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Kinetic modeling of Guerbet coupling chemistry over a CuMgAlO x catalyst is presented. Integration of the model in a process simulation enables assessing a recycling strategy to optimize the production of diesel fuel precursors.
Experiment and density functional theory (DFT) models are combined to develop a unified, quantitative model of the mechanism and kinetics of fast selective catalytic reduction (SCR) of NO/NO2 mixtures over H-SSZ-13 zeolite. Rates, rate orders, and apparent activation energies collected under differential conditions reveal two distinct kinetic regimes. First-principles thermodynamics simulations are used to determine the relative coverages of free Brønsted sites, chemisorbed NH4+ and physisorbed NH3 as a function of reaction conditions. First-principles metadynamics calculations show that all three sites can contribute to the rate-limiting N-N bond forming step in fast SCR. The results are used to parameterize a kinetic model that encompasses the full range of reaction conditions and recovers observed rate orders and apparent activation energies. Observed kinetic regimes are related to changes in most-abundant surface intermediates. Financial support was provided by the National Science Foundation GAOLI program under award number 1258690-CBET. We thank the Center for Research Computing at Notre
The project goal was to create an energy saving paradigm shift in how polymers are manufactured in the 21st century. It used Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) integrated for the first time with automatic active control to create the innovative ‘ACOMP/Control Interface’, or ‘ACOMP/CI’. ACOMP/CI will begin the transformation from old, inefficient processes into highly evolved, energy and resource efficient ones. The ACOMP platform is broadly applicable to many types of reactions and processes throughout the vast polymer industry. The industry provides materials for sectors such as automotive, aerospace, oil recovery, agriculture, paints, resins, adhesives, pharmaceuticals and therapeutic proteins, optics, electronics, lightweight building materials, and many more. The U.S. chemical industry is one of the last major sectors in which the U.S. has top global stature. It consumes 24% of all U.S. manufacturing energy, produces over $800B of product annually, supports 25% of the U.S. GDP and employs over 6 million people. It is also a major source of GHG emissions. Polymers make up approximately 30% of this sector. It is estimated that annually 60 TBtu of energy could be saved and 3 million tons less of GHG emissions produced by optimizing production in the polyolefin manufacturing sector alone. The project scope included first time design and prototyping of an ACOMP/CI, creation of active reaction controllers, and demonstration of control capabilities on ideal, low concentration polymerization reactions. All these elements of the scope were met, including advances and findings not originally anticipated. Extensions to more complex reactions, beyond the reactor capabilities of the current project ACOMP/CI, such as polyolefins and other high pressure/high temperature reactions, are being proposed in Fall 2017 to CESMII, a DoE based NNMI. The initial proposal was for a three year funded project, but this was reduced to a two year project and budget due to funding constraints. Hence, some of the original plans, such as adaptation of the ACOMP/CI to more relevant industrial processes, such as emulsion and dispersion technologies, could not be carried out. A third year of funding was requested at the end of the project, but DoE did not have resources to grant this. The sub-contractor Fluence Analytics (previously Advanced Polymer Monitoring Technologies, Inc) designed, prototyped, and commissioned a working ACOMP/CI by June 2015. The reaction characteristics to be automatically controlled were i) conversion kinetics, ii) molecular weight, iii) copolymer composition, and iv) simultaneous molecular weight and composition. A two pronged control strategy was used. The Tulane/Fluence group took a basic principles approach that did not rely on kinetic models. The LSU group took a more complex, non-linear model-oriented approach involving complete kinetic descriptions of the reaction system Each of these approaches proved successful in their own way. By April 2016 fully automatic control of conversion and weight average molecular weight, Mw, trajectories was achieved using the Tulane/Fluence (TF) basic principles controller. Similar results were obtained by the LSU non-linear model controller by August 2016. The demonstration system was aqueous free radical polymerization of acrylamide, Am. The control variables were temperature and semi-batch feed to the reactor of Am monomer and initiator. A demonstration of active manual conversion control in an industrial process using high solids in inverse emulsion polymerization of Am was achieved. During Summer 2016 the TF controller was used in conjunction with a chain transfer agent, another control variable, to automatically produce multi-modal molecular weight distributions, MWD, in a single reactor. Industrially, multi-modal MWD are produced by mixing products made in separate reactors, requiring significant extra time, energy, and reactor resources. Recognizing the industrial potential a patent on automatic production of multi-modal polymers was filed, and DoE acknowledged. In Fall 2016 the TF team developed a basic principles controller for copolymer composition and demonstrated it on aqueous free radical copolymerization of comonomers Am and styrene sulfonate, SS. TF then fused the Mw and composition controllers to achieve simultaneous control of both Mw and copolymer composition trajectories. Numerous simultaneous trajectories were demonstrated, including a trimodal composition distribution with constant Mw. Meanwhile, the LSU group developed a Kalman filter to improve the results of their automatic Mw and conversion controller and successful tests were made. During the project the TF team developed a means of computing full MWD during polymer synthesis without need for any chromatographic separation, based on model distributions. This means the polymer product is ‘born characterized’ and this can eliminate post-manufacture analytical laboratory quality control. TF filed a joint patent application on this new approach to chromatography-free determination of MWD with acknowledgment to DoE. The Tulane group obtained a 60MHz NMR during the project and recently completed the first work on separating three comonomers, Am, SS, and Na-acrylate, with a first demonstration of terpolymer composition control with the TF basic principles controller. Widespread dissemination of ACOMP/CI in the polymer manufacturing sector will bolster DoE goals of energy efficiency and reduced GHG emissions: The ability to monitor and actively control polymerization reactions will lead to more efficient use of energy and non-renewable resources, plant and labor time, increase the safety of manufacturing personnel, and will enhance product quality and lead to feasibility of manufacturing of polymers currently too complex for industrial scale production, while leading to less GHG emissions per kilo of product, and allowing for increased U.S. competitiveness in this enormous manufacturing sector. When ACOMP/CI is expanded to the polyolefin industry it is estimated that 60 TeraBTU/year of energy can be saved. Much of this saving is anticipated to come from optimized control of grade changeovers in steady state reactors and maintenance of steady states. Conclusions: ACOMP’s ability to provide continuous realtime data streams of measured polymer and reaction characteristics made it possible, for the first time, to directly and automatically control free radical polymerization reactions. An industrial client of Fluence Analytics has requisitioned the first ACOMP/CI which uses the TF basic principles controller. This sets the stage for FA to add control features to the ACOMP systems it has begun to install on the industrial scale beginning in 2014. Recommendations: This successful project was mainly limited to ideal polymerizations not of an industrial sort. The most energy intensive portion of polymer manufacturing is polyolefins. Adoption of ACOMP/CI to this enormous industrial sector faces the enormous challenges of high temperature, high pressure continuous sampling and high temperature sensor operation to obtain the continuous data needed for direct reaction control. The project team has a strategy for achieving this ambitious goal and will present it in Fall 2017 as a proposal to CESMII/DoE. It is recommended that this upcoming proposal be funded in order to make full use of the achievements of this just ended DoE project as the next step towards making polyolefin ACOMP/CI an energy saving reality. It is projected that ACOMP/CI can have its first polyolefin testbed demonstrations within two years of beginning the proposed project.
