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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reactor physics benchmark experiments at the JSI TRIGA MARK II reactor - Current status and future outlook

Full text of publication follows. With the development of new high-fidelity computational methods, improvement of nuclear data, and multiphysics modelling, there is an increased need for benchmark experiments to experimentally validate the models, methods and input data. Many of the nuclear facilities designed to perform reactor physics benchmark experiments have been shut down. Therefore, research reactors offer a great opportunity for benchmark experiments, if they are well designed and performed with great care and accuracy. In this presentation we provide an overview of the past and ongoing activities related to benchmark experiments at the Jozef Stefan Institute TRIGA Mark II research reactor. The following experiments have been performed: criticality with fresh fuel, {sup 197}Au(n,γ) and {sup 27}Al(n,α) reaction rates in irradiation channels, absolute and relative {sup 197}Au(n,γ), {sup 235}U(n,f) and {sup 238}U(n,f) reaction rates in the core, burnup, kinetic parameters, control rod worth, isothermal reactivity coefficient, self-shielding, slow and fast (pulse) transients, nuclear heating, delayed and prompt gamma ray production, temperature profiles for multi-physics. Since the existing fleet of research reactors is ageing very rapidly and new experiments are needed, new research reactors should be designed and built to meet the needs of future advanced reactors, education and training, and other technologies in the coming years. We will review planned activities at the JSI TRIGA reactors and plans for the new research reactor in Slovenia. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Kinetics-Informed Neural Networks

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. Furthermore, this approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Operando neutron radiography validates a parameter-free transport–kinetics model for thick solid-state battery cathodes

Tortuosity-weighted interfacial flux for lithium (TWIF-Li) predicts through-thickness Li gradients in thick composite all-solid-state cathodes without fitted parameters. Image-derived microstructures, GITT-derived concentration-dependent solid diffusion, and tortuosity-weighted interfacial kinetics reproduce operando neutron radiography across practical rates, delivering transferable design rules to suppress transport-limited reaction fronts.

Adam, Andre [ORNL] (ORCID:0000000245023033)↗
DOE OSTI2024

Surface anchoring requirements for vanadia clusters on titanium oxide surfaces and their impact on activity for oxidative dehydrogenation of ethanol

Titanium oxide site requirements for the anchoring of catalytically active forms of dispersed vanadia species are investigated. Using a series of oxygen-modified titanium nitride materials, we systematically modify the available vanadia-anchoring moieties on the titanium-bearing support surface. This strategy in turn results in a very narrow size distribution of well-dispersed vanadia clusters anchored on the support. This synthesis technique ensures that only highly dispersed vanadia is present on the catalyst surface even at nominal monolayer coverages. A deeper insight into the catalytic behavior for ethanol partial oxidation of the active vanadia species is obtained by comparing intrinsic kinetic and thermodynamic parameters of kinetically relevant reaction steps (heat of ethanol adsorption, hydrogen abstraction activation energy, and the energetics of catalyst reoxidation). In conclusion, it is found that these parameters are dependent on the relative amount of oxygen in the titanium-bearing support and the resulting distribution of vanadia species, which validates the premise of a potential participation of lattice oxygen atoms of the titania support into the catalytic activity of active vanadia species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Parameter study of a Kinetic Phase Transition model

We investigate the ability of the two model parameters in the Kinetic Phase Transition (KPT) model of Carl Greeff to describe phase transitions of various types when combined with the tin Equation of states for the phases, SESAME 2162. While the two parameters are correlated, one combination can be associated with the nucleation process giving rise to the hysteresis seen in phase transition paths in pressure-temperature space and another can be associated with the overall timescale of the transition. We acknowledge the desire to have a model where the parameters are more independent of each other for easier model parameter determination.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Covalent Organic Framework (COF) Derived Ni–N–C Catalysts for Electrochemical CO 2 Reduction: Unraveling Fundamental Kinetic and Structural Parameters of the Active Sites

