Uncertainty quantifications of calibrating laser-induced incandescence intensity on sooting propensity in a wick-fed diffusion flame burner

Abstract

Extensive research has been devoted to engineering analysis in the presence of parameter uncertainties. Meanwhile, parameter estimations with uncertainty quantifications facilitate the reduction of bias and physical unrealistic estimates on interpreting model predictions. In this study, the sooting propensity from wick-fed diffusion flames tested by Jet A-1, diesel and their blended fuels are interpreted, with Laser-induced incandescence (LII) diagnosis to quantitative calibrate the soot volume fraction f . To make the calibration independent of optical properties, the f is directly inferred from particle size distribution measured in flames by the Differential Mobility Spectrometer 500 (DMS500). Thus, the calibration parameter with its uncertainties is therefore qualified with errors that arise from measurements. This study refers to several methodologies with potential estimates of parameter uncertainties for proper interpretation of f by LII diagnosis measurement. Bayesian regression method with Gaussian mixture functions are accounted for calibration parameter uncertainties derived from heteroscedastic measurement errors. And the principal component analysis (PCA) assisted statistical approach is responsible for projecting multivariable datasets into low-dimension space, therefore joint probability distribution would be inferred. As a consequence, probability interval from inferred probability distribution of the calibration parameter is associated with degree of uncertainties, which provides better guidance regarding the applicability and uncertainty of LII diagnosis on soot characteristics. v v v