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This presentation touches on ENDF/B-VIII.1 Validation: Covariance. Topics include inconsistencies in the ENDF files, derived matrices, lumped reactions and process ENDF data file with PUFF. Finally correcting data with cognac and redundant covariance that are not consistent are discussed.
A numerical model is developed to study cell venting, internal pressure, and gas-phase dynamics behavior of 18650 Li-ion cells undergoing thermal runaway. A k-e Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer's equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. Then, a series of computational fluid dynamics (CFD) simulations are conducted on a single 18650 cell at various states-of-charge (100%, 50%, 25%) to study detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is found that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.
A numerical model is implemented to study cell venting, internal pressure, and gas-phase dynamics behavior of Li-ion cells undergoing thermal runaway. A k-ɛ Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer’s equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. A series of computational fluid dynamics (CFD) simulations are conducted on a single cell at various states-of-charge (100%, 50%, 25%) to demonstrate detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is shown that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.
Her we report that we studied the hydrogenation at temperatures from 313–393 K of a biomass-derived platform molecule, 5-hydroxymethyl furfural (HMF)-acetone-HMF (HAH) over Pd, Ru, and Cu based catalysts. HAH was selectively hydrogenated to produce partially-hydrogenated monomers (PHAH) over Cu and Ru catalysts and to fully-hydrogenated HAH monomers (FHAH) over the Ru catalyst. Pd based catalysts yielded a mixture of partially and fully hydrogenated monomers. Lumped reaction kinetics models were employed to quantify the kinetic behavior for hydrogenation over Ru, Cu, and Pd catalysts. The 5-step pathway exhibited over Pd and Ru catalysts consists of both series and parallel reaction steps, where HAH is both converted to fully hydrogenated products sequentially via series reactions of partially hydrogenated intermediates, as well as converted directly in parallel reactions to form the fully hydrogenated products. In contrast, the 3-step pathway over the Cu catalyst consists only of the consecutive reaction steps, where the final product was formed via series reactions of intermediate products. Additionally, reaction over the Cu catalyst did not hydrogenate the furan rings of the HAH molecule and yielded a different final product than those hydrogenation over Pd and Ru catalysts. Batch conditions are determined for each hydrogenated product that give the highest yields in both batch and plug flow reactors.
In this study, the catalytic pyrolysis of low-density polyethylene (LDPE), polyethylene terephthalate (PET) and their mixture (1 : 1 wt/wt) with three zeolite catalysts (HZSM-5, H-beta, HY) was investigated using a thermogravimetric analyzer (TGA) and a Pyroprobe® micro-reactor coupled to a gas chromatograph mass spectrometer (Py-GC/MS). The TGA results demonstrated that during pyrolysis at 10 °C min –1 , on average, zeolite catalysts decreased the maximum decomposition temperature by 149 °C for LDPE while only decreasing by 8 °C for PET. The derivative thermogravimetric (DTG) curve evidenced interactions when the two polymers were catalytically co-pyrolyzed for all the three catalysts. A lumped nth order reaction scheme was able to accurately model both non-catalytic and catalytic pyrolysis and co-pyrolysis by using the least squares fitting approach for determining the kinetic parameters. The kinetic model was able to model well the interaction effects observed during catalytic co-pyrolysis of LDPE and PET with HZSM-5, H-beta, and HY (Fit% Wt% > 96%, Fit% DTG > 94%). Py-GC/MS experiments for the catalytic fast pyrolysis of LDPE revealed that HZSM-5 resulted in the highest selectivity to aromatic hydrocarbons (31.6%) and HY resulted in the highest selectivity to gasoline range C 5 –C 10 paraffins and olefins (40.9%). Catalytic fast pyrolysis of PET showed high selectivity to benzene for all the catalysts (>43%) and that HZSM-5 resulted in the highest selectivity to polyaromatic hydrocarbons (24.7%). Finally, the catalytic fast co-pyrolysis of LDPE and PET revealed interaction effects for all the three catalysts evidenced by a positive synergy% for alkylated benzenes (3–142%) and polyaromatics (105–187%) with a concomitant negative synergy% for benzene (24–36%) and C 5 –C 10 paraffins and olefins (27–53%).