Electrochemical CO 2 reduction is a potential approach to convert CO 2 into valuable chemicals using electricity as feedstock. Abundant and affordable catalyst materials are needed to upscale this process in a sustainable manner. Nickel-nitrogen-doped carbon (Ni-N-C) is an efficient catalyst for CO 2 reduction to CO, and the single-site Ni-N x motif is believed to be the active site. However, critical metrics for its catalytic activity, such as active site density and intrinsic turnover frequency, so far lack systematic discussion. In this work, we prepared a set of covalent organic framework (COF)-derived Ni-N-C catalysts, for which the Ni-N x content could be adjusted by the pyrolysis temperature. The combination of high-angle annular dark-field scanning transmission electron microscopy and extended X-ray absorption fine structure evidenced the presence of Ni single-sites, and quantitative X-ray photoemission addressed the relation between active site density and turnover frequency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗
DOE OSTI2023

Structure and enzymatic characterization of CelD endoglucanase from the anaerobic fungus Piromyces finnis

Anaerobic fungi found in the guts of large herbivores are prolific biomass degraders whose genomes harbor a wealth of carbohydrate-active enzymes (CAZymes), of which only a handful are structurally or biochemically characterized. Here, we report the structure and kinetic rate parameters for a glycoside hydrolase (GH) family 5 subfamily 4 enzyme (CelD) from Piromyces finnis, a modular, cellulosome-incorporated endoglucanase that possesses three GH5 domains followed by two C-terminal fungal dockerin domains (double dockerin). We present the crystal structures of an apo wild-type CelD GH5 catalytic domain and its inactive E154A mutant in complex with cellotriose at 2.5 and 1.8 Å resolution, respectively, finding the CelD GH5 catalytic domain adopts the (β/α) 8 -barrel fold common to many GH5 enzymes. Structural superimposition of the apo wild-type structure with the E154A mutant-cellotriose complex supports a catalytic mechanism in which the E154 carboxylate side chain acts as an acid/base and E278 acts as a complementary nucleophile. Further analysis of the cellotriose binding pocket highlights a binding groove lined with conserved aromatic amino acids that when docked with larger cellulose oligomers is capable of binding seven glucose units and accommodating branched glucan substrates. Activity analyses confirm P. finnis CelD can hydrolyze mixed linkage glucan and xyloglucan, as well as carboxymethylcellulose (CMC). Measured kinetic parameters show the P. finnis CelD GH5 catalytic domain has CMC endoglucanase activity comparable to other fungal endoglucanases with k cat = 6.0 ± 0.6 s –1 and K m = 7.6 ± 2.1 g/L CMC. Enzyme kinetics were unperturbed by the addition or removal of the native C-terminal dockerin domains as well as the addition of a non-native N-terminal dockerin, suggesting strict modularity among the domains of CelD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Robust Methodology to Elucidate Kinetics of Room Temperature Electrochemical Propane Adsorption on Platinum

Electrocatalytic activation of alkanes can further decarbonize chemical manufacturing by leveraging affordable renewable electricity and readily available shale gas reserves in the United States. Earlier works have identified the unique role of Pt in adsorbing and activating alkanes, like propane, at room temperature in acidic, aqueous electrolytes, revealing spontaneous formation of deeply dehydrogenated propane-derived surface species with an intact C 3 - backbone. Although an adsorption mechanism was hypothesized, it has not been explicitly investigated to date, preventing the quantification of kinetic rate parameters. A robust methodology to investigate and benchmark propane adsorption kinetics on Pt is critical for the rational design of electrocatalysts that exhibit higher selectivity toward desired partially oxidized products. Herein, we analyze an oxidative current transience that appears during the adsorption of propane on Pt in aqueous electrochemical conditions and develop a methodology that elucidates the adsorption mechanism and enables quantification of rate parameters such as order dependences and apparent activation barriers. This method yields an expected first-order dependence with respect to propane concentration at low coverage and reveals a second-order dependence with respect to the concentration of surface active sites. Additionally, the apparent activation barrier for propane adsorption was calculated using an Arrhenius analysis of the current transience under temperature control. The experimentally measured activation barrier of 35 kJ mol –1 is in excellent agreement with the theoretical barrier calculated by density functional theory (DFT). The kinetic analysis was extended, via the use of transition state theory, to extract entropy and enthalpy of activation, yielding consistent results with the proposed two-step adsorption mechanism and DFT calculations. These results demonstrate reliable quantification of kinetic parameters for electrocatalytic activation of C–H bonds in alkanes that can be employed for rational catalyst development for a versatile range of electrocatalytic conditions.

