Simultaneous application of multi-channel laser-induced incandescence (LII) and shifted vibrational coherent anti-Stokes Raman scattering (SV-CARS) to study sooting flames is demonstrated for the first time. The potential of this diagnostics combination is evaluated on the basis of characterization of soot particles and correlation of soot presence with temperature. For that purpose, a sooting swirl flame operated at three bars has been employed with ethylene as fuel. The novel combination of CARS and time-resolved LII (TiRe LII) enables the estimation of particle size and correlation of this quantity with local gas temperature; simultaneously acquired 2D LII images provide information on the soot distribution in the ambience of the measurement volume which is used by CARS and TiRe LII. Even if the used LII model is approximative in some respect, the detected LII decay times indicate very small particle size throughout the flame relative to an atmospheric laminar diffusion flame which was used for comparison. In most instances, soot presence relates to local gas temperatures in a range between 1600 and 2400 K. Rare soot events at cooler temperatures occur near the nozzle exit and are attributed to transported soot. Comparison of the peak soot temperatures during the LII process shows a significant decrease in the turbulent pressurized flame relative to the laminar atmospheric reference flame. This is attributed to a less-efficient LII heat-up process at turbulent pressurized conditions due to beam steering. The background blackbody temperature, which can be derived by evaluating the signal captured in the different color channels of the LII system towards the end of the LII process, has been identified to be mostly controlled by hotter soot filaments between the laser plane and the detector. Thus, the LII signal tail is not a good measure of the local gas temperature in the measurement volume for this type of configuration.
Tomographic reconstruction of laser absorption of H2O is demonstrated in a swirl-stabilized gas turbine model combustor. Superior reconstruction performance is achieved against conventional methods using a nonlinear regression technique based primarily on convolutional neural networks.
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