Gaussian process regression for direct laser absorption spectroscopy in complex combustion environments

Abstract

Tunable diode laser absorption spectroscopy (TDLAS) has been proved to be a powerful diagnostic tool in combustion research. However, current methods for post-processing a large number of blended spectral lines are often inadequate both in terms of processing speed and accuracy. The present study verifies the application of Gaussian process regression (GPR) on processing direct absorption spectroscopy data in combustion environments to infer gas properties directly from the absorbance spectra. Parallelly-composed generic single-output GPR models and multi-output GPR models based on linear model of coregionalization (LMC) are trained using simulated spectral data at set test matrix to determine multiple unknown thermodynamic properties simultaneously from the absorbance spectra. The results indicate that compared to typical data processing methods by line profile fitting, the GPR models are proved to be feasible for accurate inference of multiple gas properties over a wide spectral range with a manifold of blended lines. While further validation and optimization work can be done, parallelly composed single-output GPR model demonstrates sufficient accuracy and efficiency for the demand of temperature and concentration inference.