alkane activation↗
DOE OSTI2020

Constraints on dark photons and axionlike particles from the SuperCDMS Soudan experiment

We present an analysis of electron recoils in cryogenic germanium detectors operated during the SuperCDMS Soudan experiment. The data are used to set new constraints on the axioelectric coupling of axion-like particles and the kinetic mixing parameter of dark photons, assuming the respective species constitutes all of the galactic dark matter. This study covers the mass range from 40 eV/$c^2$ to 500 eV/$c^2$ for both candidates, excluding previously untested parameter space for masses below ~1 keV/$c^2$. For the kinetic mixing of dark photons, values below $10^{-15}$ are reached for particle masses around 100 eV/$c^2$; for the axioelectric coupling of axion-like particles, values below $10^{-12}$ are reached for particles with masses in the range of a few-hundred eV/$c^2$.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Prediction of moisture transfer in cement-based materials: Use of a porous network model to access transfer parameters

Knowing the water transport phenomena is fundamental for understanding the durability of cement-based materials. The moisture content within cementitious materials is of primary importance because it has a direct influence on mechanical (shrinkage, creep) and chemical (penetration of aggressive agents) degradation. Many models based on the determination of the transfer properties of the material under study already exist but require fitting of the parameters to find the experimental kinetics. Transfer parameters are generally estimated from the empirical approaches developed for soils by Van Genuchten and the water vapour diffusion reduction factor is often calculated via Millington's semi-empirical model. In this paper, we make a proposal to improve the prediction of moisture transfer, through mechanisms such as drying, capillary imbibition and water vapour permeability in cement-based materials, by using a porous network model to access the transfer parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reorganization Energies for Interfacial Proton-Coupled Electron Transfer to a Water Oxidation Catalyst

The reorganization energy (λ) for interfacial electron transfer (ET) and proton-coupled electron transfer (PCET) from a conductive metal oxide (In 2 O 3 :Sn, ITO) to a surface-bound water oxidation catalyst were extracted from kinetic data measured as a function of the thermodynamic driving force. Visible light excitation resulted in rapid excited-state injection (k inj > 10 8 s -1 ) to the ITO, which photo-initiated the two interfacial reactions of interest. The rate constants for both reactions increased with the driving force, −ΔG°, to a saturating limit, k max , with rate constants consistently larger for ET than for PCET. Marcus-Gerischer analysis of the kinetic data provided the reorganization energy for interfacial PCET (0.90 ± 0.02 eV) and ET (0.40 ± 0.02 eV), respectively. The magnitude of k max for PCET was found to decrease with pH, behavior that was absent for ET. Both the decrease in k max and the larger reorganization energy for an unwanted competing PCET reaction from the ITO to the oxidized catalyst showcases a significant kinetic advantage for driving solar water oxidation at high pH. Computational analysis revealed a larger inner-sphere reorganization energy contribution for PCET than for ET arising from a more significant change in the Ru–O bond length for PCET. Extending Marcus-Gerischer theory to PCET by including the excited electron-proton vibronic states and the proton donor-acceptor motion provided an apparent reorganization energy of 1.01 eV. Furthermore, this study demonstrates that the Marcus-Gerischer theory initially developed for ET can be reliably extended to PCET for quantifying and interpreting reorganization energies observed experimentally.

