Abstract

Velocity field quantification for high-speed flows is of fundamental importance to understand flow dynamics, turbulence, and flow–structure interactions. Optical velocimetry techniques commonly provide sparse information in the flows. Dense fields of velocity vectors with high spatial resolutions are indispensable for detailed analysis of complex motion patterns and accurate motion tracking within the field of view. In the present work, two-dimensional (2D) Rayleigh scattering imaging (RSI) at a rate of 10- to 100-kHz was utilized to quantify the high-speed flow velocity by employing deep learning-based optical flow analysis, along with density and temperature fields from Rayleigh scattering intensity profiles. High-speed Rayleigh scattering images are highly spatially resolved, have smooth gradients without intensity discontinuities, and precisely track key features of the flows. The deep learning-based optical flow method utilizes recurrent neural network architecture to extract the per-pixel features of both input images, calculate correlation from all pairs of the features, and get training by recurrently updating the optical flow. 2D instantaneous velocity fields of both nonreacting and reacting flows measured by RSI were obtained from deep learning-based optical flow analysis, thus extending RSI as a non-intrusive, nonseeded, and multiscalar measurement technique of high-speed nonreacting and reacting flows.

Graphical abstract

中文翻译：

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基于深度学习的高速流二维瑞利散射成像光流分析

摘要

高速流动的速度场量化对于理解流动动力学、湍流和流动-结构相互作用至关重要。光学测速技术通常提供流动中的稀疏信息。具有高空间分辨率的密集速度矢量场对于复杂运动模式的详细分析和视场内的精确运动跟踪是必不可少的。在目前的工作中，利用基于深度学习的光流分析和密度，采用 10 至 100 kHz 速率的二维 (2D) 瑞利散射成像 (RSI) 来量化高速流速和瑞利散射强度分布的温度场。高速瑞利散射图像具有高度空间分辨率，具有平滑的梯度，没有强度不连续性，并且精确跟踪流动的关键特征。基于深度学习的光流方法利用循环神经网络架构来提取两个输入图像的每像素特征，计算所有特征对的相关性，并通过循环更新光流来进行训练。 RSI 测量的非反应流和反应流的二维瞬时速度场是通过基于深度学习的光流分析获得的，从而将 RSI 扩展为一种非侵入式、非种子式和多标量的高速非反应流和反应流测量技术。

图形概要