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A previously developed set of kinetic model equations for a chemically-reacting gas is modified. By examining closely the H theorem, a new set of constraints is obtained. These conditions are then used to determine the inelastic collision parameters proposed in the model. The kinetic equations so obtained are able to produce exactly the same rate equations as prescribed by the actual chemical reactions.
Catalytic Fast Pyrolysis (CFP) is a process that converts biomass into liquid intermediates suitable for transportation fuels by rapidly heating it in the presence of a catalyst, aiming to produce stable oils with reduced oxygen content. During CFP, the catalyst can become deactivated by the accumulation of coke, a carbon-rich deposit formed from the decomposition of biomass components. Unlike in petroleum refining, regenerating coked catalysts from biomass pyrolysis requires specific approaches due to the different chemical nature of the coke formed. An experimental technique, Temperature Programmed Oxidation (TPO), was used to study the de-coking process by gradually increasing temperature while monitoring the production of CO and CO2, which provides data for kinetic modeling. Utilizing data from TPO experiments, coke combustion kinetic model was developed to describe the rate of coke removal at different temperatures, allowing for simulation of regeneration processes. Then kinetic model is integrated into MFIX-Exa for the simulation of Biogenic Fluid Catalytic Cracker (BFCC) regenerator at different scales, enabling analysis of catalyst flow, temperature distribution, and regeneration efficiency under various operating conditions.
This work seeks to characterize the fidelity needed in a gasoline surrogate with the intent to replicate the complex autoignition behavior exhibited within advanced combustion engines, and specifically Homogeneous Charge Compression Ignition (HCCI). A low-temperature gasoline combustion (LGTC) engine operating in HCCI mode and a rapid compression machine (RCM) are utilized to experimentally quantify fuel reactivity, through autoignition and preliminary heat release characteristics. Fuels considered include a research grade E10 U.S. gasoline (RD5-87), three multi-component surrogates (PACE-1, PACE-8, PACE-20), and a binary surrogate (PRF88.4). Each fuel was studied at lean/HCCI-like conditions covering a wide range of temperatures and pressures that are representative of naturally aspirated to high boost engine operation. Detailed chemical kinetic modeling is also undertaken using a recently updated gasoline surrogate kinetic model to simulate the RCM experiments and to provide chemical insight into surrogate-to-surrogate differences. The LGTC engine experiments demonstrate nearly identical reactivity between PACE-20 and RD5-87 across studied conditions, while faster phasing is seen for both PACE-1 and PACE-8 due to their stronger intermediate- and low-temperature heat release (ITHR/LTHR) at naturally aspirated and boosted conditions, respectively. The RCM experiments reveal typical low-temperature, negative temperature coefficient (NTC) and intermediate-temperature autoignition behaviors at all pressure conditions for RD5-87, which are qualitatively reproduced by all surrogates. Quantitative discrepancies in both autoignition and preliminary heat release are observed for all surrogates, while their ability to replicate RD5-87 autoignition behavior follows the order of PACE-20 > PACE-1 > PACE-8 > PRF88.4. Excellent mapping is obtained between the LGTC engine and the RCM, where the engine pressure-time trajectories can be characterized by the regimes represented by the RCM autoignition isopleths. The kinetic model performs commendably when simulating both autoignition and preliminary heat release of PACE-20, while typically overpredicting ignition delay times for PACE-1, PACE-8 and PRF88.4 at high-pressure and low-temperature/NTC conditions. Sensitivity and rate of production (ROP) analyses highlight surrogate-to-surrogate differences in the governing chemical kinetics where n-pentane initiates rapid OH branching at a faster rate and an earlier timing for PACE-20 than iso-pentane does for PACE-1 and PACE-8, making it computationally more reactive than the other surrogates. The current study highlights the need to include non-standardized properties, such as the lean/HCCI-like autoignition characteristics, in addition to ASTM properties (e.g., RON, MON) as metrics of fuel reactivity and targets to be matched when formulating high-fidelity surrogates that fully capture gasoline advanced combustion behavior such as HCCI-like autoignition.
