Tomographic imaging of carbon dioxide in the exhaust plume of large commercial aero-engines.

We report here the first implementation of chemically specific imaging in the exhaust plume of a gas turbine typical of those used for propulsion in commercial aircraft. The method used is chemical species tomography (CST) and the target species is CO2, absorbing in the near-infrared at 1999.4 nm. A total of 126 beams propagate transverse to the plume axis, along 7 m paths in a coplanar geometry, to probe a central region of diameter ≈1.5m. The CO2 absorption spectrum is measured using tunable diode laser spectroscopy with wavelength modulation, using the second harmonic to first harmonic (2f/1f) ratio method. The engine is operated over the full range of thrust, while data are recorded in a quasi-simultaneous mode at frame rates of 1.25 and 0.3125 Hz. Various data inversion methodologies are considered and presented for image reconstruction. At all thrust levels a persistent ring structure of high CO2 concentration is observed in the central region of the measurement plane, with a raised region in the middle of the plume assumed to be due to the engine's boat tail. With its potential to target various exhaust species, the CST method outlined here offers a new approach to turbine combustion research, turbine engine development, and aviation fuel research and development.

Constrained models for optical absorption tomography.

We consider the inverse problem of concentration imaging in optical absorption tomography with limited data sets. The measurement setup involves simultaneous acquisition of near-infrared wavelength-modulated spectroscopic measurements from a small number of pencil beams equally distributed among six projection angles surrounding the plume. We develop an approach for image reconstruction that involves constraining the value of the image to the conventional concentration bounds and a projection into low-dimensional subspaces to reduce the degrees of freedom in the inverse problem. Effectively, by reparameterizing the forward model, we impose, simultaneously, spatial smoothness and a choice among three types of inequality constraints, namely, positivity, boundedness, and logarithmic boundedness in a simple way that yields an unconstrained optimization problem in a new set of surrogate parameters. Testing this numerical scheme with simulated and experimental phantom data indicates that the combination of affine inequality constraints and subspace projection leads to images that are qualitatively and quantitatively superior to unconstrained regularized reconstructions. This improvement is more profound in targeting concentration profiles of small spatial variation. We present images and convergence graphs from solving these inverse problems using Gauss-Newton's algorithm to demonstrate the performance and convergence of our method.