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中文核心期刊

基于时程深度学习的流场特征分析方法

FLUID FEATURE ANALYSIS BASED ON TIME HISTORY DEEP LEARNING

  • 摘要: 流场的特征分析是流体力学的重要研究方向, 传统方法主要根据流动参数量值的大小加以判断, 所得结果受选取的参数形式及主观阈值影响大. 本文提出了基于流场时程深度学习的流动特征分析新方法, 建立了基于自动编码的流动特征提取模型; 采用无监督训练方法充分挖掘流动时程信号中的隐含特征, 进行流场中复杂时序特征的低维表征与特征分析. 开展了ReD = 200的低雷诺数圆柱绕流流场特征识别, 实现了周期层流流场数据的低维表征, 并根据特征编码直接获得了流动特征分布, 得到了合理的结果. 本文所提出的研究方法可为流场特征提取、流动特征分析和流动特征表征等问题的解决提供新的方法与参考.

     

    Abstract: Flow feature analysis is an important research area of fluid mechanics. Traditional feature analysis methods, which mainly based on magnitude of the flow parameter, is greatly affected by the parameter form and subjective threshold. In this paper, a new flow field feature analysis method is proposed based on flow time history deep learning, and flow time history feature extraction analysis framework based on autoencoder is established; The unsupervised training method is used to recognize the hidden complex features in the flow time history signal, and realize the low-dimensional representation and feature analysis of the complex time history features in the flow field. The flow field feature analysis of cylinder with ReD = 200 was carried out, and the low-dimensional characterization of periodic laminar flow field data was obtained. Flow feature distribution was directly obtained according to the latent code, and results obtained proved to be reasonable. The proposed method can provide new methods and references for solving problems such as flow field feature extraction, flow feature analysis and flow feature representation.

     

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