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The oxidative degradation rates of a CO 2 sorbent composed of a mesoporous alumina impregnated with poly- (ethylenimine) (PEI) are measured under systematically varied conditions and a reaction rate law is created. Good agreement is shown between the rate of oxidation obtained via in situ calorimetric heat measurement during oxidative degradation reactions and the loss of CO 2 capture performance presented as amine efficiency (mol CO 2 /mol amine). PEI mass loss and elemental composition are tracked over the course of the reaction and used in conjunction with the oxidation rate measurements to shed insight into the oxidation reaction(s). These data, in combination with measurements of the heat of reaction, suggest a common reaction set across the range of temperatures, oxygen concentrations, and sorbent compositions tested. The data are consistent with the basic autoxidation scheme (BAS), the accepted mechanism of autoxidation of aliphatic polymers. We propose a lumped kinetic model to describe the oxidation reaction set and estimate an activation energy of 105 kJ/mol and an oxygen reaction order of 0.5–0.7 from the data accordingly. Furthermore, these parameters can be incorporated into process cycle models to estimate the material lifetime, a critical uncertainty in the deployment of DAC technologies.
A thorough understanding of oxidation is important when considering the health and integrity of graphite components in graphite reactors. For the next generation of graphite reactors, HTGRs specifically, an unlikely air ingress has been deemed significant enough to have made its way into the licensing applications of many international licensing bodies. While a substantial body of literature exists on nuclear graphite oxidation in the presence of molecular oxygen and significant efforts have been made to characterize oxidation kinetics of various grades, the value of existing information is somewhat limited. Often, multiple competing processes, including reaction kinetics, mass transfer, and microstructural evolution, are lumped together into a single rate expression that limits the ability to translate this information to different conditions. This article reviews the reaction of graphite with molecular oxygen in terms of the reaction kinetics, gas transport, and microstructural evolution of graphite. It also presents the foundations of a model for the graphite-molecular oxygen reaction system that is kinetically independent of graphite grade, and is capable of describing both the bulk and local oxidation rates under a wide range of conditions applicable to air-ingress.
The Sabatier reaction (CO 2 + 4H 2 → CH 4 + 2H 2 O) is gaining renewed interest due to its potential to reduce energy carrier storage costs, serve as a feedstock for various organic chemicals, and supply in-situ propellant and life-support resources for long-duration Mars missions. This study demonstrates that combining a modest 2 mA electric field with H 2 feed modulation markedly elevates the CO 2 hydrogenation activity of 2 wt% Ru/CeO 2 catalyst. CO 2 conversion reaches 88 % and 93 % with a CH 4 yield of 83 % and 89 % at 350 °C and 450 °C, respectively. A simple lumped kinetic model reveals that the combined external perturbations not only shift the reaction mechanism but also redistribute key surface-adsorbed intermediates such as hydrogen adatoms and hydrogen-activated CO 2 among the Ru clusters, Ru/CeO 2 interface, and ceria surface. The electric field accelerates the conversion of adsorbed CO 2 to the hydrogenated CO 2 species on Ru and boosts CH 4 formation rate constant, while simultaneously suppresses the formation of undesired, non-reactive surface intermediates. Degree-of-rate-control analysis pinpoints proton migration across the metal-support interface as the decisive lever under these coupled perturbations. In conclusion, these findings establish that rational pairing of metal-support design with well-tuned electric fields and feed oscillations can unlock unprecedented Sabatier rates, guiding the development of next-generation reactors for efficient CO 2 to CH 4 conversion.
Interest in circulating fluidized bed (CFB) boilers as a power generation technology has skyrocketed in recent years because of several advantages this technology offers over conventional boilers, such as increased gas-solid mixing, which results in higher combustion efficiency, and the ability to use lower rank fuels. CFB combustors are operated at lower temperatures than conventional thermal power generation combustors, thus reducing NO x emissions, while SO 2 emissions can be conveniently controlled through the addition of Ca-based sulfur sorbents within the combustor. This report summarizes the development of a comprehensive computational fluid dynamics (CFD) model for biomass combustion in a 50 kW th CFB combustor employing the multiphase particle-in-cell (PIC) approach in the open-source Multiphase Flow with Interphase eXchanges (MFiX) Software Suite at the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL). The experimental combustor was designed, built, and operated at CanmetENERGY, Natural Resources Canada in Ottawa, Canada. This work is the culmination of a multi-year collaboration between NETL and CanmetENERGY. The hydrodynamics in the full loop are validated against cold-flow experiments conducted at CanmetENERGY. To mimic the dense packing in the standpipe in the absence of a collisional model in the MFiX-PIC approach, the ram valve in the return leg is partially closed to obstruct the flow. A combustion reaction scheme was developed that considers the volatiles released by the fuel as a lumped species that fully converts to CO 2 and H 2 O. The so-called enhanced simplified combustion scheme produces an excellent match with experimental data in terms of the species concentrations of CO 2 and O 2 at the outlet and the freeboard, demonstrating the utility of the comprehensive model for a range of flow conditions and operating temperatures. Furthermore, the model can be readily adapted to simulate combustion of a variety of biomass fuels as well as co-fired systems under air and oxy-fuel conditions.