In this report recent studies reveal that organosulfates at the particle surface can be oxidized by gas-phase OH radicals with significant rates. Inorganic sulfur species, such as the bisulfate ion (HSO 4 - ) and sulfate ion (SO 4 2- ), can be formed upon these heterogeneous oxidation processes through the formation and subsequent reactions of sulfate radical anions (SO 4 •- ) in the particle phase. However, the amount of inorganic sulfur species produced in these heterogeneous oxidation reactions is not known. We investigate the heterogeneous OH oxidation of sodium methyl sulfate (CH 3 SO 4 Na), the smallest organosulfate detected in atmospheric particles, using an oxidation flow reactor at a relative humidity of 75%. We quantify the kinetics by measuring the decay of CH 3 SO 4 Na and the amount of HSO 4 - and SO 4 2- formed upon oxidation using ion chromatography. Kinetic measurements determine the heterogeneous OH reaction rate to be (5.72 ± 0.14) x 10 -13 cm 3 molecule -1 s -1 , with an effective OH uptake coefficient, γ eff , of 0.31 ± 0.06. The molar yield of inorganic sulfur species, defined as the total number of moles of HSO 4 - and SO 4 2- formed per mole of CH 3 SO 4 Na consumed upon oxidation, is found to be significant and has an average value of 0.62 ± 0.18 upon oxidation. A kinetic model is developed to describe the kinetics and inorganic sulfur species formation upon oxidation. Model simulations suggest that CH 3 SO 4 Na tends to decompose rapidly into formaldehyde and SO 4 •- , and the reaction of SO 4 •- with CH 3 SO 4 Na plays a significant role in both governing the kinetics and the formation of inorganic sulfur species.
Catalyst design has traditionally focused on tuning active site properties to optimally bind reaction intermediates and balance the kinetic requirements of multiple competing chemical processes, as necessitated by the Sabatier principle. It has recently been proposed that for reactions following certain potential energy landscapes, the activity limit imposed by the Sabatier principle may be overcome by using programmed oscillations of surface electron density at the timescales of surface reactions (i.e., “catalytic resonance”). Here, we use a combination of density functional theory (DFT) simulations and transient kinetic models (TKMs) to simulate the kinetics of steam methane reforming (SMR) on Ru(211) surfaces under statically and dynamically applied charges. DFT-calculated binding energies of SMR intermediates and transition states exhibit strong sensitivity to positively applied charges and follow unique scaling relationships that deviate from linear periodic trends across transition metals. Our simulations demonstrate that applying a small positive charge to Ru dramatically enhances the steady-state turnover frequency (TOF) of SMR by up to 5 orders of magnitude above the TOF observed over neutral Ru. Thus, statically charging Ru catalysts may be an effective strategy to lower the temperature requirements for SMR. Dynamic square-wave oscillations in charge resulted in SMR catalytic resonance with an onset frequency f ∼ 106 Hz and the corresponding average TOFs exceeding the statically charged Ru surface by an additional 15%. Here, based on sensitivity analyses performed for the two end points of oscillation, we propose that dynamic TOF improvement beyond the Sabatier maximum can be expected when the system oscillates between two kinetic regimes that are uniquely controlled by distinct elementary steps.
Interfacial electron transfer at titanium dioxide (TiO 2 ) is investigated herein for a series of surface bound ruthenium-polypyridyl dyes whose metal-to-ligand charge-transfer state (MLCT) energetics are tuned through chemical modification. The 12 complexes are of the form Ru II (bpy-A)(L) 2 2+ , where bpy-A is a bipyridine ligand functionalized with phosphonate groups for surface attachment to TiO 2 . Functionalization of ancillary bipyridine ligands (L) enables the potential of the excited state Ru III/ * couple, E + / *, in 0.1 M perchloric acid (HClO 4 (aq)) to be tuned from –0.69 to –1.03 V vs NHE. Each dye is excited by a 200 fs pulse of light in the visible region of the spectrum and probed with a time-delayed supercontiuum pulse (350–800 nm). Decay of the MLCT excited-state absorption at 376 nm is observed without loss of the ground-state bleach, which is a clear signature of electron injection and formation of the oxidized dye. The dye-dependent decays are biphasic with time constants in the 3–30 and 30–500 ps range. The slower injection rate constant for each dye is exponentially distributed relative to E + / *. The correlation between the exponentially diminishing density of TiO 2 sub-band acceptor levels and injection rate is well described using Marcus–Gerischer theory, with the slower decay components being assigned to injection from the thermally equilibrated state and the faster components corresponding to injection from higher energy states within the 3 MLCT manifold. These results and detailed analyses incorporating molecular photophysics and semiconductor density of states measurements indicate that the multiexponential behavior that is often observed in interfacial injection studies is not due to sample heterogeneity. Rather, this work shows that the kinetic heterogeneity results from competition between excited-state relaxation and injection as the photoexcited dye relaxes through the 3 MLCT manifold to the thermally equilibrated state, underscoring the potential for a simple kinetic model to reproduce the complex kinetic behavior often observed at the interface of mesoporous metal oxide materials.
