基于混合神经网络的圆柱体周围近壁非稳态流动时空预测模型

IF 2.5 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiang Qiu , Yuanxiang Mao , Bofu Wang , Yuxian Xia , Yulu Liu
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引用次数: 0

摘要

基于密集连接卷积网络(DenseNet)、卷积长短期记忆神经网络(ConvLSTM)和去卷积神经网络(DeCNN)的混合神经网络被用于预测非稳定流场。DenseNet 的使用使模型更加紧凑,并使三维流动预测更加经济实惠。采用 ConvLSTM 预测未来多个时间步骤,提高了预测效率。所提出的模型将速度场和压力场的时间序列转换为统一的时空拓扑结构作为输入,并捕捉时空域中的非线性特征信息。对不同雷诺数的圆柱体周围流动和不同间隙比的圆柱体周围近壁流动进行了数值模拟,并建立了神经网络输入的训练样本。将预测结果与数值模拟结果进行比较,结果显示两者吻合良好。从预测周期可以看出,前三个预测周期都能保持良好的预测结果。对靠近平面壁面的圆柱体周围的三维非稳定流的预测结果显示出显著的准确性,成功地捕捉到了湍流涡旋结构的演变过程。这表明预测模型在捕捉复杂非稳定流的时空变化方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial–temporal prediction model for unsteady near-wall flow around cylinder based on hybrid neural network

A hybrid neural network based on Densely Connected Convolutional Networks (DenseNet), Convolutional Long Short-Term Memory Neural Network (ConvLSTM), and Deconvolutional Neural Network (DeCNN) is employed to predict unsteady flow fields. The utilization of DenseNet makes the model more compact and makes the prediction of three-dimensional flow affordable. The ConvLSTM is implemented to predict multiple future time steps which improves prediction efficiency. The proposed model transforms the time sequences of velocity and pressure fields into uniform spatial–temporal topology as input and captures nonlinear feature information in the spatial–temporal domain. Numerical simulations are conducted for the flow around cylinder at different Reynolds numbers and the near-wall flow around cylinder with different gap ratios, and training samples for the neural network inputs are established. The predicted results are compared with the numerical simulation results, showing good agreement. From the prediction cycle, it can be seen that good prediction results can be maintained in the first three prediction cycles. The prediction results of the three-dimensional unsteady flow around a cylinder near a plane wall, exhibit remarkable accuracy, successfully capturing the evolution of turbulent vortex structures. This signifies that the prediction model is highly effective in capturing the spatial–temporal variations of complex unsteady flows.

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来源期刊
Computers & Fluids
Computers & Fluids 物理-计算机:跨学科应用
CiteScore
5.30
自引率
7.10%
发文量
242
审稿时长
10.8 months
期刊介绍: Computers & Fluids is multidisciplinary. The term ''fluid'' is interpreted in the broadest sense. Hydro- and aerodynamics, high-speed and physical gas dynamics, turbulence and flow stability, multiphase flow, rheology, tribology and fluid-structure interaction are all of interest, provided that computer technique plays a significant role in the associated studies or design methodology.
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