Hyperspectral tomography based on multi-mode absorption spectroscopy (MUMAS)

Abstract

This paper demonstrates a hyperspectral tomographic technique that can recover the temperature and concentration field of gas flows based on multi-mode absorption spectroscopy (MUMAS). This method relies on the recently proposed concept of nonlinear tomography, which can take full advantage of the nonlinear dependency of MUMAS signals on temperature and enables 2D spatial resolution of MUMAS which is naturally a line-of-sight technique. The principles of MUMAS and nonlinear tomography, as well as the mathematical formulation of the inversion problem, are introduced. Proof-of-concept numerical demonstrations are presented using representative flame phantoms and assuming typical laser parameters. The results show that faithful reconstruction of temperature distribution is achievable when a signal-to-noise ratio of 20 is assumed. This method can potentially be extended to simultaneously reconstructing distributions of temperature and the concentration of multiple flame species.

A Quality-Hierarchical Temperature Imaging Network for TDLAS Tomography

“…Since the number of available line-of-sight (LoS) TDLAS measurements is limited in many practical applications when the optical access is restricted [10], the inverse problem involving reconstructing absorbance coefficients is inherently ill-posed, resulting into severe artefacts in the tomographic images. Alternatively, multispectral tomographic algorithms [11,12] can reconstruct temperature distributions by enhancing the sampling in the spectral domain through broadband absorption spectroscopy. However, this kind of methods suffers from extremely high computational cost which can take a few hours only for a single-frame reconstruction.…”