基于深度学习的水上飞机非定常水载荷重构

Unsteady hydrodynamic load reconstruction of seaplane based on deep learning

全息水动载荷分布对评估水上飞机的水动性能具有重要意义,模型试验是水上飞机设计中常见的获取流场数据的方法,但水动载荷试验只能获取有限的传感器数据,存在精度不足的问题,因此需要进行全息流场重构。然而水动载荷数据非线性强且数据稀疏,传统的流场重构方法难以适用。采用时序卷积网络(TCN)对水上飞机入水的船底时序流场重构问题进行建模研究,通过深度学习优秀的非线性拟合能力学习流场规律,并在传统的TCN基础上针对样本稀疏性的特点提出了一种融合扩散模型的重构损失以提高神经网络的预测精度。首先,使用训练集对扩散模型进行训练,将训练好的扩散模型作为隐式损失函数计算TCN输出的重构误差,从而融入TCN的训练流程中,对TCN的训练施加约束提高流场重构性能。对比传统TCN、门控循环单元、全连接神经网络3种模型的重构性能,验证了TCN在非定常水载荷时序数据拟合能力、泛化能力的优越性,同时通过单帧重构实验说明了水动载荷重构考虑时序因素的必要性,并在此基础上验证了融合扩散模型的TCN对重构非定常流场的有效性。本文为非定常流场重构提供了一种有效建模方法,有助于利用模型试验全面评估飞行器力学性能。

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.