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In this paper we present a large-scale sensitivity study of reaction rates in the s and i process. We identified all rates with the highest absolute sensitivity on the production of each element. In addition, the effect of the radioactive decays on the abundances during the time between the end of the nucleosynthesis and the actual observation is considered.
The SCALE code system’s ability to address stochastic distributions of fuel particles within a graphite matrix has been revisited in both multigroup (MG) features and continuous-energy (CE) Monte Carlo methods. Furthermore, this paper describes current and emergent SCALE capabilities within the CSAS sequence to address double-heterogeneous systems and presents verification and validation studies of these methods and data. Good agreement was obtained for a high temperature gas-cooled reactor (HTGR) fuel pebble model between CSAS MG eigenvalue calculations and corresponding CE reference solutions. Code-to-code comparisons for this HTGR pebble model showed good agreement of CSAS-KENO and CSAS-Shift CE calculations and the Serpent and MCNP codes in terms of eigenvalues and reaction rate ratios. Validation studies based on two HTGR experiments resulted in good agreement between MG and CE results, as well as between experiment and calculation, although the level of agreement was significantly influenced by the applied ENDF/B nuclear data library.
In this work, a total of 20 actinide samples were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL): 233 U (1), 235 U (1), 236 U (1), 238 U (1), 237 Np (2), 239 Pu (1), 240 Pu (1), 242 Pu (2), 244 Pu (2), 241 Am (2), 243 Am (2), 244 Cm (2), and 248 Cm (2). The samples were loaded in a basket containing a 1.1-mm thick cadmium filter. The presence of the filter drastically changes the neutron spectrum in the samples and accentuates the reaction rates in the resonance region. Isotopic ratios were determined using Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS) that allowed inferring effective capture cross sections. Detailed best-estimate MCNP models of the experiment using ENDF/B-VII.0 nuclear data were developed to calculate the effective neutron cross sections and, subsequently, compare them with the cross sections inferred from experimental results.
We report a low-order neutronics model is developed to carry out hundreds of simulations efficiently and investigate the neutronics behavior of samples being irradiated in a test reactor setting under different geometrical constraints. The low-order model allowed for simulations that yield the expected neutronics behavior of any irradiated sample in any environment and allows for the calculation of highly accurate spatially averaged statistics and idealized spatial distributions in the neutron flux. Several benchmarks are performed to evaluate the performance and limitations of the low-order model revealing many important findings. The low-order model predicted the LHGR in the EBR-II driver fuel to within 2.34% by only simulating the fuel rod by itself, which served as a validation for the model. Sensitivity studies investigated 3% enriched UO 2 and U-10Zr being irradiated in the Versatile Test Reactor rabbit system. The analyses investigated a range of combinations of 15 radii and 5 heights for each sample in the rabbit system. Similar data sets are also provided for irradiations in the Advanced Test Reactor’s B-10 irradiation position, which is a thermal neutron spectrum environment. Generalized fits and fit coefficients are obtained for sample heating, reaction rate densities, and local multiplication rate characteristics, allowing the predictions of the neutronics behavior of the samples based on their geometrical constraints. The analyses and fits laid the groundwork for developing a user-end Multiphysics analysis framework to assist and accelerate irradiation experiment design and optimization.
This work introduces the pin power reconstruction capability available in the Griffin reactor physics code. This capability is implemented in an unstructured mesh framework, and the methods introduced are applied to the 2D SIMBA reactor core, which has assemblies and pins arranged in a hexagonal lattice. Since this reactor has a non-Cartesian geometry and also operates in the thermal spectrum, a general approach to pin power reconstruction is adopted, where SPH-based equivalence is leveraged to preserve assembly-wise reaction rates, while computing full-core form functions to preserve pin-wise fission production rates within the fuel pins of the reactor core. In a 2D microreactor benchmark problem, this pin power reconstruction approach was shown to reproduce pin powers compared to the Serpent2 Monte Carlo code for fixed temperature conditions and control drum rotation angles, yielding a core-wide RMS error level of 0.6\% and a maximum absolute pin error of 2.3\%. In addition, a tabulated library of multigroup cross sections, SPH factors, and form functions was generated to demonstrate the applicability of pin power reconstruction to a thermal feedback problem. Finally, a control drum transient was successfully simulated, showcasing the application of pin power reconstruction in a transient multiphysics feedback problem.
