基于尾流时程目标识别的流场参数选择研究

STUDY ON FLOW FIELD PARAMETERS OF WAKE TIME HISTORY TARGET RECOGNITION

浸入流场中的固体壁面会形成高度复杂且具有一定特征的尾流流场,利用尾流所包含的信息对物体的外形特征进行识别具有重要的应用价值.然而,在较高雷诺数情况下尾流流场形态及其时序特征复杂,难以通过传统的数学物理方法对流场信号进行特征的识别与提取.本文提出了基于尾流时程数据深度学习的流场特征提取与分析方法,实现了基于一点的物理量时程进行流场中物体外形的识别;同时,对流场中不同物理参数时程的识别精度与识别结果进行分析与研究,得到适用于目标识别的最优物理量参数.通过对圆柱和方柱的尾流数据研究结果表明,本文提出的基于卷积神经网络的模型具有好的训练收敛性和高的预测精度,能够识别并提取得到时程数据中包含的流场特征,采用流场横向速度时程作为物体外形识别信号的模型准确率高.证明了本方法用于浸入流场中物体外形识别的可行性,是一种目标识别的高精度方法.

Wall immersed in fluid will form highly complex wake flow with specific features.Therefore,the extraction and analysis of flow feature has important research value.However,in the case of high Reynolds number,the wake flow field are complex,so it is difficult to identify and extract the flow features by traditional mathematical and statistical method.In this paper,a new flow field feature extraction and analysis method based on deep learning of wake time history data is proposed,and the shape recognition based on local time history is realized;At the same time,accuracy of different time history parameter is analyzed,and the optimal physical parameters suitable for target recognition are obtained.Research results on the flow field data of cylinder and square cylinder show that the model based on convolution neural network proposed in this paper has good training convergence and high prediction accuracy,and model using transverse velocity time history has highest accuracy.At the same time,it is proved that method proposed in this paper is a new high-precision method for target recognition immersed in fluid.