Selective oxidation of propene to acrolein over industrial multicomponent bismuth molybdate (BMO) catalysts significantly depends on reaction conditions that include operating parameters and catalyst state. Here, this work investigates selective propene oxidation in the intrinsic kinetic paradigm of Temporal Analysis of Products (TAP) reactor by systematically varying catalyst redox state, temperature, and oxygen-to-propene feed ratio. A 93 % propene conversion with an acrolein yield of 80 % is achieved at elevated temperatures (450 °C) on oxidized catalysts under oxygen-rich conditions (O 2 :C 3 H 6 = 10). However, these conditions diminish acrolein-to-CO 2 selectivity due to enhanced total oxidation to CO 2 . In contrast, a reduced catalyst state, moderate temperature (350 °C), and lower oxygen feed (O 2 :C 3 H 6 = 1) nearly doubles the acrolein to-CO 2 selectivity, albeit at a lower acrolein yield (33 %). Transient kinetic studies together with a kinetic model that simplify the major product formation pathways in lumped non-elementary forms reveal that the availability of surface oxygen species plays a pivotal role in governing reaction pathways. Additionally, density functional theory (DFT) calculations on pure BMO catalysts inform the role of surface redox states on propene and oxygen activation barriers. Lattice oxygen at acrolein-selective sites drives both acrolein and CO 2 formation, while adsorbed oxygen at activation sites favors unselective CO 2 generation. This work establishes a critical relationship between transient product selectivity and surface oxygen availability, which is strongly influenced by catalyst redox state, feed ratio, and reaction temperature. These insights underscore the importance of dynamic reactor operation strategies and offer a foundation for designing next-generation propene oxidation processes with tunable acrolein selectivity.
For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.
Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.
Watersheds are the fundamental Earth surface functioning units that connect the land to aquatic systems. Many watershed-scale models represent hydrological processes but not biogeochemical reactive transport processes. This has limited our capability to understand and predict solute export, water chemistry and quality, and Earth system response to changing climate and anthropogenic conditions. Here we present a recently developed BioRT-Flux-PIHM (BioRT hereafter) v1.0, a watershed-scale biogeochemical reactive transport model. The model augments the previously developed RT-Flux-PIHM that integrates land-surface interactions, surface hydrology, and abiotic geochemical reactions. It enables the simulation of (1) shallow and deep-water partitioning to represent surface runoff, shallow soil water, and deeper groundwater and of (2) biotic processes including plant uptake, soil respiration, and nutrient transformation. The reactive transport part of the code has been verified against the widely used reactive transport code CrunchTope. BioRT-Flux-PIHM v1.0 has recently been applied in multiple watersheds under diverse climate, vegetation, and geological conditions. This paper briefly introduces the governing equations and model structure with a focus on new aspects of the model. It also showcases one hydrology example that simulates shallow and deep-water interactions and two biogeochemical examples relevant to nitrate and dissolved organic carbon (DOC). These examples are illustrated in two simulation modes of complexity. One is the spatially lumped mode (i.e., two land cells connected by one river segment) that focuses on processes and average behavior of a watershed. Another is the spatially distributed mode (i.e., hundreds of cells) that includes details of topography, land cover, and soil properties. Whereas the spatially lumped mode represents averaged properties and processes and temporal variations, the spatially distributed mode can be used to understand the impacts of spatial structure and identify hot spots of biogeochemical reactions. The model can be used to mechanistically understand coupled hydrological and biogeochemical processes under gradients of climate, vegetation, geology, and land use conditions.
