基于深度学习的水上飞机非定常水载荷重构

doi:10.7527/S1000-6893.2024.29882

Abstract

Abstract:

Holographic hydrodynamic load distribution is of important significance in assessing the hydrodynamic performance of a seaplane， while model testing is a common method to obtain flow field data in the seaplane design.However，the hydrodynamic load test can only obtain a limited amount of sensor data with insufficient accuracy， thereby necessitating the holographic flow field reconstruction. Nevertheless， the hydrodynamic load data is highly nonlinear and sparse， resulting in difficult application of the traditional flow field reconstruction method. We use Temporal Convolutional Network （TCN） to model the time-sequential flow field reconstruction problem of the seaplane entering the water at the bottom of the ship， learn the flow field law through the excellent nonlinear fitting ability of deep learning， and propose the reconstruction loss of a fusion diffusion model for the sparsity of the samples on the basis of the traditional TCN to improve the prediction accuracy of the neural network. The training set is used to train the diffusion model，then the trained diffusion model is fused into the training process of the TCN， and connected to the output of theTCN，while the reconstruction error is calculated. The constraints are imposed on the training of the TCN to improve the reconstruction performance of the flow field. This paper first compares the three models of the traditional TCN， the Gated Recurrent Unit （GRU） and the fully connected network， and verifies the superiority of the TCN in modelling accuracy and generalization ability of non-constant water load reconstruction.The necessity of considering the timing factor in the hydrodynamic load reconstruction through the single-frame reconstruction experiments is illustrated， on the basis of which the validity of the TCN fused with the diffusion model for reconstructing the non-constant flow field is verified. This study provides an effective modelling method for reconstructing the non-constant flow field， acilitating comprehensive assessment of the mechanical properties of the vehicle using model tests.

Key words: unsteady flow field reconstruction, TCN, diffusion model, sparse data, deep learning

CLC Number:

V211.1⁺5

Yunxiang FAN, Huanan AI, Mingzhen WANG, Kai CAO, Xuejun LIU, Hongqiang LYU. Unsteady hydrodynamic load reconstruction of seaplane based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 129882.

Figures/Tables 21

Fig.1

Fig.2

Fig. 3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 1

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Table 2

Fig.15

Fig.16

Fig.17

Fig.18

Table 3

References 22

1	张永杰，崔博，王明振，等. 水陆两栖飞机着水试验与理论分析方法研究进展［J］. 航空学报， 2023， 44（21）： 528665.
	ZHANG Y J， CUI B， WANG M Z， et al. Research progress of amphibious aircraft water landing test and theoretical analysis methods［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（21）： 528665 （in Chinese）.
2	李勐，陈星伊，陈吉昌，等. 波浪情况下民机水上迫降性能数值分析［J］. 航空学报， 2024， 45（2）： 128604.
	LI M， CHEN X Y， CHEN J C， et al. Numerical analysis of civil aircraft ditching performance in wave condition［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（2）： 128604 （in Chinese）.
3	寇家庆，张伟伟，高传强. 基于POD和DMD方法的跨声速抖振模态分析［J］. 航空学报， 2016， 37（9）： 2679-2689.
	KOU J Q， ZHANG W W， GAO C Q. Modal analysis of transonic buffet based on POD and DMD method［J］. Acta Aeronautica et Astronautica Sinica， 2016， 37（9）： 2679-2689 （in Chinese）.
4	DOWELL E H. Eigenmode analysis in unsteady aerodynamics-Reduced-order models［J］. AIAA Journal， 1996， 34（8）： 1578-1583.
5	刘溢浪，张伟伟，蒋跃文，等. 一种基于增量径向基函数插值的流场重构方法［J］. 力学学报， 2014， 46（5）： 694-702.
	LIU Y L， ZHANG W W， JIANG Y W， et al. A reconstruction method for finite volume flow field solving based on incremental radial basis functions［J］. Chinese Journal of Theoretical and Applied Mechanics， 2014， 46（5）： 694-702 （in Chinese）.
6	陈皓，郭明明，田野，等. 卷积神经网络在流场重构研究中的进展［J］. 力学学报， 2022， 54（9）： 2343-2360.
	CHEN H， GUO M M， TIAN Y， et al. Progress of convolution neural networks in flow field reconstruction［J］. Chinese Journal of Theoretical and Applied Mechanics， 2022， 54（9）： 2343-2360 （in Chinese）.
7	LIU B， TANG J P， HUANG H B， et al. Deep learning methods for super-resolution reconstruction of turbulent flows［J］. Physics of Fluids， 2020， 32（2）： 025105.
8	QU X Y， LIU Z J， YU B Y， et al. Predicting pressure coefficients of wing surface based on the transfer of spatial dependency［J］. AIP Advances， 2022， 12（5）： 055225.
9	FUKAMI K， FUKAGATA K， TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning［J］. Journal of Fluid Mechanics， 2019， 870： 106-120.
10	KONG C， CHANG J T， WANG Z A， et al. Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks［J］. AIP Advances， 2021， 11（6）： 065321.
11	GUO M M， CHEN E D， TIAN Y， et al. Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions［J］. Physics of Fluids， 2022， 34（6）： 065111.
12	KASHEFI A， REMPE D， GUIBAS L J. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries［J］. Physics of Fluids， 2021， 33（2）： 027104.
13	SUN L N， WANG J X. Physics-constrained Bayesian neural network for fluid flow reconstruction with sparse and noisy data［J］. Theoretical and Applied Mechanics Letters， 2020， 10（3）： 161-169.
14	HAN R K， WANG Y X， ZHANG Y， et al. A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network［J］. Physics of Fluids， 2019， 31（12）： 127101.
15	HASEGAWA K， FUKAMI K， MURATA T， et al. Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes［J］. Theoretical and Computational Fluid Dynamics， 2020， 34（4）： 367-383.
16	DENG Z W， CHEN Y J， LIU Y Z， et al. Time-resolved turbulent velocity field reconstruction using a long short-term memory （LSTM）-based artificial intelligence framework［J］. Physics of Fluids， 2019， 31（7）： 075108.
17	YU J， HESTHAVEN J S. Flowfield reconstruction method using artificial neural network［J］. AIAA Journal， 2019， 57（2）： 482-498.
18	VAN DEN OORD A， DIELEMAN S， ZEN H G， et al. WaveNet： A generative model for raw audio［DB/OL］. arXiv preprint： 1609.03499， 2016.
19	BAI S J， KOLTER J Z， KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling［DB/OL］. arXiv preprint： 1803.01271， 2018.
20	SOHL-DICKSTEIN J， WEISS E A， MAHESWARANATHAN N， et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics［DB/OL］. arXiv preprint： 1503.03585， 2015.
21	HO J， JAIN A， ABBEEL P. Denoising diffusion probabilistic models［DB/OL］. arXiv preprint： 2006.11239， 2020.
22	HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］∥ 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2016： 770-778.