Catalysts Kinetic parameters↗
DOE OSTI2021

Dark Higgs dark matter

A new U(1) dark gauge group coupled to the Standard Model (SM) via the kinetic mixing portal provides a natural dark matter candidate in the form of the Higgs field, hd, responsible for generating the mass of the dark photon, γ d . We show that the condition m h d ≤m γ d , together with smallness of the kinetic mixing parameter, ϵ, and/or dark gauge coupling, g d , leads the dark Higgs to be sufficiently metastable to constitute dark matter. We analyze the Universe's thermal history and show that both freeze-in, SM→{γ d ,h d }, and freeze-out, {γ d ,h d }→SM, processes can lead to viable dark Higgs dark matter with a sub-GeV mass and a kinetic mixing parameter in the range 10 –13 ≲ϵ≲10 –6 . Observable signals in astrophysics and cosmology include modifications to primordial elemental abundances, altered energetics of supernovae explosions, dark Higgs decays in the late Universe, and dark matter self-interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Uncertainty Quantification and Error Propagation in the Enthalpy and Entropy of Surface Reactions Arising from a Single DFT Functional

Accounting for parametric uncertainty in models is essential for quantifying the models’ predictive ability. Recently, approaches have been introduced to estimate parametric uncertainty in kinetic models while accounting for correlations among energy parameters. However, correlations have been estimated indirectly and correlations in entropies have not been accounted for. For surface-catalyzed microkinetic models of >C2 (more than two carbon-containing) molecules, which consist of thousands of reaction steps and intermediate surface species, first-principles density functional theory (DFT) is costly, and thus, estimation of thermochemistry and reaction barriers requires surrogate methods of DFT, such as group additivity and Brønsted–Evans–Polanyi relationships, respectively. For such parametrization, model uncertainty is unclear. This work develops a framework to overcome these gaps using group additivity and a single DFT functional. We estimate correlations in parameters of kinetic models and quantify uncertainty for thermochemistry, reaction barriers, reaction paths, and ultimately reaction rates, accounting also for the contribution of entropic uncertainty. Furthermore, the approach is illustrated on propane combustion and ethane oxidative dehydrogenation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Comprehensive uncertainty quantification (UQ) for full engineering models by solving probability density function (PDF) equation

This report details a new method for propagating parameter uncertainty (forward uncertainty quantification) in partial differential equations (PDE) based computational mechanics applications. The method provides full-field quantities of interest by solving for the joint probability density function (PDF) equations which are implied by the PDEs with uncertain parameters. Full-field uncertainty quantification enables the design of complex systems where quantities of interest, such as failure points, are not known apriori. The method, motivated by the well-known probability density function (PDF) propagation method of turbulence modeling, uses an ensemble of solutions to provide the joint PDF of desired quantities at every point in the domain. A small subset of the ensemble is computed exactly, and the remainder of the samples are computed with approximation of the driving (dynamics) term of the PDEs based on those exact solutions. Although the proposed method has commonalities with traditional interpolatory stochastic collocation methods applied directly to quantities of interest, it is distinct and exploits the parameter dependence and smoothness of the dynamics term of the governing PDEs. The efficacy of the method is demonstrated by applying it to two target problems: solid mechanics explicit dynamics with uncertain material model parameters, and reacting hypersonic fluid mechanics with uncertain chemical kinetic rate parameters. A minimally invasive implementation of the method for representative codes SPARC (reacting hypersonics) and NimbleSM (finite- element solid mechanics) and associated software details are described. For solid mechanics demonstration problems the method shows order of magnitudes improvement in accuracy over traditional stochastic collocation. For the reacting hypersonics problem, the method is implemented as a streamline integration and results show very good accuracy for the approximate sample solutions of re-entry flow past the Apollo capsule geometry at Mach 30.

42 ENGINEERING↗