It is increasingly popular to use Computational Fluid Dynamics (CFD) models to study floating structures subjected to ocean waves, especially when it comes to applications of floating offshore wind turbines and Wave Energy Converters (WECs). Mooring dynamics are currently lacking in most of these applications. This paper presents a coupled simulation study of moored floating body motion by coupling two open-source libraries: a finite volume CFD toolbox, OpenFOAM, and a lumped-mass mooring model, MoorDyn. The instantaneous floating body position and velocity are passed from the body motion solver in the CFD model to the mooring model to calculate the fairlead kinematics. The mooring reaction forces, which are calculated by MoorDyn after updating the mooring system states, are then returned to the body motion solver to update the floating body motion. Both mesh deformation and overset mesh methods are used as the mesh motion solver in the CFD model to account for the floating body motion. The coupled model was validated against experimental measurements for a floating box moored with four catenary lines under the action of regular waves, which came from a preliminary test campaign for WECs. Apart from the lumped-mass mooring model, the present work also coupled a quasi-static mooring model and a finite element model with the floating body motion solver in OpenFOAM. The mooring line tensions predicted by these models were compared. The coupled model equipped with three mooring line codes may be further used to carry out survivability studies of FOWTs and WECs subject to severe sea states.
The increasing availability of high-resolution characterization of natural organic matter (OM) data has shifted the paradigm of lumped descriptions of OM components and potential microbial activities. Our recent development of a substrate-explicit thermodynamic model uniquely enables incorporating complex OM pools to formulate biogeochemical reaction models based on their elemental compositions. While this previous work facilitates prediction of aerobic respiration of complex OM, it is equally imperative to consider the role of non-oxygenic electron acceptors in regulating OM turnover and the fate of carbon. In this study, we significantly expand our previous model by flexibly incorporating both detailed OM chemistry and electron acceptors other than oxygen. Here, our modeling analysis has revealed substantial variations in the energy status of OM molecules across different soils, which drive the co-occurrence of different electron-accepting processes. We demonstrated the effectiveness of the proposed model using a consistency check with experimental data. Through systematic evaluation of the impact of diverse chemical inputs (both electron donors and acceptors) on OM decomposition, the new model also revealed how key microbial growth parameters such as carbon use efficiency (CUE) and reaction rates vary across different electron-accepting processes. Our model provides a unified framework integrating thermodynamic and kinetic constraints on microbial metabolic activities. It complements traditional kinetic models, which are often designed solely to capture mass fluxes. We conclude that thermodynamic modeling emerges as a powerful tool for describing the mechanisms underlying the interplay between microbial growth and OM chemistry and cycling across different electron acceptors, enhancing our ability to project complex ecosystem behaviors in dynamic environments.
A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO2/Pt-X/SiO2; X is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C23-centered distribution of hydrocarbons in high yield using very low Pt loadings (~10 -5 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C–C bond cleavage occurs and then the smaller fragment exits the pore. mSiO 2 /Pt/SiO 2 resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved. Here, we report the effects of Pt NP size on activity and selectivity in PE hydrogenolysis. Time-dependent conversion and yields and a lumped kinetics model based on the competitive adsorption of long vs short chains reveal that the activity of catalytic material is highest with the smallest NPs, consistent with a structure-sensitive reaction. Remarkably, the three mSiO 2 /Pt-X/SiO 2 catalysts give equivalent selectivity. We propose that mesoscale pores in the catalytic architecture template the C 23 -centered distribution, whereas the active Pt sites influence the carbon–carbon bond cleavage rate. This conclusion provides a framework for catalyst design by separating the C–C bond cleavage activity at catalytic sites from selectivity for chain lengths of the products influenced by the structure of the catalytic architecture. The increased activity, selectivity, efficiency, and lifetime obtained using this architecture highlight the benefits of localized and confined environments for isolated catalytic particles under condensed-phase reaction conditions.
This report discussed progress made in the development of a computational model for intermediate pressure capacitively coupled plasma discharge. A new full fidelity argon chemistry model has been developed that includes electron, argon ion, three lumped states for electronically excited species, and the background ground state argon. As such the mechanism has been validated over a wide range of intermediate pressure conditions from 100 mTorr to a few Torr. Uncertainties in the electron impact cross sections and the rate coefficients for the reactions are propagated through the model to establish uncertainties that emerge in the model due the reaction mechanism and its impact on the discharge properties. The model is therefore validated in face of uncertainties.