层	卷积核	膨胀系数	输入维度	输出维度
BasicBlock	3	1	3	64
Bottleneck1	3	3	64	64
Bottleneck2	3	9	64	64
Bottleneck3	3	27	64	64
Bottleneck4	3	81	64	64
Linear			64	1

模型	采样比例/%	R²	MAE
TCN	30	0.940 41	56.02
	20	0.933 35	62.91
	10	0.527 68	166.23
DNN	30	0.946 95	54.94
	20	0.867 95	82.98
	10	-6.208 73	463.15

模型	时刻/s	R²	MAE
改进TCN	0.15	0.987 39	34.29
改进TCN	0.45	0.983 22	38.77
传统TCN	0.15	0.979 20	50.81
传统TCN	0.45	0.975 19	59.71

[1]	Jianyu XU, Li ZHOU, Zhanxue WANG, Jie SHI, Hao SHI. Calculation method for hypersonic plume infrared radiation based on a fast line-by-line calculation model [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(8): 630778-630778.
[2]	Lingjie MENG, Hongguang LI, Xinjun LI. SAR image simulation method guided by geomorphic category information [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 331003-331003.
[3]	Zhihao ZHAO, Zhaohua YANG, Yun WU, Yuanjin YU. Single-photon counting imaging denoising method based on deep learning in low-light environment [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 630531-630531.
[4]	Yiquan WU, Kang TONG. Research advances on deep learning-based small object detection in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(3): 30848-030848.
[5]	Xiaowei JIANG, Yiquan WU. Research progress of UAV aerial image mosaic methods [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(17): 331799-331799.
[6]	Lin CHEN, Xiwen GU, Zhiying CHEN, Zhuo ZHANG, Xiaoliang SUN. High-precision monocular vision pose measurement for large distance span in carrier landing guidance [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 331568-331568.
[7]	Bin SUN, Hang YOU, Wenbo LI, Xiangrui LIU, Jiayi MA. Dual-band payload image fusion and its applications in low-altitude remote sensing [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531343-531343.
[8]	Fanteng MENG, Yong QIN, Jing CUI, Yunpeng WU, Zicheng ZHANG, Shaowei WEI. Unknown risk detection in external environment of railroad using UAV images [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531262-531262.
[9]	Weishi CHEN, Hongchuang NIU, Xin WANG, Jian WAN, Xianfeng LU, Jie ZHANG, Qingbin WANG. Review on multi-source detection technologies for birds and drones in airport clearance area [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 31251-031251.
[10]	Jing WANG, Wei LIU, Hairun XIE, Miao ZHANG, Tuliang MA. Diffusion model-driven multi-objective generative design of supercritical airfoils [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631210-631210.
[11]	Jie LIN, Zhigong TANG, Weiqi QIAN, Yueqing WANG, Peng ZHANG, Weixia XU, Jie LIU. Research progress and prospects of aircraft aerodynamic design based on generative models [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631679-631679.
[12]	Ruitao ZHANG, Cong WANG, Jun TAO, Liyue WANG, Gang SUN. Airfoil parameterization method based on latent diffusion model [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631180-631180.
[13]	Youtao XUE, Shaobo YAO, Yuxin YANG, Yi DUAN, Wenwen ZHAO, Haoge LI. Generalization of three-dimensional flight vehicle shape representation using generative models [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631511-631511.
[14]	Yonghai WANG, Haoge LI, Jiaxin LI, Yi DUAN, Chuan TIAN, Lingxi GUO, Xusheng WU. Rapid aircraft shape generation based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631614-631614.
[15]	Jiaqi LIU, Rongqian CHEN, Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529828-529828.

Unsteady hydrodynamic load reconstruction of seaplane based on deep learning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 21

References 22

Related Articles 15

Recommended Articles

Metrics

Comments