In the realm of corrosion protection, Zr-based conversion coatings offer an environmentally friendly, chromate-free alternative to conventional coating. This study uses advanced X-ray, electron microscopy and electrochemical testing techniques to better understand the impact of varying Cu²⁺ ion concentrations on the characteristics of Zr-based coatings on Fe substrates. Our findings demonstrate that within the tested conditions, higher Cu²⁺ additive concentrations at 40 ppm enhance surface characteristics, increasing stability towards anti-corrosion capability, particularly under NaCl treatment. Conversely, at a lower Cu²⁺ concentration of 20 ppm, coatings exhibited more significant dissolution of Cu clusters and increased vulnerability to chloride-induced degradation. X-ray photoelectron spectroscopy and synchrotron X-ray fluorescence (XRF) and X-ray absorption near edge structure (XANES) spectroscopy analyses revealed Cu²⁺ formation in Cu20, while Cu40 retained its metallic state (Cu⁰) with a slower reaction rate. Cu20 offers some protection but lacks durability, whereas in Cu40, lower I corr , and enhance corrosion resistance, making it ideal for protective coatings in salt-water environments. This study underscores the importance of balancing Cu²⁺ ions concentration in the coating solution to optimize performance, highlighting the role of Cu in enhancing both surface properties and long-term stability.
Fermentation of gaseous substrates such as carbon dioxide (CO 2 ) has emerged as a sustainable approach for transforming greenhouse gas emissions into renewable fuels and biochemicals. CO 2 fermentations are catalyzed by hydrogenotrophic methanogens and homoacetogens, these anaerobic microorganisms selectively reduce CO 2 using hydrogen (H 2 ) as electron donor. However, H 2 possesses low solubility in liquid media leading to slow mass transport, limiting the reaction rates of CO 2 reduction. Solving the problems of mass transport of H 2 could boost the advance of technologies for valorizing industrial CO 2 -rich streams, like biogas or syngas. The application could further be extended to combustion flue gases or even atmospheric CO 2 . In this work, an overview of strategies for overcoming H 2 mass transport limitations during methanogenic and acetogenic fermentation of H 2 and CO 2 is presented. Here, the potential for using these strategies in future full-scale facilities and the knowledge gaps for these applications are discussed in detail.
Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.
A simplified macroscopic CFD approach is presented to model mass transport including chemical reactions in washcoated open-cell foams. The foam is treated as a porous medium. Species conversion during chemical reactions is modeled using appropriate source terms based on reaction rate expressions and modified to account for the mass transport resistances occurring at the fluid-washcoat interfaces and within the washcoat layers. As example, the catalytic CO oxidation over platinum is studied. The simulation results show good agreement with experimental data from literature. A parametric study on washcoat parameters, such as thickness, tortuosity, porosity, and size, is carried out. Increasing the washcoat thickness from 5 to 100 µm or decreasing the tortuosity to porosity ratio from 5 to 20 decreases the CO conversion by 10 %. The proposed model is found to be reliable and has the advantage of lower computational cost, making it a suitable tool for foam-based catalytic reactor design.
Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.
The size and complexity of chemical kinetic models used to simulate combustion of hydrocarbon fuels continue to increase with our improved understanding of the underlying physical mechanisms, along with the improved ability to utilize such models as the result of increased computational power and efficiency. As mechanisms grow beyond thousands of species and tens of thousands of reactions, it becomes increasingly difficult to manually check for errors and physical inconsistencies. In this work, we present several automated methods to check for such issues in the specification of chemical reaction models. First, we demonstrate how discontinuities in thermodynamic data can cause simulation difficulties manifested as long wall-clock times and convergence failures. To correct this type of problem, we describe an automated method for refitting thermodynamic parameters. We also outline several methods to check the timescales of reaction rate coefficients to ensure physical consistency. All the methods are made available through a web-based tool (https://combustiontools.llnl.gov) intended to aid mechanism developers and users to improve the accuracy and performance of fuel models used by the combustion community.
