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  1. Pepiot, Perrine (Ed.)
    The Global Pathway Analysis (GPA) algorithm helps analyze the chemical kinetics of complex combustion systems by identifying important global reaction pathways connecting a source species to a sink species through various important intermediate species (i.e., hub species). The present work aims to extend GPA algorithm to plasma-assisted combustion and fuel reforming systems to identify the dominant global pathways in such systems at various conditions. In addition, the present study extends the ability of GPA algorithm to identify reaction cycles involving the excitation of high-concentration species (e.g., O2, N2, and fuel) to their vibrational and electronic states and the subsequent de-excitation to their ground state, based on their significance on the reactivity of plasma-assisted systems in terms of gas heating and radical production. Provisions are made in the GPA algorithm to evaluate the reactivity of identified reaction pathways and cycles based on the element-flux transfer (i.e., dominance), heat release, and radical production rate. The newly developed Plasma-based Global Pathway Analysis (PGPA) algorithm is then used to analyze the plasma-assisted combustion of ammonia and reforming of methane. The PGPA analyses elucidated the significance of vibrational-translational cycles on the reactivity of NH3/air mixtures. Further, analyses on the production of NO ascribed the early reforming of NH3 to N2 and H2 in impeding the production of NO during plasma-assisted NH3 ignition. Lastly, the enhanced reforming of CH4/N2 mixtures using plasma has been attributed to electron impact dissociation of CH4 when compared to thermal reforming. In contrast, conventional path-Flux analysis (PFA) was found to require significant manual effort and pre-analysis intuitions from expert knowledge, making it arduous to provide valuable insights into plasma chemistry. The user-friendly and automated nature of PGPA thus provides a valuable tool for assessing the kinetics of plasma-assisted systems helpful in analyzing and, further, a foundation in reducing plasma-assisted chemistry, without the needs of expert knowledge. 
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  2. Coagulation is a key factor governing the size distribution of nanoclusters during the high temperature synthesis of metal oxide nanomaterials. Population balance models are strongly influenced by the coagulation rate coefficient utilized. Although simplified coagulation models are often invoked, the coagulation process, particularly for nanoscale particles, is complex, affected by the coagulating nanocluster sizes, the surrounding temperature, and potential interactions. Toward developing improved models of nanocluster and nanoparticle growth, we have developed a neural network (NN) model to describe titanium dioxide (TiO 2 ) nanocluster coagulation rate coefficients, trained with molecular dynamics (MD) trajectory calculations. Specifically, we first calculated TiO 2 nanocluster coagulation probabilities via MD trajectory calculations varying the nanocluster diameters from 0.6 to 3.0 nm, initial relative velocity from 20 to 700 m s −1 , and impact parameter from 0.0 to 8.0 nm. Calculations consider dipole–dipole interactions, dispersion interactions, and short-range repulsive interactions. We trained a NN model to predict whether a given set of nanocluster diameters, impact parameter, and initial velocity would lead to the outcome of coagulation. The accuracy between the predicted outcomes from the NN model and the MD trajectory calculation results is >95%. We subsequently utilized both the NN model and MD trajectory calculations to examine coagulation rate coefficients at 300 and 1000 K. The NN model predictions are largely within the range 0.65–1.54 of MD predictions, and importantly NN predictions capture the local minimum coagulation rate coefficients observed in MD trajectory calculations. The NN model can be directly implemented in population balances of TiO 2 formation. 
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  3. To achieve high power density and thermodynamic cycle efficiency, the working pressures of liquid-propellant rocket engines, diesel engines, and gas turbines (based on deflagration or detonation) are continuously increasing, which could reach or go beyond the thermodynamic critical pressure of the liquid propellant. For this reason, the studies of trans- and super-critical injection are getting more and more attention. However, the simulation of transcritical phase change is still a challenging topic. The phase boundary, especially near the mixture critical point, needs to be accurately determined to investigate the multicomponent effects on transcritical injection and atomization. This work used our previously developed thermodynamic model based on the vapor-liquid equilibrium (VLE) theory, which can predict the phase separation near the mixture critical point. An \textit{in situ} adaptive tabulation (ISAT) method was developed to accelerate the computationally expensive multicomponent VLE computation such that it can be cheap enough for CFD. The new thermodynamic model was integrated into OpenFOAM to develop a VLE-based CFD solver. In this work, shock-droplet interaction and two-phase mixing simulations are conducted using our new VLE-based CFD solver. The shock-droplet interaction simulation results capture the thermodynamic condition of the surface entering the supercritical state after shock passes through. The atomization of droplets could be triggered by vorticity formed at the droplets' surface. 2D temporal mixing layer simulations show the evolution of the transcritical mixing layer and capture the phase split effect at the mixing layer. 
