzle profile design of solid rocket motor based on RBF proxy optimization[J]. Journal of Solid Rocket Technology, 2024, 47(2): 188-198. (in Chinese doi: 10.7673/j.issn.1006-2793.2024.02.006
Dai Wujie, Yu Yong. Nozzle profile design of solid rocket motor based on RBF proxy optimization[J]. Journal of Solid Rocket Technology, 2024, 47(2): 188-198. (in Chinese) doi: 10.7673/j.issn.1006-2793.2024.02.006
|
| [3] |
刘锐, 陈雄, 周长省, 等. 复合结构喷管烧蚀形貌的测定及其对流场的影响[J]. 航空学报, 2015, 36(9): 2958-2967. Liu Rui, Chen Xiong, Zhou Changsheng, et al. Measurement of erosion morphology in a composite structure nozzle and its influence on flow field[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(9): 2958-2967. (in Chinese
Liu Rui, Chen Xiong, Zhou Changsheng, et al. Measurement of erosion morphology in a composite structure nozzle and its influence on flow field[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(9): 2958-2967. (in Chinese)
|
| [4] |
何振川, 李映坤, 武炎, 等. 多脉冲固体火箭发动机长尾喷管多层热防护结构传热烧蚀特性[J]. 推进技术, 2025, 46(2): 2403009. He Zhenchuan, Li Yingkun, Wu Yan, et al. Heat transfer and ablation characteristics of multi-layer thermal protection structure for long tail nozzle of multi pulse solid rocket motor[J]. Journal of Propulsion Technology, 2025, 46(2): 2403009. (in Chinese doi: 10.13675/j.cnki.tjjs.2403009
He Zhenchuan, Li Yingkun, Wu Yan, et al. Heat transfer and ablation characteristics of multi-layer thermal protection structure for long tail nozzle of multi pulse solid rocket motor[J]. Journal of Propulsion Technology, 2025, 46(2): 2403009. (in Chinese) doi: 10.13675/j.cnki.tjjs.2403009
|
| [5] |
Nadda M, Shah S K, Roy S, et al. CFD-based deep neural networks (DNN) model for predicting the hydrodynamics of fluidized beds[J]. Digital Chemical Engineering, 2023, 8: 100113. doi: 10.1016/j.dche.2023.100113
|
| [6] |
汪淼. 面向并行CFD迭代计算的网格划分关键技术研究[D]. 长沙: 国防科技大学, 2017. Wang Miao. Mesh partitioning for parallel iterative computation in CFD simulations[D]. Changsha: National University of Defense Technology, 2017. (in Chinese
Wang Miao. Mesh partitioning for parallel iterative computation in CFD simulations[D]. Changsha: National University of Defense Technology, 2017. (in Chinese)
|
| [7] |
Fujii K. Progress and future prospects of CFD in aerospace: Wind tunnel and beyond[J]. Progress in Aerospace Sciences, 2005, 41(6): 455-470. doi: 10.1016/j.paerosci.2005.09.001
|
| [8] |
Zhong Shihao, Zhang Zhishuang, Zheng Mingyuan. A review: application of machine learning in flow field prediction in aeroengine engineering[J]. Advances in Engineering Innovation, 2024, 12(1): 12-26. doi: 10.54254/2977-3903/12/2024123
|
| [9] |
谢晨月, 袁泽龙, 王建春, 等. 基于人工神经网络的湍流大涡模拟方法[J]. 力学学报, 2021, 53(1): 1-16. Xie Chenyue, Yuan Zelong, Wang Jianchun, et al. Artificial neural network-based subgrid-scale models for large-eddy simulation of turbulence[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 1-16. (in Chinese
Xie Chenyue, Yuan Zelong, Wang Jianchun, et al. Artificial neural network-based subgrid-scale models for large-eddy simulation of turbulence[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 1-16. (in Chinese)
|
| [10] |
张帆, 张恒, 李卓越, 等. 基于深度学习方法的圆柱绕流实验缺失数据重构[J]. 物理学报, 2025, 74(7): 154-166. Zhang Fan, Zhang Heng, Li Zhuoyue, et al. Reconstruction of gappy data in cylindrical flow experiments based on deep learning method[J]. Acta Physica Sinica, 2025, 74(7): 154-166. (in Chinese
Zhang Fan, Zhang Heng, Li Zhuoyue, et al. Reconstruction of gappy data in cylindrical flow experiments based on deep learning method[J]. Acta Physica Sinica, 2025, 74(7): 154-166. (in Chinese)
|
| [11] |
杨茂桃, 梁爽, 易淼荣, 等. 基于深度神经网络的超声速隔离段湍流涡黏性系数辨识[J]. 航空动力学报, 2023, 38(2): 312-324. Yang Maotao, Liang Shuang, Yi Miaorong, et al. Identification of turbulence eddy viscosity coefficient in supersonic isolation section based on deep neural network[J]. Journal of Aerospace Power, 2023, 38(2): 312-324. (in Chinese doi: 10.13224/j.cnki.jasp.20220168
Yang Maotao, Liang Shuang, Yi Miaorong, et al. Identification of turbulence eddy viscosity coefficient in supersonic isolation section based on deep neural network[J]. Journal of Aerospace Power, 2023, 38(2): 312-324. (in Chinese) doi: 10.13224/j.cnki.jasp.20220168
|
| [12] |
Sarghini F, De Felice G, Santini S. Neural networks based subgrid scale modeling in large eddy simulations[J]. Computers & Fluids, 2003, 32(1): 97-108. doi: 10.1016/S0045-7930(01)00098-6
|