Herein, we investigate the electrochemical conversion of methane to CO 2 on platinum electrodes under ambient conditions. Through a combination of experimentation, density functional theory (DFT) calculations, and ab initio kinetic modeling, we have developed an improved understanding of the reaction mechanism and the factors that determine catalyst activity. We hypothesized that the rate-determining methane activation step is thermochemical (i.e., CH 4 (g) → CH 3 * + H * ) as opposed to electrochemical based on a fitted barrier of approximately 0.96 eV that possesses minimal potential dependence. We developed a simple kinetic model based on the assumption of thermochemical methane activation as the rate-determining step, and the results match well with experimental data. Namely, the magnitude of the maximum current density and the electrode potential at which it is realized agree with our ab initio kinetic model. Finally, we expanded our kinetic model to include other transition metals via a descriptor-based analysis and found platinum to be the most active catalyst for the oxidation of methane, which is in line with previously published experimental observations.
A kinetic model is developed to predict the influence of temperature and hydrothermal aging on the redox of active Cu sites under standard SCR, NO oxidation and NH 3 oxidation conditions over a practically relevant fully-formulated Cu-SSZ-13 catalyst. NO 2 /N 2 production during NO/NH 3 titration of Cu II sites is utilized to identify rate parameters associated with NO-only RHC (reduction half cycle) and NH 3 -only RHC respectively. Integral N 2 formation during subsequent NO + NH 3 titration is consistent with the production of one NO 2 per two CuII sites reduced during NO-only RHC and one N 2 per six Cu II sites reduced during NH 3 -only RHC. Decreased reduction of Cu II sites by NO/NH 3 upon hydrothermal aging, along with the production of one NO 2 per two CuII sites during NO-only RHC, is accordant with the involvement of proximal ZCuOH and oxygen-bridged dimeric Cu II sites. Oxidation of partially solvated and framework coordinated ZCu (Cu I ) sites occurs in presence of O 2 , does not produce N 2 and can lead to the consumption of Brønsted acid sites. A global OHC kinetic model is developed to predict transient and integral N 2 formation during exposure of CuI sites to a mixture of NO and O 2 . The resulting redox kinetic model quantitatively predicts NO and NH 3 consumption during isothermal transient response Cu redox (TRCR) protocols, along with temperature and age dependent steady-state standard SCR and oxidation conditions. The redox model presented in this work synthesizes recent kinetic, spectroscopic and computational findings to provide a foundational description of active site redox during standard SCR, NO oxidation and NH3 oxidation over Cu-SSZ-13.
A unified overview is presented for chemical kinetics and chemical modeling in planetary atmospheres. The recent major advances in the understanding of the chemistry of the terrestrial atmosphere make the study of planets more interesting and relevant. A deeper understanding suggests that the important chemical cycles have a universal character that connects the different planets and ultimately link together the origin and evolution of the solar system. The completeness (or incompleteness) of the data base for chemical kinetics in planetary atmospheres will always be judged by comparison with that for the terrestrial atmosphere. In the latter case, the chemistry of H, O, N, and Cl species is well understood. S chemistry is poorly understood. In the atmospheres of Jovian planets and Titan, the C-H chemistry of simple species (containing 2 or less C atoms) is fairly well understood. The chemistry of higher hydrocarbons and the C-N, P-N chemistry is much less understood. In the atmosphere of Venus, the dominant chemistry is that of chlorine and sulfur, and very little is known about C1-S coupled chemistry. A new frontier for chemical kinetics both in the Earth and planetary atmospheres is the study of heterogeneous reactions. The formation of the ozone hole on Earth, the ubiquitous photochemical haze on Venus and in the Jovian planets and Titan all testify to the importance of heterogeneous reactions. It remains a challenge to connect the gas phase chemistry to the production of aerosols.
The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.
