基于深度学习的流场时程特征提取模型

Flow feature extraction models based on deep learning

特征识别是流体力学的重要研究方向,然而在中高雷诺数情况下物体的尾流流场复杂,难以通过传统方法实现特征的提取与识别.深度学习理论与技术的不断发展为复杂流场特征的识别提供了新方法.基于流场时程数据的深度学习模型,本文研究了4种模型对尾流场特征提取与识别的精度,得到了针对流场时程特征提取的高精度新方法.结果表明:所提出的模型能够识别尾流物理时程的不同特征,并通过流场时程实现了目标的外形识别,验证了方法的可行性;同时结果表明基于卷积运算的深度学习模型精度高,适用于流场时程数据的特征分析;深度学习网络结构更深、层间结构复杂的残差卷积网络识别精度最高,是尾流时程分析的高精度算法.本文所提方法从流场物理量时程的角度对流场特征进行了提取与识别,证明了深度学习方法具有较高的识别精度,是研究流场特征的重要途径.

Extraction and recognition of the features of flow field is an important research area of fluid mechanics.However,the wake flow field of object immersed in fluid is complicated in the case of medium-and highReynolds number,thus it is difficult to extract and recognize the key features by using traditional physical models and mathematical methods.The continuous development of deep learning theory provides us with a new method of recognizing the complex flow features.A new method of extracting the features of the flow time history is proposed based on deep learning in this work.The accuracy of four deep learning model for feature recognition is studied.The results show that the proposed model can identify different characteristics of the wake time history and object shapes accurately.Some conclusions can be obtained below(ⅰ)The model based on convolutional layers has higher accuracy and is suitable for analyzing the features of flow time history data.(ⅱ)The residual convolutional network,with a deeper structure and more complex inter-layer structure,has highest accuracy for feature recognition.(ⅲ)The proposed method can extract and recognize the flow features from the perspective of physical quantities time history,which is a high-accuracy method,and it is an important new way to study the features of flow physical quantities.