Nonlinear multispectral tomographic absorption deflection spectroscopy based on Bayesian estimation for spatially resolved multiparameter measurement in methane flame exhaust

Abstract

In aerospace propulsion systems with high temperature, pressure, and distortion, inhomogeneous thermophysical gradients can cause line-of-sight deflection and spectral integral deviation. This introduces nonlinearity and measurement error into spectral models, impacting multiparameter field measurements. To address this, we developed a nonlinear multispectral tomographic absorption deflection spectroscopy (MTADS) technique, based on Bayesian inference, for temperature and gas concentration prediction. Our forward model considers inhomogeneous refractive index gradient and laser deflection, synthesising multispectral laser absorption signal through a gas mixing equation and multipoint ray tracing. Simulation results reveal inhomogeneous parameter distribution can cause non-Gaussian and non-zero mean deviations of the spectral absorption signal. The inverse model increases spectral lines to expand measurement signals under sparse optical arrangements. Combining the measured laser absorption signal with Bayesian statistical information of the estimated parameter field improves measurement robustness and accuracy. The maximum a posteriori (MAP) estimation introduces priors such as experiment samples, computational fluid dynamics data, and diffusive transport-induced spatial smoothing. A proof-of-concept based on methane diffusion flame data demonstrates the effectiveness of MTADS, enabling quantitative flame topology and parameter distribution, more accurate 2D temperature, and concentration imaging. These measurements are crucial for revealing chemical reaction dynamics and monitoring combustion systems.