<p>  </p> <p>With economies shifting towards net-zero carbon emissions, there is an increased interest in carbon-free energy carriers. Hydrogen is a potential carbon-free energy source. However, it poses several production, infrastructural, and safety challenges. Ammonia blends have been identified as a potential hydrogen carrier and fuel for gas turbine combustion. Partially cracked ammonia mixtures consist of large quantities of hydrogen that help overcome the disadvantages of pure ammonia combustion. The presence of nitrogen in the fuel blends leads to increased NO_x emissions, and therefore lean premixed combustion is necessary to curb these emissions. Understanding the flame features, precursors, and dynamics of blowout of such blends due to lean conditions is essential for stable operation, lean blowout prediction, and control. </p> <p><br></p> <p>In this study, high-fidelity large eddy simulations for turbulent premixed ammonia/hydrogen/nitrogen-air flames in an axisymmetric, unconfined, bluff-body stabilized burner are performed to gain insights into lean blowout dynamics. Partially cracked ammonia (40% NH₃, 45% H₂, and 15% N₂, by volume) is chosen as fuel since its laminar burning velocity is comparable to CH4-air mixtures. A finite rate chemistry model with a detailed chemical kinetic mechanism (36 species and 247 reactions) is utilized to capture characteristics of various species during blowout. A comprehensive study of the flow field and flame structure for a weakly stable burning at an equivalence ratio of 0.5 near the blowout limit is presented. Further, the effects of blowout on the heat release rate, vorticity, distribution of major species, and ignition radicals are studied at four time instances at blowout velocity of 70 m/s. Since limited data is available on turbulent premixed combustion of partially cracked ammonia, such studies are essential in understanding flame behavior and uncertainties with regard to blowout.</p>

<p>The main accomplishments of this research are </p> <p>(1) developed a high fidelity computational methodology based on large eddy simulation to capture lean blowout (LBO) behaviors of different fuels; </p> <p>(2) developed fundamental insights into the combustion processes leading to the flame blowout and fuel composition effects on the lean blowout limits; </p> <p>(3) developed artificial intelligence-based models for early detection of the onset of the lean blowout in a realistic complex combustor. </p> <p>The methodologies are demonstrated by performing the lean blowout (LBO) calculations and statistical analysis for a conventional (A-2) and an alternative bio-jet fuel (C-1).</p> <p>High-performance computing methodology is developed based on the large eddy simulation (LES) turbulence models, detailed chemistry and flamelet based combustion models. This methodology is employed for predicting the combustion characteristics of the conventional fuels and bio-derived alternative jet fuels in a realistic gas turbine engine. The uniqueness of this methodology is the inclusion of as-it-is combustor hardware details such as complex hybrid-airblast fuel injector, thousands of tiny effusion holes, primary and secondary dilution holes on the liners, and the use of highly automated on the fly meshing with adaptive mesh refinement. The flow split and mesh sensitivity study are performed under non-reacting conditions. The reacting LES simulations are performed with two combustion models (finite rate chemistry and flamelet generated manifold models) and four different chemical kinetic mechanisms. The reacting spray characteristics and flame shape are compared with the experiment at the near lean blowout stable condition for both the combustion models. The LES simulations are performed by a gradual reduction in the fuel flow rate in a stepwise manner until a lean blowout is reached. The computational methodology has predicted the fuel sensitivity to lean blowout accurately with correct trends between the conventional and alternative bio-jet fuels. The flamelet generated manifold (FGM) model showed 60% reduction in the computational time compared to the finite rate chemistry model. </p> <p>The statistical analyses of the results from the high fidelity LES simulations are performed to gain fundamental insights into the LBO process and identify the key markers to predict the incipient LBO condition in swirl-stabilized spray combustion. The bio-jet fuel (C-1) exhibits significantly larger CH₂O concentrations in the fuel-rich regions compared to the conventional petroleum fuel (A-2) at the same equivalence ratio. It is observed from the analysis that the concentration of formaldehyde increases significantly in the primary zone indicating partial oxidation as we approach the LBO limit. The analysis also showed that the temperature of the recirculating hot gases is also an important parameter for maintaining a stable flame. If this temperature falls below a certain threshold value for a given fuel, the evaporation rates and heat release rated decreases significantly and consequently leading to the global extinction phenomena called lean blowout. The present study established the minimum recirculating gas temperature needed to maintain a stable flame for the A-2 and C-1 fuels. </p> The artificial intelligence (AI) models are developed based on high fidelity LES data for early identification of the incipient LBO condition in a realistic gas turbine combustor under engine relevant conditions. The first approach is based on the sensor-based monitoring at the optimal probe locations within a realistic gas turbine engine combustor for quantities of interest using the Support Vector Machine (SVM). Optimal sensor locations are found to be in the flame root region and were effective in detecting the onset of LBO ~20ms ahead of the event. The second approach is based on the spatiotemporal features in the primary zone of the combustor. A convolutional autoencoder is trained for feature extraction from the mass fraction of the OH ( data for all time-steps resulting in significant dimensionality reduction. The extracted features along with the ground truth labels are used to train the support vector machine (SVM) model for binary classification. The LBO indicator is defined as the output of the SVM model, 1 for unstable and 0 for stable. The LBO indicator stabilized to the value of 1 approximately 30 ms before complete blowout.