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Purpose: The reduced concentration of oxygen in cells (hypoxia) results in a lower cell death rate after irradiation that can lead to treatment failure. The effect can be expressed by the oxygen enhancement ratio (OER). So far, only few attempts to include OER in treatment planning for ion beam therapy were made, which are based on the dose averaged LET estimates and do not distinguish among ion species and fractionation schemes. To overcome these limitations, we implemented a new OER model and used it to estimate tumor control in clinical cases. Methods: The model, based on the microdosimetric kinetic model, was benchmarked with in-vitro data from different ions irradiation. It was included in the simulation of treatments of a set of clinical cases (glioblastoma) using p, Li, He, C and O ion beams. Tumor Control Probability (TCP) was estimated as a function of oxygen partial pressure, dose per fraction and primary ion type. Results: The modelized OER was found to be strongly dependent on both LET and ion type, and showed a decreasing OER for increasing dose per fraction with a slope that depends on the LET and ion type, in good agreement with the experimental data. In the clinical cases studied, an increase in TCP by increasing ion charge and dose per fraction (more than 30% variation from p to O for moderate hypoxia) was found. Higher OER decrease rates as function of dose per fraction were found for lighter ions (up to 20% varying from 2 to 8 Gy(RBE)). Conclusions: A novel modeling of the OER that explicitly includes the dependence on ion type and dose per fraction was implemented. The model was exploited to evaluate the impact of hypoxia in ion beam radiotherapy, facilitating the identification of the treatment condition optimality, including fractionation scheme and ion type.
The main objective of this paper is to analyze transport models for lithium symmetric cells and arrive at an efficient model and code to simulate the same. Two one-dimensional models are considered. The first model uses the dilute solution Nernst–Planck equations in conjunction with the electroneutrality assumption (EN-NP model). For binary electrolytes, an analytical solution for electrolyte concentration is derived and compared with numerical solutions by an approximate finite volume method. The second approach relaxes the electroneutrality assumption by way of Poisson’s equation for the electrostatic potential (PNP model). The computational difficulties of the PNP model are tackled using the approximate finite volume method, with demonstrated convergence characteristics even with bulk dimensions several orders of magnitude larger than the characteristic double layer size. A robust code is developed for the PNP model. The computationally efficient transport models can facilitate simulations, physical understanding, and analysis. This is illustrated by a case study in which these models are coupled with modified kinetic models, which are then parameterized with experimental voltage response data using a systems-level approach. The estimated parameters provide further insight into the electrochemical phenomena underpinning voltage transitions in symmetric cells.
Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.
Deterministic transport simulations for national-security and energy applications often operate in high-dimensional phase-space, where accuracy and cost both become major challenges. A common numerical artifact in such problems is the “ray-effect,” which appears as unphysical streaks. Beyond misinterpretation, these artifacts can contaminate tightly coupled physics, such as fluid dynamics, radiation-hydrodynamics, and laser-plasma interactions, eroding the predictive capability of entire multiphysics workflows. Our objective was to make high-dimension studies practical on modern hardware while mitigating the ray-effect without relying on prohibitively expensive sampling approaches such as Monte Carlo methods. We developed the Generic Discretization Library (GenDiL), a Graphics Processing Unit (GPU)-first framework that uses high-order Discontinuous Galerkin (DG) methods and matrix-free algorithms to reduce memory usage and improve computational efficiency, critical for phase-space simulations. GenDiL supports phase-space adaptivity in both mesh size and polynomial order (hp-adaptivity) to place resolution only where it is needed. A central capability is Local Dimensional Refinement (LDR), which couples lower-dimension continuum models to higher-dimension kinetic models through stable and conservative interfaces, so that high-fidelity physics is applied only in regions where it is essential. Building on the GenDiL framework, we developed the General SN (GSN) family of algorithms as a true generalization of the polar SN approach (discrete ordinates, often denoted SN). Rather than tying discrete ordinates to a specific polar change of coordinates, GSN formulates transport on an arbitrary change of coordinates chosen to reduce ray-effect. We studied two complementary variants: an analytic variant, where the coordinate map is prescribed in advance by a closed-form function; and a data-driven variant, where a quantity of interest, such as the net flux, guides the coordinate system. GenDiL provides the library infrastructure for efficient GPU execution, but the GSN concept is algorithmic and independent of any one library. Across representative high-dimension tests, including non-symmetric solutions, both variants delivered strong ray-effect mitigation at practical cost, moving four- to six-dimensional analysis toward repeatable, routine studies.