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  4. Natural gas associated with oil wells and natural gas fields is a significant source of greenhouse gas emissions and airborne pollutants. Flaring of the associated gas removes greenhouse gases like methane and other hydrocarbons. The present study explores the possibility of enhancing the flaring of associated gas mixtures (C1 – C4 alkane mixture) using nanosecond pulsed non-equilibrium plasma discharges. Starting with a detailed chemistry for C0 – C4 hydrocarbons (Aramco mechanism 3.0 – 589 species), systematic reductions are performed to obtain a smaller reduced mechanism (156 species) yet retaining the relevant kinetics of C1 – C4 alkanes at atmospheric pressure and varying equivalence ratios (φ = 0.5 – 2.0). This conventional combustion chemistry for small alkanes is then coupled with the plasma kinetics of CH4, C2H6, C3H8, and N2, including electron-impact excitations, dissociations, and ionization reactions. The newly developed plasma-based flare gas chemistry is then utilized to investigate repetitively pulsed non-equilibrium plasma-assisted reforming and subsequent combustion of the flare gas mixture diluted with N2 at different conditions. The results indicate an enhanced production of hydrogen, ethylene and other species in the reformed gas mixture, owing to the electron-impact dissociation pathways and subsequent H-abstractions and recombination reactions, thereby resulting in a mixture of CH4, H2, C2H4, C2H2, and other unsaturated C3 species. The reformed mixture shows an enhanced reactivity as exhibited by their shorter ignition delays. The reformed mixture is also observed to undergo increased methane destruction and higher equilibrium temperatures compared to the original mixture as the gas temperature increases, thereby exhibiting a potential for reducing the unburnt emissions of methane and other hydrocarbons. 
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  5. Nanosecond Pulsed High Frequency Discharges (NPHFD) are gaining popularity over conventional spark and arc discharges as they have been shown to increase energy efficiency, enhance ignition probability and sustained kernel growth, and offer more flexibility and control for ignition applications under various conditions. Hence, it is important to determine the impact of different factors such as the optimal pulse energy, background flow conditions, inter-pulse time, mixture equivalence ratio, etc. on the success of ignition of premixed mixtures with NPHFD. This work presents a numerical investigation of the morphology of ignition kernel development with both single-pulse and multiple-pulse discharges. Nanosecond non-equilibrium plasma discharges are modeled between pin-pin electrodes in a subsonic ignition tunnel with quiescent and flowing premixed mixtures of methane and air. Large eddy simulations (LES) are conducted to investigate the reasons for successful and failed ignition in different scenarios. A single pulse discharge in the presence of electrodes, in a quiescent medium, elucidates the gas recirculation pattern caused by the plasma pulse which results in a separated toroidal kernel from the primary ignition kernel between the electrodes. Convection heat loss to the mean flow results in quenching of the high temperature, radical-rich hot-spots creeping on the electrode walls, and leaving only the semi-toroidal kernel to propagate downstream. Finally, simulations with multiple pulses with different inter-pulse times have been conducted to analyze the synergistic effect of overlapping kernels with high temperature and OH concentration, which has been attributed as the primary reason for higher ignition probabilities in the “fully coupled” regime reported in the experiments. Successful ignition kernel formation is reported with 3 pulses at a pulse repetition frequency of 300 kHz in the fully coupled regime. This kernel volume was almost 4 times, and develops in two-thirds the time, compared to the ignition kernel volume formed by the single pulse discharge with the same total energy. Ten pulses with twice as much total energy were deposited at a much lower frequency of 2 kHz, which resulted in disjoint hot-spots that fail to form an ignition kernel in the decoupled regime. 
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  6. For energy-assisted compression ignition (EACI) engine propulsion at high-altitude operating conditions using sustainable jet fuels with varying cetane numbers, it is essential to develop an efficient engine control system for robust and optimal operation. Control systems are typically trained using experimental data, which can be costly and time consuming to generate due to setup time of experiments, unforeseen delays/issues with manufacturing, mishaps/engine failures and the consequent repairs (which can take weeks), and errors in measurements. Computational fluid dynamics (CFD) simulations can overcome such burdens by complementing experiments with simulated data for control system training. Such simulations, however, can be computationally expensive. Existing data-driven machine learning (ML) models have shown promise for emulating the expensive CFD simulator, but encounter key limitations here due to the expensive nature of the training data and the range of differing combustion behaviors (e.g. misfires and partial/delayed ignition) observed at such broad operating conditions. We thus develop a novel physics-integrated emulator, called the Misfire-Integrated GP (MInt-GP), which integrates important auxiliary information on engine misfires within a Gaussian process surrogate model. With limited CFD training data, we show the MInt-GP model can yield reliable predictions of in-cylinder pressure evolution profiles and subsequent heat release profiles and engine CA50 predictions at a broad range of input conditions. We further demonstrate much better prediction capabilities of the MInt-GP at different combustion behaviors compared to existing data-driven ML models such as kriging and neural networks, while also observing up to 80 times computational speed-up over CFD, thus establishing its effectiveness as a tool to assist CFD for fast data generation in control system training.

     
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  7. To achieve high performance, the working pressure of liquid-fueled rocket engines, diesel engines, and gas turbines (based on deflagration or detonation) is continuously increasing, which could reach the thermodynamic critical pressure of the liquid fuel. For this reason, the studies of trans- and super-critical injection are getting more attention. However, most of the multiphase researches were mainly concentrated on single- or two-component systems, which cannot capture the multicomponent phase change in real high-pressure engines and gas turbines. The phase boundary, especially near the critical points, needs to be accurately determined to investigate the multicomponent effects in transcritical flow. This work used our previously developed thermodynamic model based on the vapor-liquid equilibrium (VLE) theory, which can predict the phase separation near the critical points. An in situ adaptive tabulation (ISAT) method was developed to accelerate the computation of the VLE model such that the expensive multicomponent VLE calculation can be cheap enough for CFD. The new thermodynamic model was integrated into OpenFOAM to build a VLE-based CFD solver. In this work, simulations are conducted using our new VLE-based CFD solver to reveal the phase change effects in transcritical flow. Specifically, shock-droplet interaction are investigated to reveal the shock-driven high pressure phase change. 
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