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Pulsed jet phase-averaged flow field estimation based on neural network approach
Experiments in Fluids ( IF 2.4 ) Pub Date : 2021-03-31 , DOI: 10.1007/s00348-021-03180-0
Céletin Ott , Charles Pivot , Pierre Dubois , Quentin Gallas , Jérôme Delva , Marc Lippert , Laurent Keirsbulck

Abstract

Single hot-wire velocity measurements have been conducted along a three-dimensional measurement grid to capture the flow-field induced by a 45\(^\circ\) inclined slotted pulsed jet. Based on the periodic behavior of the flow, two different estimation methods have been implemented. The first one, considered as the reference baseline, is the conditional approach which consists in the redistribution of the experimental data into space- and time-resolved three-dimensional velocity fields. The second one uses a neural network to estimate 3D velocity fields given spatial coordinates and time. This paper compares the two methods for a complete flow-field estimation based on hot-wire measurements. Results suggest that the neural network is tailored to capture the phase-averaged dynamic response of the jet induced by the actuator, and identify the coherent structures in the flow field. Interesting performances are also observed when degrading the learning database, meaning that neural networks can be used to drastically improve the temporal or spatial resolution of a flow field estimation compared to the experimental data resolution.

Graphic abstract



中文翻译:

基于神经网络的脉冲射流平均流场估计

摘要

已沿着三维测量网格进行了单热线速度测量,以捕获由45 \(^ \ circ \)引起的流场倾斜的开槽脉冲射流。基于流的周期性行为,已经实现了两种不同的估计方法。第一种方法被认为是参考基线,是一种有条件的方法,其中包括将实验数据重新分配到空间和时间分辨的三维速度场中。第二种方法是使用神经网络在给定空间坐标和时间的情况下估算3D速度场。本文比较了两种基于热线测量的完整流场估计方法。结果表明,对神经网络进行了定制,以捕获执行器引起的射流的相位平均动态响应,并确定流场中的相干结构。在降级学习数据库时,还会观察到有趣的表现,

图形摘要

更新日期:2021-04-01
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