基于深度学习的螺旋桨动态尾流快速预测

IF 4.1 2区 工程技术 Q1 MECHANICS Physics of Fluids Pub Date : 2024-08-07 DOI:10.1063/5.0220551
Changming Li, Bingchen Liang, Peng Yuan, Qin Zhang, Yongkai Liu, Bin Liu, Ming Zhao
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引用次数: 0

摘要

有效预测螺旋桨尾流对实现螺旋桨设计优化具有重要意义。本研究提出了一种名为螺旋桨尾流卷积神经网络(PWCNN)的深度学习(DL)方法,该方法结合了变压器编码器和扩张卷积块来捕捉尾流的多尺度特征。利用尾流延迟分离涡模拟模型进行了计算流体动力学(CFD)模拟,以生成螺旋桨在 DL 所需的不同运行条件下运行时的大量高保真尾流数据。PWCNN 将上一时间步预测的尾流作为更新输入,并迭代预测未来时间步的尾流,从而实现动态尾流预测。DL 预测结果与 CFD 仿真结果的一致性很好,15 个未来时间步的速度分量平均相对误差小于 2.36%,这证明 PWCNN 能够有效捕捉动态尾流的时空演变特征。此外,PWCNN 还能以合理的精度预测未知运行条件下的尾流动态变化,进一步证实了所提模型在预测螺旋桨尾流时空演变方面的通用性。
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Fast prediction of propeller dynamic wake based on deep learning
Efficiently predicting the wake of propellers is of great importance for achieving propeller design optimization. In this work, the deep learning (DL) method called propeller wake convolutional neural networks (PWCNN) is proposed, which combines the transformer encoder and dilated convolutional block to capture the multi-scale characteristics of wakes. Computational fluid dynamics (CFD) simulations are conducted using the delayed detached eddy simulation model for the wake to generate extensive high-fidelity wake data of the propeller operating under different operating conditions required for DL. PWCNN takes the wake predicted at the previous time step to update input and iteratively predicts the wake at future time steps to achieve dynamic wake prediction. The good agreement between DL prediction and CFD simulation results, with the mean relative error of the velocity components less than 2.36% for 15 future time steps, proves that PWCNN can efficiently capture the spatiotemporal evolution characteristic of dynamic wakes. Furthermore, PWCNN can predict the wake dynamic changes with reasonable accuracy under unseen operating conditions, further confirming the generality of the proposed model in forecasting the spatiotemporal evolution of propeller wake.
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来源期刊
Physics of Fluids
Physics of Fluids 物理-力学
CiteScore
6.50
自引率
41.30%
发文量
2063
审稿时长
2.6 months
期刊介绍: Physics of Fluids (PoF) is a preeminent journal devoted to publishing original theoretical, computational, and experimental contributions to the understanding of the dynamics of gases, liquids, and complex or multiphase fluids. Topics published in PoF are diverse and reflect the most important subjects in fluid dynamics, including, but not limited to: -Acoustics -Aerospace and aeronautical flow -Astrophysical flow -Biofluid mechanics -Cavitation and cavitating flows -Combustion flows -Complex fluids -Compressible flow -Computational fluid dynamics -Contact lines -Continuum mechanics -Convection -Cryogenic flow -Droplets -Electrical and magnetic effects in fluid flow -Foam, bubble, and film mechanics -Flow control -Flow instability and transition -Flow orientation and anisotropy -Flows with other transport phenomena -Flows with complex boundary conditions -Flow visualization -Fluid mechanics -Fluid physical properties -Fluid–structure interactions -Free surface flows -Geophysical flow -Interfacial flow -Knudsen flow -Laminar flow -Liquid crystals -Mathematics of fluids -Micro- and nanofluid mechanics -Mixing -Molecular theory -Nanofluidics -Particulate, multiphase, and granular flow -Processing flows -Relativistic fluid mechanics -Rotating flows -Shock wave phenomena -Soft matter -Stratified flows -Supercritical fluids -Superfluidity -Thermodynamics of flow systems -Transonic flow -Turbulent flow -Viscous and non-Newtonian flow -Viscoelasticity -Vortex dynamics -Waves
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