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Shadowgraphy is a relatively recent optical diagnostics technique for the analysis of multiphase flows. Shadowgraphy can provide information about the geometric properties of individual particles and measure their velocity, size and shape. Using digital image processing, one can quantify these parameters and measure their joint statistics; however, using machine vision and machine learning methods, further quantification is possible. The formation of so-called tar-tails, secondary products formed by the condensation of primary pyrolysis products, is an important phenomenon in pulverized coal combustion, as the presence of tar-tails alters the radiation properties of pulverized coal flames. In shadow images, tar-tails can be distinguished from coal particles based on their appearance. In this study, we present a machine vision-based methodology to quantitatively measure the amount of tar-tails in shadow images of pulverized coal flames. After the description of the methodology, validation results using a laboratory-scale, piloted oxy-coal burner are presented. The amount of tar-tails was investigated as a function of the oxy-combustion environment in terms of the composition of the combustion environment. It was found that shifting conditions from air combustion towards oxy-combustion significantly suppresses the formation of tar-tails.
International Combustion Symposium
INCOS2018
Szabolcs Stremler
Marton Aczel
Arpad B. Palotas
Pal Toth