Designing processing equipment for the mixing of settling suspensions is a challenging problem. Achieving low-cost mixing is especially difficult for the application of slowly reacting suspended solids because the cost of impeller power consumption becomes quite high due to the long reaction times (batch mode) or due to large-volume reactors (continuous mode). Further, the usual scale-up metrics for mixing, e.g., constant tip speed and constant power per volume, do not apply well for mixing of suspensions. As an alternative, computational fluid dynamics (CFD) can be useful for analyzing mixing at multiple scales and determining appropriate mixer designs and operating parameters. We developed a mixture model to describe the hydrodynamics of a settling cellulose suspension. The suspension motion is represented as a single velocity field in a computationally efficient Eulerian framework. The solids are represented by a scalar volume-fraction field that undergoes transport due to particle diffusion, settling, fluid advection, and shear stress. A settling model and a viscosity model, both functions of volume fraction, were selected to fit experimental settling and viscosity data, respectively. Simulations were performed with the open-source Nek5000 CFD program, which is based on the high-order spectral-finite-element method. Simulations were performed for the cellulose suspension undergoing mixing in a laboratory-scale vane mixer. The settled-bed heights predicted by the simulations were in semi-quantitative agreement with experimental observations. Further, the simulation results were in quantitative agreement with experimentally obtained torque and mixing-rate data, including a characteristic torque bifurcation. In future work, we plan to couple this CFD model with a reaction-kinetics model for the enzymatic digestion of cellulose, allowing us to predict enzymatic digestion performance for various mixing intensities and novel reactor designs.
Adsorption kinetics of molsidomine on mesoporous silica material (UMS), the phenyl- (PhMS) and mercaptopropyl-functionalized (MMS) derivatives from solution with different pH and 298 K was studied. The adsorption kinetics was found to follow the pseudo-second-order kinetic model for all studied silica materials and pH. Effects of surface functional groups and pH on adsorption efficiency and kinetic adsorption parameters were investigated. At all studied pH, the highest molsidomine amount is adsorbed on PhMS due to π–π interactions and hydrogen bonding between surface groups of PhMS and molsidomine molecules. An increase of pH results in a decrease of the amounts of adsorbed molsidomine onto the silica materials. Furthermore, the highest adsorption rate kinetically evaluated using a pseudo-second-order model, is observed onto UMS and it strongly depends on pH. The mechanism of the adsorption process was determined from the intraparticle diffusion and Boyd kinetic film–diffusion models. The results showed that the molsidomine adsorption on the silica materials is controlled by film diffusion. Effect of pH on the diffusion parameters is discussed. - Graphical abstract: The kinetic study showed that the k{sub 2} value, the rate constant of pseudo-second order kinetic model, is the highest for molsidomine adsorption on UMS and strongly depends on pH because it is determined by availability and accessibility of the reaction sites of the adsorbents molsidomine binding. Display Omitted - Highlights: • The adsorption capacities of UMS, PhMS and MMS were dependent on the pH. • At all studied pH, the highest molsidomine amount is adsorbed on PhMS. • The highest adsorption rate, k{sub 2}, is observed onto UMS and strongly depends on pH. • Film diffusion was the likely rate-limiting step in the adsorption process.
