Uncertainty quantifications of calibrating laser-induced incandescence intensity on sooting propensity in a wick-fed diffusion flame burner. (1st April 2021)

• Record Type: Journal Article
• Authors: Yu, Wenbin; Zhao, Feiyang; Yang, Wenming; Zhu, Qiren
• Is Part Of: Fuel. Volume 289(2021)
• Publication Date: 2021-04-01
• Subjects: Laser-induced incandescence (LII) diagnosis -- Soot volume fraction -- Uncertainty quantification -- Bayesian inference -- Probability distribution; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2020.119921
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: Laser-induced incandescence was quantitatively calibrated to soot volume fraction. Soot particles measured by DMS500 were used to quantified soot volume fraction. Bayesian inference was accounted for uncertainties derived from measurement errors. Statistics was responsible to infer probability interval of calibration parameter. 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 fv . To make the calibration independent of optical properties, the fv 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 fv 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.