Light alkenes are common combustion intermediates for a variety of fuels. Therefore, understanding their oxidation and pyrolysis chemistry is key to building detailed mechanisms for heavier fuels. This work was focused on the development and evaluation of a detailed kinetic mechanism suitable for the combustion of light alkenes up to C 4 without the use of tuned parameters, instead, the parameter values come from first principles or direct measurements. The generated mechanism accurately estimates the laminar burning velocity (S u ) and ignition delay time (IDT) of light alkenes available in the literature, which represent fundamental combustion properties at a wide range of conditions. Because each parameter is thought to have a physically realistic value, not tuned to these measurements, the new model could be used as a sub-mechanism in models for other applications. The reaction network was generated with the open-source Reaction Mechanism Generator (RMG) software. Sensitivity analyses were performed under wide ranges of temperatures and pressures, allowing for the identification of the most impactful species and reactions. Based on these, a comprehensive thermochemistry database, including calculations on 550 molecules performed in this work at the CBS-QB3 level of theory, and a kinetic library, including theoretically-derived reaction rates retrieved from the literature, were built and used in the mechanism generation. The developed mechanism was compared against several existing detailed kinetic mechanisms for ethene, propene, 1-butene, 2-butene, and isobutene. Here, the newly generated model is the most accurate among the ones analyzed, in terms of fractional bias and normalized mean square error. Hence, this new model was used to analyze the chemistry of alkene combustion. Key rate coefficients were compared, to identify the cause of deviations between the models and possible areas for further improvements.
In contrast to the adage “Models are to be used, not believed”, combustion kinetics models have been intended to be predictive in nature. Theoretical chemical kinetics is now understood to provide a firm foundation for the reaction parameters, thereby facilitating predictive simulations of chemical reactivity, even in regimes that are poorly characterized by chemical kinetic and/or combustion experiments. In this study, we describe a theory-informed kinetics model (ThInK) for small molecule combustion chemistry (H 2 and C 1 – C 3 species) that is based on the prodigious use of theoretical predictions for reaction rate coefficients, thermochemistry, and transport parameters. The distinct features of this kinetics model, which was developed over the course of several decades, are illustrated through simulations of flame propagation and auto-ignition.
Electrochemical technologies have garnered intense interest in both academic research and industrial applications due to their potential to increase energy efficiency and reduce carbon footprint. However, electrochemical process fundamentals have been absent from the chemical process simulators available to millions of chemical engineers worldwide. To expedite process research and development of electrochemical technologies in the chemical industry, it is imperative that essential electrochemical process fundamentals be incorporated into process simulators to support modeling and simulation of electrochemical processes. Here, this study examines three process fundamentals key to the research and development of electrochemical processes: electrochemical reaction kinetics, electrolyte thermodynamics, and heat and mass transfer. It further illustrates application of these process fundamentals with a case study modeling an electrochemical process for the conversion of acrylonitrile to adiponitrile. The modeling example highlights the roles of applied voltage on reaction rates, and electron flow rates on the performance of electrochemical processes. It suggests that the applied voltage and electron flow rates are two unique concepts that should be included in simulators for electrochemical processes.
The Direct Discontinuous Galerkin (DDG (Liu and Yan, 2008)) method and a counterpart with Interface Correction (DDGIC (Danis and Yan, 2022)) are extended to compute diffusion terms that arise when solving the compressible multi-component flow equations in thermochemical nonequilibrium. Thermodynamic properties, transport properties, chemical reaction rates, and energy exchange terms are computed using Mutation++ (Scoggins et al., 2020). The DG method is applied on unstructured grids, where the accuracy and convergence rates can be sensitive to the numerical method chosen for parabolic terms. A method for determining the homogeneity tensor of the flow equations required for DDGIC is shown. The convergence properties of the DDG methods are studied and compared to the Interior Penalty (IP) method. A number of numerical experiments are conducted to assess the accuracy and performance of the method. The numerical results and convergence studies indicate that DDG and DDGIC provide accurate solutions and perform well for general flows in thermochemical nonequilibrium.
