Tomographic imaging of combustion zones using tunable diode laser absorption spectroscopy (TDLAS)

This work concentrates on enabling the usage of a specific variant of tunable diode laser absorption spectroscopy (abbr. TDLAS) for tomogaphically reconstructing spatially varying temperature and concentrations of gases with as few reconstruction artifacts as possible. The specific variant of TDLAS used here is known as wavelength modulation with second harmonic detection (abbr. WMS-2f) which uses the wavelength dependent absorbance information of two different spectroscopic transitions to determine temperature and concentration values. Traditionally, WMS-2f has generally been applied to domains where temperature although unknown, was spatially largely invariant while concentration was constant and known to a reasonable approximation (_x0006_+/- 10% ). In case of unknown temperatures and concentrations with large variations in space such techniques do not hold good since TDLAS is a “line-of-sight” (LOS) technique. To alleviate this problem, computer tomographic methods were developed and used to convert LOS projection data measured using WMS-2f TDLAS into spatially resolved local measurements. These locally reconstructed measurements have been used to determine temperature and concentration of points inside the flame following a new temperature and concentration determination strategy for WMS-2f that was also developed for this work. Specifically, the vibrational transitions (in the 1.39 microns to 1.44 microns range) of water vapor (H2O) in an axi-symmetric laminar flame issuing from a standard flat flame burner (McKenna burner) was probed using telecom grade diode lasers. The temperature and concentration of water vapor inside this flame was reconstructed using axi-symmetric Abel de-convolution method. The two different sources of errors in Abel’s deconvolution - regularization errors and perturbation errors, were analyzed and strategies for their mitigation were discussed. Numerical studies also revealed the existence of a third kind of error - tomographic TDLAS artifact. For 2D tomography, studies showing the required number of views, number of rays per view, orientation of the view and the best possible algorithm were conducted. Finally, data from 1D tomography was extrapolated to 2D and reconstructions were benchmarked with the results of 1D tomography

Measurement Enhancement on Two-Dimensional Temperature Distribution of Methane-Air Premixed Flame Using SMART Algorithm in CT-TDLAS

In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS (summation of line of sight) and the CSLOS (corrective summation of line of sight) methods have been adopted to increase measurement accuracies. It has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was about 10%

Two dimensional angular domain optical imaging in biological tissues

Optical imaging is a modality that can detect optical contrast within a biological sample that is not detectable with other conventional imaging techniques. Optical trans-illumination images of tissue samples are degraded by optical scatter. Angular Domain Imaging (ADI) is an optical imaging technique that filters scattered photons based on the trajectory of the photons. Previous angular filters were limited to one dimensional arrays, greatly limiting the imaging capability of the system. We have developed a 2D Angular Filter Array (AFA) that is capable of acquiring two dimensional projection images of a sample. The AFA was constructed using rapid prototyping techniques. The contrast and the resolution of the AFA was evaluated. The results suggest that a 2D AFA can be used to acquire two dimensional projection images of a sample with a reduced acquisition time compared to a scanning 1D AFA

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

Tomographic Laser Absorption Imaging of Combustion Gases in the Mid-wave Infrared

This dissertation describes advancements in mid-infrared laser absorption tomography for spatio-temporal measurements of thermochemistry in reacting flows relevant to combustion systems. Tunable laser absorption spectroscopy is combined with tomographic reconstruction techniques to resolve small diameter ( < 1 cm) non-uniform flow fields with steep spatial gradients, leveraging emerging mid-wave infrared photonics. Multiple novel measurement methods, hardware configurations, and image processing techniques were investigated. Initially, a mid-infrared laser absorption tomography sensing method was developed for quantitative measurement of CO and CO2 concentrations and temperature distributions in turbulent premixed jet flames using a translation-stage-mounted optical system. This sensing approach was used to examine effects of varying fuel structure on carbon oxidation over a range of Reynolds number regimes. It was found that spatial and temporal resolution is limited in this method due to the finite laser beam size (~ 1 mm) and the slow mechanical translation of the optical system. To address these limitations, a novel laser absorption imaging (LAI) technique, that expands a single laser beam and replaces the detector with a high-speed infrared camera, was introduced to achieve enhanced spatial and temporal resolution for thermo-chemical imaging. As a demonstration of this new technique, distributions of combustion species were imaged in both axisymmetric and non-axisymmetric flow fields using linear tomography algorithms. For non-axisymetric flows, the limited view tomography problem often results in a blurring effect and artifacts in the reconstructed flow-field. In an effort to address these issues, state-of-the-art deep learning neural networks were developed and applied to solve the limited angle inversion. Initial results suggest that deep neural networks have potential to more accurately predict flame structures with fewer projection angles than linear tomography. This work provides a foundation for a new approach to quantitative time-resolved 3D thermo-chemical imaging in high-temperature reacting flows