Energetic particles interact with the plasma surrounding them, resonating with certain types of plasma waves to stabilize them while destabilizing others, and changing the character of the background turbulence in ways that have not been fully quantified or understood. Interaction with the turbulent background plasma is key to the acceleration of many types of energetic particles including high-energy cosmic rays, solar energetic particles, and pick-up ions. The acceleration of particles is a process that would ideally be described by a kinetic model, a type of model that follows a probability distribution function (PDF) for all particles in 7-dimensional (x, y, z, v x , v y , v z , t) space. Because of the high dimensionality of a kinetic model, simulations that solve kinetic equations use the largest computational resources currently available, and are yet unable to simulate a realistic number of particles, reach the large scales necessary for astrophysical problems, and use high-precision numerical methods. Two available alternatives to kinetic plasma models have been explored for this problem, with limited success. One is a multi-fluid model produced by a cumulant discarding closure, which evolves coupled equations for the velocity, magnetic field, and internal energy for both the background plasma and the fluid of energetic particles. However, simulations that solve multi-fluid magnetohydrodynamic (MHD) equations are able to include the interaction with energetic particles only in crude ways, typically as an add-on pressure term. The second alternative is to use a hybrid method to couple a fluid description of the background plasma to a kinetic model or a Fokker–Planck model for the energetic particles. These methods are hampered by the physical modeling of the coupling. In this work, we develop a new model, which follows the PDF for all particles; this can be viewed as a step toward physical realism above a multi-fluid MHD model, while also being more computationally efficient than a kinetic model. The equations we develop model both the background plasma and the energetic particles self-consistently. Over the last decade, similar PDF methods have been developed to a high level of sophistication to model reactive flows and turbulent combustion for engineering applications. For treatment of the feedback of the energetic particles on a background plasma, a PDF closure approach should evaluate the mean characteristics, including the density, with better statistical quality than will particle-sampling procedures.
Nutrient release in clinoptilolite-phosphate rock (Cp-PR) systems occurs through dissolution and cation-exchange reactions. Investigating the kinetics of these reactions expands our understanding of nutrient release processes. Research was conducted to model transport kinetics of nutrient release in Cp-PR systems. The objectives were to identify empirical models that best describe NH4, K, and P release and define diffusion-controlling processes. Materials included a Texas clinoptilolite (Cp) and North Carolina phosphate rock (PR). A continuous-flow thin-disk technique was used. Models evaluated included zero order, first order, second order, parabolic diffusion, simplified Elovich, Elovich, and power function. The power-function, Elovich, and parabolic-diffusion models adequately described NH4, K, and P release. The power-function model was preferred because of its simplicity. Models indicated nutrient release was diffusion controlled. Primary transport processes controlling nutrient release for the time span observed were probably the result of a combination of several interacting transport mechanisms.
Currently, there are several models in the literature, such as kinetic models, microstructural models, and mass transport models that describe a Li-air battery's discharge behavior. Many of these models are calibrated and tested at low current densities and cannot be easily transferred to high current densities. Even at low current densities, there is no quantitative method for a researcher to choose a reaction kinetic model such as classical Butler-Volmer and its derivatives, and modified Marcus-Hush-Chidsey, a resistance model for lithium peroxide such as electron transport via tunneling or linear resistivity, a surface coverage model (lithium peroxide growth) such as partial coverage or full coverage, and mass transport model (discussed in Ref. [1]). Also, it is time-consuming to test different models at high current density (1C) due to a lack of well-tested models and well-calibrated model parameters. For this presentation, we will develop an analytical model, which acts as a surrogate model for a sophisticated finite element model to predict discharge time and discharge voltage. Next, we use an uncertainty quantifying technique called reduced-order stochastic optimization [2, 3] to determine the uncertainty in model parameters for rate kinetics, lithium peroxide resistivity, and parasitic resistance. Finally, a finite element simulation is performed to determine the error introduced by the surrogate model and its influence on the uncertainty in the model parameters.