Abstract Abnormal overexpression of tyrosinase activity can lead to the production of hyperpigmentation in human skin and enzymatic browning in fruits and vegetables. Herein, the inhibition and mechanism of the H 3 PMo 12 O 40 and two transition metal‐substituted Keggin‐type polyoxometalates (Na 7 PMo 11 CoO 40 and Na 7 PMo 11 ZnO 40 ) on tyrosinase were studied by kinetics and molecular modeling. Kinetic studies indicated that all compounds had more potent inhibitory activities than standard arbutin, and H 3 PMo 12 O 40 (IC 50 = 0.443 ± 0.006 m m ) is ~15‐fold stronger inhibition than arbutin. Additionally, all compounds inhibited tyrosinase in a reversible competitive manner. Intriguingly, molecular modeling elucidated that three compounds competitively bind to tyrosinase mainly through more interactions with Cu 2+ ions and the amino acid residue capable of forming cation‐π and hydrogen bonding, forming a reversible non‐covalent complex. Molecular simulation study correlated well with the biological activity of three compounds in vitro. This work provided new insights into design and synthesis of polyoxometalates as tyrosinase inhibitors in the field of medicine, cosmetic, and food.
We have developed a multi-scale approach (Burke, M. P.; Klippenstein, S. J.; Harding, L. B. Proc. Combust. Inst. 2013, 34, 547–555.) to kinetic model formulation that directly incorporates elementary kinetic theories as a means to provide reliable, physics-based extrapolation to unexplored conditions. Here, we extend and generalize the multi-scale modeling strategy to treat systems of considerable complexity – involving multi-well reactions, potentially missing reactions, non-statistical product branching ratios, and non-Boltzmann (i.e. non-thermal) reactant distributions. The methodology is demonstrated here for a subsystem of low-temperature propane oxidation, as a representative system for low-temperature fuel oxidation. A multi-scale model is assembled and informed by a wide variety of targets that include ab initio calculations of molecular properties, rate constant measurements of isolated reactions, and complex systems measurements. Active model parameters are chosen to accommodate both “parametric” and “structural” uncertainties. Theoretical parameters (e.g. barrier heights) are included as active model parameters to account for parametric uncertainties in the theoretical treatment; experimental parameters (e.g. initial temperatures) are included to account for parametric uncertainties in the physical models of the experiments. RMG software is used to assess potential structural uncertainties due to missing reactions. Additionally, branching ratios among product channels are included as active model parameters to account for structural uncertainties related to difficulties in modeling sequences of multiple chemically activated steps. The approach is demonstrated here for interpreting time-resolved measurements of OH, HO2, n-propyl, i-propyl, propene, oxetane, and methyloxirane from photolysis-initiated low-temperature oxidation of propane at pressures from 4 to 60 Torr and temperatures from 300 to 700 K. In particular, the multi-scale informed model provides a consistent quantitative explanation of both ab initio calculations and time-resolved species measurements. The present results show that interpretations of OH measurements are significantly more complicated than previously thought – in addition to barrier heights for key transition states considered previously, OH profiles also depend on additional theoretical parameters for R + O2 reactions, secondary reactions, QOOH + O2 reactions, and treatment of non-Boltzmann reaction sequences. Extraction of physically rigorous information from those measurements may require more sophisticated treatment of all of those model aspects, as well as additional experimental data under more conditions, to discriminate among possible interpretations and ensure model reliability. Keywords: Optimization, Uncertainty quantification, Chemical mechanism, Low-Temperature Oxidation, Non-Boltzmann
Coal devolatilization, as first step in thermochemical processes involving solid fuels, has been targeted in plenty of CFD investigations: despite the variety of models that were developed to define the rate of the process and the amount of volatile matter released, not all of them are suitable for CFD simulations of large-scale coal combustors. Empirical models ensure both simplicity and feasibility, but not full reliability for all coal types and operating conditions. CFD modeling results for coal conversion during devolatilization in an entrained flow reactor and oxy-conditions are presented and compared to experimental data. A benchmark assessment of one-step models on the accuracy of the volatile yield prediction is then performed. It is shown that the heating rate strongly influences the devolatilization process and its modeling, not only in terms of rate but also in terms of yield, which differs significantly from the proximate analysis value. Furthermore, this indicates the need for a model able to predict the variation of coal volatile yield with temperature and heating rate, in addition to the kinetic model.
