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Results for “Nuclear Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cold-cap structure in a slurry-fed electric melter

We report as glass batch is charged into an electric melter, a cold cap forms on the glass melt surface. Heat transfer to the cold cap from the molten glass below and the melter atmosphere above determines the melting rate. A mathematical model of the cold cap and the experimental kinetic data of the feed-to-glass conversion that were collected for several simulated low-activity and high-level waste melter feeds allowed us to develop relationships between the internal structure of the cold cap, its properties, its thickness, and the internal heat transfer. This contribution shows the distribution of major crystalline phases and the cumulative evolution of gases within the cold cap. It also examines the temperature, conversion degree, and heating rate the melter feed is experiencing during the passage through the cold cap and their effects on the cold-cap bottom temperature and morphology, which are important for the computational fluid dynamics simulations of melters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Charged skyrmions and topological origin of superconductivity in magic-angle graphene

Topological solitons, a class of stable nonlinear excitations, appear in diverse domains as in the Skyrme model of nuclear forces. Here, we argue that similar excitations play an important role in a remarkable material obtained on stacking and twisting two sheets of graphene. Close to a magic twist angle, insulating behavior is observed, which gives way to superconductivity on doping. Here, we propose a unifying description of both observations. A symmetry breaking condensate leads to the ordered insulator, while topological solitons in the condensate—skyrmions—are shown to be charge 2 e bosons. Condensation of skyrmions leads to a superconductor, whose physical properties we calculate. More generally, we show how topological textures can mitigate Coulomb repulsion and provide a previously unexplored route to superconductivity. Our mechanism not only clarifies why several other moiré materials do not show superconductivity but also points to unexplored platforms where robust superconductivity is anticipated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Impact of symmetry energy on sound speed and spinodal decomposition in dense neutron-rich matter

Using a meta model for nuclear Equation of State (EOS) with its parameters constrained by astrophysical observations and terrestrial nuclear experiments, we examine effects of nuclear EOS especially its symmetry energy $E$ sym ($ρ$) term on the speed of sound squared $C$ $^2_s$($ρ$) and the critical density ρt where $C$ $^2_s$($ρ_t$) vanishes (indicating the onset of spinodal decomposition) in both dense neutron-rich nucleonic matter relevant for relativistic heavy-ion collisions and the cold $n$ + $p$ + $e$ + $μ$ matter in neutron stars at $β$- equilibrium. Unlike in nucleonic matter with fixed values of the isospin asymmetry $δ$, in neutron stars with a density dependent isospin profile $δ$($ρ$) determined consistently by the $β$-equilibrium and charge neutrality conditions, the $C$ $^2_s$($ρ$) almost always show a peak and then vanishes at $ρ_t$. Furthermore, the latter strongly depends on the high-density behavior of $E$ sym ($ρ$) if the skewness parameter $J$ 0 characterizing the stiffness of high-density symmetric nuclear matter (SNM) EOS is not too far above its currently known most probable value of about -190 MeV inferred from recent Bayesian analyses of neutron star observables. Moreover, in the case of having a super-soft $E$ sym ($ρ$) that is decreasing with increasing density above about twice the saturation density of nuclear matter, the $ρ$ $t$ is significantly lower than the density where the $E$ sym ($ρ$) vanishes (indicating the onset of isospin-separation instability and pure neutron matter formation) in neutron star cores.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Analyzing the effect of pressure on the properties of point defects in $\gamma\mathrm{U–Mo}$ through atomistic simulations

Uranium–molybdenum (U–Mo) alloys in monolithic fuel foil are the primary candidate for the conversion of high-performance research reactors in the USA. Monolithic fuel is utilized in a plate-type design with a zirconium diffusion barrier and aluminum cladding. These fuel types are unique in that they contain no plenum for the release of fission gases, which, in conjunction with the aluminum cladding, can lead to large stress states within the fuel. The nature of how fundamental processes of radiation damage, including the evolution of point defects, under such stresses occur is unknown. In this work, we present molecular dynamics simulations of the formation energy of point defects under applied stress. We report this work will allow for the implementation of stress-dependent microstructural evolution models of nuclear fuels, including those for both fission gas bubble growth and creep, which are critical to ensure the stable and predictable behavior of research reactor fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Modeling Systematic Discrepancies in Nuclear-Data Measurements with Machine Learning