Potential energy surfaces (PES) for dimerization of acepyrene-pyrene, aceanthracene-anthracene, and acenaphthalene-naphthalene were computed by the G3(MP2,CC) and ONIOM2(G3(MP2,CC):B3LYP/6-31G(d)) methods. Close agreement of relative energies of corresponding species along reaction paths for different reactions was found suggesting that the PES for E-bridge formation depends weakly on the number of rings in the interacting monomers. Our results indicate that the reaction rate is controlled by the dynamics of the initial collision, which may depend on the monomer size. The ONIOM approach is proposed as an accurate tool for calculations of PES in large PAH systems being more reliable than various DFT methods.
Alkyl-substituted 1,3-dioxolanes, including 4,5-dimethyl-2-pentan-3-yl-1,3-dioxolane (Fuel 1), 4,5-dimethyl-2-pentyl-1,3-dioxolane (Fuel 2), and 2-(heptan-3-yl)-4,5-dimethyl-1,3-dioxolane (Fuel 3), have been recently suggested as potential biodiesels. In this paper, we investigate the initial pyrolysis of the alkyl-substituted 1,3-dioxolanes at high temperatures using ReaxFF molecular dynamics (MD) simulations. We analyze the decomposition rate, reaction mechanism, and product distribution in the pyrolysis of the three alkyl 1,3-dioxlanes. The three fuels primarily decompose to 4,5-dimethyl-1,3-dioxolane radical and hydrocarbons derived from the alkyl side-chains. The further decomposition of 4,5-dimethyl-1,3-dioxolane radical primarily leads to 2-C 4 H 8 and CO 2 within a few decomposition steps. The hydrocarbon product distribution is significantly affected by the molecular structure of the alkyl side-chain, which would have a strong influence on the sooting tendency of these fuels. The ReaxFF simulations predict that the order of sooting tendency would be Fuel 3 > Fuel 1 > Fuel 2, which agrees with the measured sooting tendency trend. Based on the pyrolysis mechanism identified by ReaxFF, we propose a new alkyl dioxolane, 4-hexyl-5-methyl-1,3-dioxolane (Fuel 4), which might produce even less soot, by modifying the molecular structure of Fuel 2. Our ReaxFF simulation shows that Fuel 4 produce much less C 4 H 8 , an effective non-aromatic soot precursor, than Fuel 2. Moreover, more carbon atoms are bonded to each oxygen atom in Fuel 4 than Fuel 2, which would help reduce soot yield by removing more carbon atoms from the soot-producing pool of species. The major decomposition pathways identified in this work can be used to develop chemical kinetic models for 1,3-dioxolane based compounds, as biodiesel components, applicable to combustion engine simulations. We also demonstrate that the chemical kinetic insight offered by ReaxFF simulations can be used to design new fuel molecules with more desired properties.
Ammonia is carbon-free and is regarded as a potential fuel to address global warming issues. Here, in this work, three-dimensional direct numerical simulations (DNS) of ammonia/air turbulent premixed flames were performed to explore the influence of turbulence and equivalence ratio on the flame structure and NO formation characteristics. Two equivalence ratios were considered, i.e. Φ=0.9 and Φ=1.1. The general flame structures were presented and species distributions were examined. The NO mass fraction was found to be the highest in the product for the lean case and in the reaction zone for the rich case. The conditional mean values of species mass fractions and reaction rates were compared with those of the unstrained and strained laminar premixed flames to explore how well the laminar flame structures can approach those of the turbulent flame. The budget analysis of the species transport equations showed that turbulent diffusion plays an important role in species transport. The turbulent diffusivity D T was estimated using the gradient hypothesis based on the DNS data. Various laminar flame simulations with different diffusivities were carried out. It was shown that the conditional means of the DNS agree well with those of the laminar flames including D T in the transport property calculation. The global and local NO formation characteristics were investigated. It was found that the mean NO production conditioned on the progress variable is lower compared with the corresponding laminar flame in the rich case. However, the relative contributions from various NO pathways are rarely affected by turbulence. The NO mass fraction is higher in negative curvature regions compared with positive curvature regions of the flame surface for the rich case, which is due to the preferential diffusion of H 2 and other radicals and the enhanced NO pathways in negatively curved regions.