Denoising neural network for low-light imaging of acoustically coupled combustion

IF 2.3 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Arin Hayrapetyan, Andres Vargas, Ann R. Karagozian
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引用次数: 0

Abstract

This study investigates the use of a trained neural network (NN) to enable more efficient noise reduction in processing images associated with acoustically coupled combustion phenomena, as compared with more commonly used image processing techniques. The approach is applied to experiments involving high-speed imaging of a single and coaxial methane–air jet diffusion flames exposed to various acoustically resonant environments. Proper orthogonal decomposition (POD) analysis applied to the flame imaging may be used to capture characteristic signatures in the flame dynamics and in verification of the proposed approach in this investigation. The NN trains on low-exposure input images and high-exposure response images for a steadily burning fuel jet with no coaxial flow, yet is remarkably successful when applied to a range of coaxial flow and acoustic excitation conditions. The proposed neural network approach demonstrates a significant decrease in the preprocess time required in analyzing flame images, typically by over a factor of 5, and preserves image quality. The approach replicates POD-based flame dynamics very well, for both low-amplitude and high-amplitude flame responses, the latter involving transitions in the dynamics due to the introduction of multiple timescales. The relative simplicity and success of this NN approach thus show the potential for improved image processing for complex dynamical flows and their transitional features.

弱光声耦合燃烧成像降噪神经网络
与更常用的图像处理技术相比,本研究探讨了使用经过训练的神经网络(NN)在处理与声学耦合燃烧现象相关的图像时能够更有效地降噪。该方法被应用于实验中,涉及暴露在各种声学谐振环境下的单轴和同轴甲烷-空气射流扩散火焰的高速成像。适当的正交分解(POD)分析应用于火焰成像可用于捕捉火焰动力学特征签名和验证本研究中提出的方法。该神经网络在低曝光输入图像和高曝光响应图像上进行训练,用于无同轴流的稳定燃烧的燃料射流,但当应用于一系列同轴流和声激励条件时,它是非常成功的。所提出的神经网络方法表明,在分析火焰图像所需的预处理时间显著减少,通常超过5个因素,并保持图像质量。该方法非常好地复制了基于pod的火焰动力学,对于低振幅和高振幅火焰响应,后者涉及由于引入多个时间尺度而导致的动力学转变。因此,这种神经网络方法的相对简单和成功显示了改进复杂动态流及其过渡特征的图像处理的潜力。
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来源期刊
Experiments in Fluids
Experiments in Fluids 工程技术-工程:机械
CiteScore
5.10
自引率
12.50%
发文量
157
审稿时长
3.8 months
期刊介绍: Experiments in Fluids examines the advancement, extension, and improvement of new techniques of flow measurement. The journal also publishes contributions that employ existing experimental techniques to gain an understanding of the underlying flow physics in the areas of turbulence, aerodynamics, hydrodynamics, convective heat transfer, combustion, turbomachinery, multi-phase flows, and chemical, biological and geological flows. In addition, readers will find papers that report on investigations combining experimental and analytical/numerical approaches.
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