Nuclear data and their associated co-variances are constantly being reevaluated as techniques improve and as new experimental data, as well as nuclearmodel developments, emerge. A standard technique used to evaluate mean values in nuclear data, and their associated covariances, is the generalized linear least squares (GLLS) method. Aligning with recent efforts to incorporate measurement features into nuclear data evaluation, we augment GLLS by including a linear term which attempts to predict potential systematic discrepancies in experimental data as related to the measurement features. Due to the general nature of this augmentation, we are able to apply this evaluation to three key observables: neutroninduced fission cross sections, the average prompt neutron multiplicity, and the prompt-fission neutron spectrum of ²³⁹Pu.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Calculated Roughness Changes due to Clear-Cutting At Savannah River Site

At the Savannah River Site (SRS), multiple facilities are routinely producing and working with radiological constituents which require safety basis modeling by the National Nuclear Safety Administration. The models used to determine the safety basis employ roughness length as an input parameter which describes the roughness of the ground surface which influences momentum exchange near the surface and the subsequent generation of mechanical turbulence. Because roughness length is dependent on surface characteristics, including vegetation, any changes to the surface would have a subsequent impact on the roughness length and require a new safety basis analysis to be performed. An analysis of measurements prior to and after the dear-cuts and thinning was performed and roughness lengths were calculated for each of sixteen wind sectors. Not all sectors contained dear-cuts, so this provides comparison of natural variations in roughness. Based on the results of the estimated changes, consideration will be given to how these changes may affect atmospheric dispersion modeling. It was found that clear-cutting activities generally led to a reduction in roughness. Reductions in roughness length also occurred in some of the wind sectors containing only undisturbed forest, but those in sectors with dear-cuts were found to be slightly greater in magnitude than natural variation. Only in one instance was the change in roughness length found to exceed a decrease of 10%, indicating that there is no significant safety basis to the site as a whole based on previous clear-cutting activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling Astrophysical Explosions and Nucleosynthesis (Final Technical Report)

The goal of this project was to further our theoretical understanding of the extreme physics and heavy element nucleosynthesis occurring in stellar explosions, in particular supernovae and neutron star mergers. To study these systems, we further developed and applied numerical simulations (radiation hydrodynamical models and nuclear reaction networks) run on high performance computers. The numerical calculations and companion theoretical analysis helped to better define the sites of astrophysical nucleosynthesis, and make predictions of the associated observables (nucleosynthetic yields and electromagnetic signals) that can be used to anticipate and interpret experimental data. This project enhances the scientific value of the nuclear data obtained from current and future experimental facilities (e.g., rare isotope beams) by improving our understanding of the astrophysical context and specific physical conditions under which we expect such reactions to take place in Nature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

High Quality Doping of GaN Through Transmutation Processing

This project led to several outstanding achievements: - Computational modeling of nuclear reactions, including estimates of the production ratio and doping concentration using TALYS 1.96 based on nuclear reactions in GaN induced by gamma-ray and protons. For photonuclear transmutation doping, simulations proved that Zn/Ge ratio with 40 MeV is higher than the other energies. - Proton transmutation doping was shown to be a potential nuclear source to fabricate p-type gallium-based semiconductor materials, examined during new irradiations with Oak Ridge National Laboratory and Brookhaven National Laboratory (in 200 MeV H - –Linac). - Patent disclosure to the University of Missouri-Columbia about healing effects in GaN by radiations. - Zn-doped p-type GaN through photonuclear transmutation doping process. - The PIs made an oral presentation on the p-type characteristics of Zn-doped GaN at the Materials Research Society Fall meeting in Boston. - The last irradiation process was in progress in collaboration with Argonne National Laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2022

Methodology of the Extraction of Multi-Differential Cross Sections of Charged-Current $\nu_\mu$-Argon Interactions in MicroBooNE Using Wire-Cell $\nu_\mu$CC Selection

Neutrino physics experiments rely on accurate nuclear-interaction models that are in part guided by experimental observations. Multi-differential cross section measurements are particularly valuable for exploring the underlying physics described in these models, making them a significant step forward in the field. This note presents methodology in preparation to unfold neutrino flux-averaged double and triple-differential cross sections of the inclusive muon neutrino charged-current interaction on Argon. This work builds upon the existing framework and methodology presented in recent energy dependent cross section measurements at MicroBooNE using the Wire-Cell tomographic event reconstruction. The signal definition, choice of binning, and handling of estimated detector uncertainties are discussed, and the MicroBooNE simulation model is validated over the multi-dimensional phase space of muon momentum, muon polar angle, and visible hadronic energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SAGIPS: A scalable Framework for scidac quantom

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Thomas Jefferson National Accelerator Facility and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event level by taking into leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. This presentation gives an overview over of the current status of this workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel [Thomas Jefferson National Accelera↗
DOE OSTI2025

2020 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.

97 MATHEMATICS AND COMPUTING↗