| [13] |
Duru C, Alemdar H, Baran Ö U. CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils[J]. Neural Computing and Applications, 2021, 33(12): 6835-6849. doi: 10.1007/s00521-020-05461-x
|
| [14] |
Zhou Jianbo, Zhang Rui, Chen Lyu. Prediction of flow field over airfoils based on transformer neural network[J]. International Journal of Computational Fluid Dynamics, 2023, 37(2): 167-180. doi: 10.1080/10618562.2023.2259806
|
| [15] |
Song Junyong, Wang Lei, Xin Zhiqiang, et al. Wake field prediction of a wind farm based on a physics-informed neural network with different spatiotemporal prediction performance improvement strategies[J]. Theoretical and Applied Mechanics Letters, 2025, 15(2): 100577. doi: 10.1016/j.taml.2025.100577
|
| [16] |
Fukami K, Maulik R, Ramachandra N, et al. Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning[J]. Nature Machine Intelligence, 2021, 3(11): 945-951. doi: 10.1038/s42256-021-00402-2
|
| [17] |
Wang Tongsheng, Xi Guang, Sun Zhongguo, et al. The prediction of external flow field and hydrodynamic force with limited data using deep neural network[J]. Journal of Hydrodynamics, 2023, 35(3): 549-570. doi: 10.1007/s42241-023-0042-y
|
| [18] |
Linese D J, Stengel R F. Identification of aerodynamic coefficients using computational neural networks[J]. Journal of Guidance, Control, and Dynamics, 1993, 16(6): 1018-1025. doi: 10.2514/6.1992-172
|
| [19] |
Yang Zhishuang, Dong Yidao, Deng Xiaogang, et al. AMGNET: multi-scale graph neural networks for flow field prediction[J]. Connection Science, 2022, 34(1): 2500-2519. doi: 10.1080/09540091.2022.2131737
|
| [20] |
Wu Haizhou, Liu Xuejun, An Wei, et al. A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils[J]. Computers & Fluids, 2020, 198: 104393. doi: 10.1016/j.compfluid.2019.104393
|
| [21] |
闫盼盼, 牛青林, 高文强, 等. 基于卷积神经网络的地面尾喷焰流场预测[J]. 力学学报, 2024, 56(4): 980-990. Yan Panpan, Niu Qinglin, Gao Wenqiang, et al. Prediction of ground rocket exhaust plume flow field based on convolutional neural network[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(4): 980-990. (in Chinese
Yan Panpan, Niu Qinglin, Gao Wenqiang, et al. Prediction of ground rocket exhaust plume flow field based on convolutional neural network[J]. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(4): 980-990. (in Chinese)
|
| [22] |
陈海昕, 邓凯文, 李润泽. 机器学习技术在气动优化中的应用[J]. 航空学报, 2019, 40(1): 522480. Chen Haixin, Deng Kaiwen, Li Runze. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 522480. (in Chinese
Chen Haixin, Deng Kaiwen, Li Runze. Utilization of machine learning technology in aerodynamic optimization[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(1): 522480. (in Chinese)
|
| [23] |
Azad R, Aghdam E K, Rauland A, et al. Medical image segmentation review: the success of U-net[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10076-10095. doi: 10.1109/TPAMI.2024.3435571
|
| [24] |
Ribeiro M D, Rehman A, Ahmed S, et al. Deep CFD: efficient steady-state laminar flow approximation with deep convolutional neural networks[EB/OL]. (2020-04-19)[2026-04-14]. https://arxiv.org/abs/2004.08826
|
| [25] |
奕建苗, 邓枫, 覃宁, 等. 快速预测跨声速流场的深度学习方法[J]. 航空学报, 2022, 43(11): 526747. Yi Jianmiao, Deng Feng, Qin Ning, et al. Fast prediction of transonic flow field using deep learning method[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(11): 526747. (in Chinese
Yi Jianmiao, Deng Feng, Qin Ning, et al. Fast prediction of transonic flow field using deep learning method[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(11): 526747. (in Chinese)
|
| [26] |
Zhou Ziheng, He Bijiao, Cai Guobiao, et al. Real-time vacuum plume flow field reconstruction during lunar landings based on deep learning[J]. Physics of Fluids, 2024, 36(7): 077112. doi: 10.1063/5.0212949
|
| [27] |
Hinton E G, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647
|
| [28] |
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham: Springer, 2015: 234-241.
|
| [29] |
Wang Fuyong, Zai Yun. Image segmentation and flow prediction of digital rock with U-Net network[J]. Advances in Water Resources, 2023, 172: 104384. doi: 10.1016/j.advwatres.2023.104384
|
| [30] |
Angsali K, Krishnamurthy V R, Hasnain Z. Generalizability of convolutional encoder-decoder networks for aerodynamic flow-field prediction across geometric and physical-fluidic variations[J]. Journal of Mechanical Design, 2021, 143(5): 051704. doi: 10.1115/1.4048221
|