ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Unsteady hydrodynamic load reconstruction of seaplane based on deep learning
Received date: 2023-11-17
Revised date: 2024-01-24
Accepted date: 2024-02-18
Online published: 2024-03-11
Supported by
Aeronautical Science Foundation of China(2018ZA52002)
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
Yunxiang FAN , Huanan AI , Mingzhen WANG , Kai CAO , Xuejun LIU , Hongqiang LYU . Unsteady hydrodynamic load reconstruction of seaplane based on deep learning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(20) : 129882 -129882 . DOI: 10.7527/S1000-6893.2024.29882
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