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Profiling and characterization of flame radicals by combining spectroscopic imaging and neural network techniques

Profiling and characterization of flame radicals by combining spectroscopic imaging and neural network techniques

Krabicka, Jan, Lu, Gang and Yan, Yong (2011) Profiling and characterization of flame radicals by combining spectroscopic imaging and neural network techniques. IEEE Transactions on Instrumentation and Measurement, 60 (5). pp. 1854-1860. ISSN 0018-9456 (doi:10.1109/TIM.2010.2102411)

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Abstract

This paper presents the development of an instrumentation system for visualizing and characterizing free radicals in combustion flames. The system combines optical splitting, filtering, intensified imaging and image processing techniques for simultaneous and continuous monitoring of specific flame radicals ( OH*, CN*, CH*, and C2*). Computing algorithms are developed to analyze the images and quantify the radiative characteristics of the radicals. Experimental results are obtained from a gas-fired combustion rig to demonstrate the effectiveness of the system. The information obtained by the system is used to establish relationships between radical characteristics and air-to-fuel ratios of combustion gases, helping to obtain an in-depth understanding of burn characteristics.

Item Type: Article
Additional Information: [1] Date of Publication : 31 January 2011. Date of Current Version : 05 April 2011. Issue Date : May 2011.
Uncontrolled Keywords: electron-multiplying charge-coupled device (EMCCD), flame, flame radicals, image processing, neural networks, principal component analysis (PCA)
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Q Science > QD Chemistry
T Technology > TA Engineering (General). Civil engineering (General)
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Computer & Communications Engineering
School of Engineering > Mobile & Wireless Communications Research Laboratory
Related URLs:
Last Modified: 14 Oct 2016 09:17
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/6815

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