In natural environments, kinetics of As(V) sequestration/release is usually coupled with the dynamic iron mineral transformation, which is further influenced by the presence of the natural organic matter (NOM). However, both quantitative and mechanistic understanding of this process is still lacking. In this study, we investigated the effect of humic acids (HA), a major component of NOM, on the coupled kinetics of ferrihydrite transformation into hematite/goethite and sequestration of As(V) in iron minerals. Time-resolved As(V) and HA interactions with iron minerals were studied in stirred-flow kinetic experiments, using aberration-corrected scanning transmission electron microscopy (STEM), and X-ray adsorption spectroscopy. Based on the experimental results, we developed a mechanistic kinetics model for As(V) reactions with iron minerals with varying iron mineral compositions and solution chemistry. Our results showed that HA slowed ferrihydrite transformation and the rates of As(V) speciation changes within iron minerals, while HA did not incorporate into iron minerals. Our kinetics model is able to account for the variations of iron mineral compositions, solution chemistry, and As(V) speciation, which has significant environmental implications on predicting the As(V) behavior in the environment.
To overcome the limitations of thermodynamic defect models, we introduce a multilevel kinetic approach to simulate the activation of p doping in CdTe by group V elements using arsenic as a “model” dopant. On the lowest level, we calculate thermodynamic and kinetic parameters of point defects, complexes, and reactions from first principles. On the intermediate level, we use these parameters to calculate the kinetic rates of defect reactions. Finally, we simulate the time evolution of defects and free carriers. Our results show the importance of kinetic factors in defect chemistry models. We reveal the primary arsenic activation pathway to be a fast reaction in which the tellurium atoms get kicked out and replaced on the regular anion sites by interstitial arsenic species. We discover the important role of (As i As Te ) and (As i As i ) complexes that arise during activation anneal to form kinetically stabilized transient states that not only compensate the doping but also can produce deep recombination levels. Lastly, we expect that our modeling approach and the gained insight into the atomic processes behind the doping formation will advance the defect chemistry modeling of electronic properties of materials.
The work performed under this award resulted in the development of a computationally-efficient kinetic model to simulate the chemistry, thermodynamics, and microphysics of organic aerosol. Versions of the kinetic model were used to study a suite of aerosol processes including, multigenerational gas-phase chemistry, phase-state influenced gas/particle partitioning, heterogeneous oxidation, oligomerization reactions, and experimental artifacts. The kinetic model has now been implemented into a regional chemistry-climate model (WRF-Chem) to further study the formation, evolution, properties, and spatiotemporal distribution of organic aerosol at urban, regional, and global scales.
New powertrain solutions are needed to address societal challenges stemming in part from a transportation sector which relies heavily on combustion of conventional hydrocarbon fuels like gasoline. One way to advance new solutions is through predictive simulations. Predictive simulations can potentially limit the extent of experimental validations, reduce time spent in engineering design cycles, unlock new strategies for high efficiency combustion with power density, and minimize tailpipe emissions. However, the simulation tools currently available are either too computationally expensive, inadequate in their accuracy, or a combination of the two. A DOE-funded consortium of national laboratories called the Partnership to Advance Combustion Engines (PACE) seeks to address this gap by rapidly delivering new knowledge and tools which enable market-competitive powertrain solutions for light-duty vehicles. Approaches to modeling combustion in powertrain systems typically incorporate computational fluid dynamics (CFD) simulations with chemical kinetic models. These CFD simulations generally use surrogate fuels featuring a limited number of existing components in a reduced kinetic model to limit computational costs. Such surrogate fuels often sacrifice matching several or more combustion and physical properties of the target gasoline fuel. Validation of surrogate fuels is also often only pursued for a small subset of standard metrics such as research or motor octane number (RON, MON). The purpose of this project is to develop an optimal E10 gasoline surrogate fuel suitable for tasks across PACE to facilitate rapid common analysis and progression toward consortium goals. This project received contributions from tasks led by eight PACE principal investigators. The project funding here reflects all tasks under Pitz, whose tasks also contributed to additional PACE projects. Based on the optimal gasoline surrogate fuel composition, a reduced chemical kinetic model will be shared with PACE researchers, industry, and the broader combustion community.
A multilesion cell kinetic model is derived, and radiation kinetic coefficients are related to the Katz track structure model. The repair-related coefficients are determined from the delayed plating experiments of Yang et al. for the C3H10T1/2 cell system. The model agrees well with the x ray and heavy ion experiments of Yang et al. for the immediate plating, delaying plating, and fractionated exposure protocols employed by Yang. A study is made of the effects of target fragments in energetic proton exposures and of the repair-deficient target-fragment-induced lesions.
