Image convolution-based experimental technique for flame front detection and dimension estimation: a case study on laminar-to-transition jet diffusion flame height measurement

Abstract

A computationally-supported experimental technique is presented, to measure height of luminous flames, using convolution and density-based spatial clustering for image processing. The experimental setup employs a high-definition camera array to capture flame imagery from 0°, 45° and 90° visualization planes. The volumetric fuel flow was ranged from 350 to 1800 cc/min and images of the resulting flame structure were captured and measured. Results show that output measurements are affected by both the volumetric fuel flow and the visualization plane. Whilst the former is evidently the most significant factor, the latter was found to be relevant, since several flame features, particularly, flame tilt and flickering, are only perceivable through specific visualization planes; attributed to uneven flame structure due non-homogeneous thermal stress. The experimental technique proposed yields an overall statistical tolerance of 1.29 cm and an expanded uncertainty of 0.609 cm (~11.5%). From these results, the test is considered successful and the proposed experimental technique is deemed to be on par with the already existing ones. The utilization of spatial density clustering of image gridded data has only been tested for this implementation; being severely constrained by the sample size and density variability of the data. Consequently, care should be exercised. Nonetheless, this approach was found to inherently recognize flame front edge, and mitigate variations of pixel value due change in flame intensity, normalizing image processing, hence, it is proposed as a viable alternative for flame feature/structure visualization and estimation, and further research is encouraged.

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