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.

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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

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Unsteady hydrodynamic load reconstruction of seaplane based on deep learning

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