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Journal of Combustion
Volume 2017, Article ID 6159802, 17 pages
Research Article

Parametric Study to Improve Subpixel Accuracy of Nitric Oxide Tagging Velocimetry with Image Preprocessing

1Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
2Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India

Correspondence should be addressed to Ravi Teja Vedula; ude.usm.rge@araludev

Received 30 September 2016; Accepted 20 November 2016; Published 20 February 2017

Academic Editor: Guohong Tian

Copyright © 2017 Ravi Teja Vedula et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Biacetyl phosphorescence has been the commonly used molecular tagging velocimetry (MTV) technique to investigate in-cylinder flow evolution and cycle-to-cycle variations in an optical engine. As the phosphorescence of biacetyl tracer deteriorates in the presence of oxygen, nitrogen was adopted as the working medium in the past. Recently, nitrous oxide MTV technique was employed to measure the velocity profile of an air jet. The authors here plan to investigate the potential application of this technique for engine flow studies. A possible experimental setup for this task indicated different permutations of image signal-to-noise ratio (SNR) and laser line width. In the current work, a numerical analysis is performed to study the effect of these two factors on displacement error in MTV image processing. Also, several image filtering techniques were evaluated and the performance of selected filters was analyzed in terms of enhancing the image quality and minimizing displacement errors. The flow displacement error without image preprocessing was observed to be inversely proportional to SNR and directly proportional to laser line width. The mean filter resulted in the smallest errors for line widths smaller than 9 pixels. The effect of filter size on subpixel accuracy showed that error levels increased as the filter size increased.