Simultaneous high-speed imaging of temperature, heat-release rate, and multi-species concentrations in turbulent jet flames

Hyperspectral image reconstruction from colored natural flame luminosity imaging in a tri-fuel optical engine

The detection of chemiluminescence from various radicals and molecules in a hydrocarbon flame can provide valuable information on the rate of local heat release, combustion stability, and combustion completeness. In this study, chemiluminescence from the combustion process is detected using a high-speed color camera within the broadband spectrum of visible light. Whereon, a novel hyperspectral reconstruction approach based on the physically plausible spectral reconstruction (PPSR) is employed to reconstruct the spectral chemiluminescence signals from 400 to 700 nm with a resolution of 10 nm to provide 31 different spectral channels. The reconstructed key chemiluminescence signals (e.g., CH*, CH₂O*, C₂*, and CO₂*) from the color images are further analyzed to characterize the chemical kinetics and combustion processes under engine conditions. The spectral chemiluminescence evolution with engine crank angle is identified to comprehend the effect of H₂ fraction on flame characteristics and combustion kinetics. Additionally, in this study, a detailed kinetic mechanism is adopted to deepen the theoretical understanding and describe the spectral chemiluminescence from H₂/CH₄ and H₂/CH₄/n-dodecane flames at relevant conditions for various species including OH*, CH*, C₂*, and CO₂*. The results indicate that the PPSR is an adequately reliable approach to reconstructing spectral wavelengths based on chemiluminescence signals from the color images, which can potentially provide qualitative information about the evolution of various species during combustion. Here, the reconstructed chemiluminescence images show less than 1% errors compared to the raw images in red, green, and blue channels. Furthermore, the reconstructed chemiluminescence trends of CH*, CH₂O*, C₂*, and CO₂* show a good agreement with the detailed kinetics 0D simulation.

100 kHz CH2O imaging realized by lower speed planar laser-induced fluorescence and deep learning

This paper reports an approach to interpolate planar laser-induced fluorescence (PLIF) images of CH₂O between consecutive experimental data by means of computational imaging realized with convolutional neural network (CNN). Such a deep learning based method can achieve higher temporal resolution for 2D visualization of intermediate species in combustion based on high-speed experimental images. The capability of the model was tested for generating 100 kHz PLIF images by interpolating single and multiple PLIF frames into the sequences of experimental images of lower frequencies (50, 33, 25 and 20 kHz). Results show that the prediction indices, including intersection over union (IoU), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and time averaged correlation coefficient at various axial positions could achieve acceptable accuracy. This work sheds light on the utilization of CNN-based models to achieve optical flow computation and image sequence interpolation, also providing an efficient off-line model as an alternative pathway to overcome the experimental challenges of the state-of-the-art ultra-high speed PLIF techniques, e.g., to further increase repetition rate and save data transfer time.

10 kHz 2D thermometry in turbulent reacting flows using two-color OH planar laser-induced fluorescence

10 kHz two-color OH planar laser-induced fluorescence (PLIF) thermometry was demonstrated in both laminar Hencken flames and turbulent premixed jet flames using two injection-seeded optical parametric oscillators (OPOs) pumped by a high-speed three-legged burst-mode laser. The two burst-mode OPOs generate ∼5mJ/pulse at 282 nm and 286 nm to excite the Q₁(5) and Q₁(14) transitions of the A²Σ⁺←X²Π (1,0) system of OH, respectively. PLIF images were collected simultaneously from each of the two transitions and ratios of intensities from the two images were used to determine local temperatures. Analyses of flame curvature, temperature, and the correlation in time of these two quantities are also discussed. The results from this work are promising for the use of this technique in more complex flow environments and at, potentially, even higher repetition rate.

Combined use of TDLAS and LIBS for reconstruction of temperature and concentration fields

A new technique is developed for reconstructing the temperature and species-concentration fields by employing tunable diode laser absorption spectroscopy (TDLAS) and laser-induced breakdown spectroscopy (LIBS) on axisymmetric combustion fields. For two-line thermometry, the uncertainties in linestrengths of the absorption lines may cause systematic errors in temperature and species concentration estimations. Thus, the radial profiles of water vapor concentration are obtained first using the LIBS, assuming that the combustion is complete; then, the radial temperature profiles are estimated from the radial profiles of absorption coefficient, as reconstructed from the absorbance profiles obtained using the TDLAS. The spectral lines of water vapor at 7185.6 and 7444.36 cm^-1 are selected as the linestrengths show monotonic decreases with the increase in temperature within the measuring temperature range. The radial profiles of temperature and water mole fraction are well-reconstructed, and the measurement error is found to be as low as 3%. The technique yielded higher temperatures compared to the thermocouple, possibly owing to the significant radiative heat loss in the thermocouple data.