Olefins, and particularly iso-olefins constitute a significant portion of petroleum-derived gasoline, while they also represent a production route for bio-derived transportation-relevant fuels. Although linear structures have been studied for some time, the combustion behavior of iso-olefins is not as well understood. To appropriately mimic gasoline in fundamental tests and in computational models, this lack of understanding must be addressed. Towards this end, rapid compression machine (RCM) experiments were performed for 2-methyl-2-butene (2M2B) and 2-methyl-2-pentene (2M2P) as two representative structures. A detailed chemical kinetic model for these iso-olefins is under development to gain further insight into their autoignition chemistry. In the kinetic model, reaction types and associated rate constants for olefins are less clear than those for n-alkanes. The structural similarity of the two studied iso-olefins allows the development of consistent reaction rate rules to accurately simulate their behavior. The RCM tests cover temperatures from 700 to 1000 K, pressures of 25 and 45 bar, with lean, stoichiometric and rich conditions, and indicate that 2M2P is more reactive than 2M2B due to its longer hydrocarbon backbone. The difference is more extreme at rich conditions. These trends are investigated by the kinetic model where the presence of allylic carbons in the molecule limits the contribution to the low temperature reactivity.
We develop kinetic plasma models of pulsar magnetospheres with magnetic-field-line-dependent plasma injection that reveal the importance of various magnetosphere regions in regulating the gamma-ray emission. We set different particle injection rates for the so-called open, closed, and separatrix zones. Moderate particle injection rates in open and closed zones ensure a global field structure close to the force-free one, while the dissipation occurs mainly in and around the equatorial current sheet. The particles injected in the separatrix zone affect the particle populations that enter the equatorial current sheet region and, therefore, the corresponding accelerating electric fields, particle energies, the spectral cutoff energy, and gamma-ray efficiency. The separatrix zone models reproduce the recently discovered fundamental plane of gamma-ray pulsars consistent with curvature radiation emission, the gamma-ray light-curve shapes, and the radio-lag vs. peak-separation correlation reported in the Fermi second pulsar catalog. The model beaming factors indicate that the pulsar total gamma-ray luminosities listed in the Fermi catalogs are overestimations of the actual ones. We find that the radiation reaction limited regime starts ceasing to govern the high-energy emission for spin-down powers less than 1034 erg/s. Our results also indicate that toward high magnetic inclination angles, the "Y point" around the rotational equator migrates well inside the light cylinder sparking additional peaks in the gamma-ray pulse profiles. We find that an equivalent enhanced particle injection beyond the Y point strengthens these features making the model gamma-ray light curves inconsistent with those observed.
While more efficient vehicles have been developed over the decades, their performance is limited by the properties of existing fuels. In response, the Co-Optimization of Fuels and Engines Initiative (Co-Optima) under the U.S. Department of Energy (DOE) has developed a rigorous screening approach to identify the most promising biomass-derived blendstocks that are suitable for advanced spark-ignition (SI) engines. A detailed kinetic model has also been developed to predict the combustion properties of the selected blendstocks. This kinetic model has been designed mainly targeting ignition and flame propagation properties, and it has not previously been validated for soot formation. In this work, we numerically predicted the sooting tendency of 20 Co-Optima SI blendstocks using the Co-Optima kinetic model. The sooting tendencies are determined quantitatively using the Yield Sooting Index (YSI) methodology. As shown in Fig. 1, the predicted YSIs show good agreement with measurements, except for 2,5-dimethylfuran. We also quantify the sensitivity of the YSI predictions to aromatic growth reactions and fuel decomposition reactions of the test fuels. It is found that PAH growth reactions have little impact on YSIs for all 20 fuels under investigation. On the other hand, fuel decomposition reactions have a significant influence on the YSI of 2,5-dimethylfuran. Perturbing their reaction rates by a factor of two is shown to lead to 11 % YSI prediction uncertainty.