Polycyclic aromatic hydrocarbons (PAHs) are important precursors to the formation of soot and are also pollutant emissions from combustion devices, including internal combustion engines. To understand the formation of PAHs, the low-molecular-weight hydrocarbon chemistry leading to their formation needs to be understood. Toward this goal, fuel-rich oxidation of three different hydrocarbons, i.e., ethylene, toluene, and n-decane, has been investigated in an atmospheric pressure flow reactor at varying temperatures (1000–1350 K), equivalence ratios (φ = 3.0–12.0), and residence times of 0.25–1.5 s. The major C 1 to C 7 intermediates, such as methane, acetylene, ethylene, allene, propyne, propylene, vinylacetylene, 1,3-butadiene, cyclopentadiene, benzene, and toluene, were quantified using a gas chromatograph equipped with a flame ionization detector. The experimental tendency of intermediate species formation, e.g., the dependence of temperature, equivalence ratio, and residence time, was similar in ethylene oxidation and n-decane oxidation. The concentrations of intermediate species up to C 4 were higher in ethylene/n-decane oxidation than toluene oxidation, while a nearly equal or larger amount of C5–C7 species was produced in toluene oxidation. The experimental data were compared with modeling results using a detailed chemical kinetic mechanism. The calculated data using the kinetic model were in agreement with the experimental results. Additionally, a comprehensive kinetic analysis on the reaction pathways of each species was conducted to assess the differences in the oxidation chemistry with the change in the structure of hydrocarbons. In particular, in-depth analysis of benzene formation was performed, elucidating that two benzene formation pathways were important in ethylene and n-decane oxidation: (1) H elimination of the 2,4-cyclohexadienyl radical produced from the isomerization of the 2,4-cyclopentadienylmethyl radical, and (2) dehydrogenation of cyclohexadiene produced from reactions of vinyl radical with 1,3-butadiene. In toluene oxidation, it was found that benzene was primarily produced from toluene through the replacement of the methyl group with hydrogen.
“Reduced” (i.e., simplified or approximate) ion-kinetic (RIK) models in radiation-hydrodynamic simulations permit a useful description of inertial-confinement-fusion (ICF) implosions where kinetic deviations from hydrodynamic behavior are important. For implosions in or near the kinetic regime (i.e., when ion mean free paths are comparable to the capsule size), simulations using a RIK model give a detailed picture of the time- and space-dependent structure of imploding capsules, allow an assessment of the relative importance of various kinetic processes during the implosion, enable explanations of past and current observations, and permit predictions of the results of future experiments. The RIK simulation method described here uses moment-based reduced kinetic models for transport of mass, momentum, and energy by long-mean-free-path ions, a model for the decrease of fusion reactivity owing to the associated modification of the ion distribution function, and a model of hydrodynamic turbulent mixing. The transport models are based on local gradient-diffusion approximations for the transport of moments of the ion distribution functions, with coefficients to impose flux limiting or account for transport modification. After calibration against a reference set of ICF implosions spanning the hydrodynamic-to-kinetic transition, the method has useful, quantifiable predictive ability over a broad range of capsule parameter space. Calibrated RIK simulations show that an important contributor to ion species separation in ICF capsule implosions is the preferential flux of longer-mean-free-path species out of the fuel and into the shell, leaving the fuel relatively enriched in species with shorter mean free paths. Also, the transport of ion thermal energy is enhanced in the kinetic regime, causing the fuel region to have a more uniform, lower ion temperature, extending over a larger volume, than implied by clean simulations. We expect that the success of our simple approach will motivate continued theoretical research into the development of first-principles-based, comprehensive, self-consistent, yet useable models of kinetic multispecies ion behavior in ICF plasmas.