A study on application of machine-learning on DBI soot diagnostics

Abstract

Nowadays, Diffused Background-illumination Extinction Imaging technique (DBI) has been widely applied to quantify the in-flame soot formation of diesel-like sprays. In order to eliminate the errors on soot KL value brought from flame radiation, the soot radiation images are usually needed to be recorded between every two successive back light-on pulses. Consequently, it is necessary to do the frame interpolation for the missing flame radiation images and back light-on images. In this study, a Super SloMo machine-learning method was applied to generate the missing frames and the accuracy of interpolated frames was evaluated from three aspects, namely the frame interval length, prediction region and number of intermediate frames. Meanwhile, Gunner Farnebäck method was applied here as a reference. The results show that the Super SloMo method consistently outperformed the Gunner Farnebäck method in terms of accuracy, regardless of the variations on frame intervals, soot regions, as well as interpolated frame numbers. Consequently, the application of the Super SloMo method in conjunction with the DBI technique significantly improved the accuracy of soot KL value, resulting in a 26.4% reduction in average KL error compared to the Gunner Farnebäck method in the studied cases.