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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

MAPPER – A NOVEL CAPABILITY TO SUPPORT NUCLEAR MODEL VALIDATION AND MAPPING OF BIASES AND UNCERTAINTIES

This paper overviews the initial results of a new project at the Oak Ridge National Laboratory -- supported via an internal seed funding program -- focused on the development of a novel computational capability, called MAPPER, to support model validation. The MAPPER development aims to eliminate the need for empirical criteria, such as similarity indices, often employed to identify applicable experiments for given application conditions. To achieve that, MAPPER employs an information-theoretic-based approach, based on the Kullback-Leibler (KL) divergence principle, to combine responses of available or to-be-built experiments with application responses of interest using a training set of samples generated using randomized execution of the experiments and the application high fidelity analysis models. These samples are condensed using reduced order modeling techniques in the form of a joint probability distribution function (PDF) connecting each application response of interest with a new effective experimental response. The initial focus of the MAPPER capability will be to support the confirmatory analysis required for criticality safety analysis of storage facilities which require k-eff biases to be known for safe operation. This paper reports some of the initial results obtained with MAPPER as applied to a set of critical experiments for which existing similarity-based methods have been shown to provide inaccurate estimates of the biases.

Mertyurek, Ugur↗
DOE OSTI2018

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MG Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

The Nucleon Axial Form Factor and Staggered Lattice QCD

The study of neutrino oscillation physics is a major research goal of the worldwide particle physics program over the upcoming decade. Many new experiments are being built to study the properties of neutrinos and to answer questions about the phenomenon of neutrino oscillation. These experiments need precise theoretical cross sections in order to access fundamental neutrino properties. Neutrino oscillation experiments often use large atomic nuclei as scattering targets, which are challenging for theorists to model. Nuclear models rely on free-nucleon amplitudes as inputs. These amplitudes are constrained by scattering experiments with large nuclear targets that rely on the very same nuclear models. The work in this dissertation is the rst step of a new initiative to isolate and compute elementary amplitudes with theoretical calculations to support the neutrino oscillation experimental program. Here, the eort focuses on computing the axial form factor, which is the largest contributor of systematic error in the primary signal measurement process for neutrino oscillation studies, quasielastic scattering. Two approaches are taken. First, neutrino scattering data on a deuterium target are reanalyzed with a model-independent parametrization of the axial form factor to quantify the present uncertainty in the free-nucleon amplitudes. The uncertainties on the free-nucleon cross section are found to be underestimated by about an order of magnitude compared to the ubiquitous dipole model parametrization. The second approach uses lattice QCD to perform a rst-principles computation of the nucleon axial form factor. The Highly Improved Staggered Quark (HISQ) action is employed for both valence and sea quarks. The results presented in this dissertation are computed at physical pion mass for one lattice spacing. This work presents a computation of the axial form factor at zero momentum transfer, and forms the basis for a computation of the axial form factor momentum dependence with an extrapolation to the continuum limit and a full systematic error budget.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

On the approximations underpinning fast nuclear cloud models

Fast nuclear cloud models have been used for many decades to predict cloud rise and growth following a nuclear detonation for both emergency response and debris collections mission planning. We present derivations and discussions of the approximations made by such fast nuclear cloud models in common use. In particular, the models we discuss all fundamentally rely on an empirical entrainment parameter to inexpensively model the cloud rise and growth of a buoyant bubble due to entrainment of an ambient air mass on its surface. We show the particular circumstances under which these models are equivalent as well as apply a virtual mass correction throughout them. Aside from some minor differences in representation and the use of the Boussinesq approximation, these models are all very similar and necessitate the specification of an entrainment parameter to accurately determine the cloud rise.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

Use of multiscale zirconium alloy deformation models in nuclear fuel behavior analysis

Accurate prediction of cladding mechanical behavior is a key aspect of modeling nuclear fuel behavior, especially for conditions of pellet-cladding interaction (PCI), reactivity-initiated accidents (RIA), and loss of coolant accidents (LOCA). Current approaches to fuel performance modeling rely on empirical constitutive models for cladding creep, growth and plastic deformation, which are limited to the materials and conditions for which the models were developed. To improve upon this approach, a microstructurally-based zirconium alloy mechanical deformation analysis capability is being developed within the United States Department of Energy Consortium for Advanced Simulation of Light Water Reactors (CASL). Specifically, the viscoplastic self-consistent (VPSC) polycrystal plasticity modeling approach, developed by Lebensohn and Tomé [1], has been coupled with the BISON engineering scale fuel performance code to represent the mechanistic material processes controlling the deformation behavior of light water reactor (LWR) cladding. A critical component of VPSC is the representation of the crystallographic nature (defect and dislocation movement) and orientation of the grains within the matrix material and the ability to account for the role of texture on deformation. A future goal is for VPSC to obtain information on reaction rate kinetics from atomistic calculations to inform the defect and dislocation behavior models described in VPSC. The multiscale modeling of cladding deformation mechanisms allowed by VPSC far exceed the functionality of typical semi-empirical constitutive models employed in nuclear fuel behavior codes to model irradiation growth and creep, thermal creep, or plasticity. This paper describes the implementation of an interface between VPSC and BISON and provides initial results utilizing the coupled functionality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Comparison of nuclear mass models at nuclearmasses.org

Nuclear masses are crucial in many astrophysics studies, as well as other areas of basic and applied nuclear science. There are now many different global theoretical models of nuclear masses - but choosing the best model for a given application can be challenging. The suite of codes online at nuclearmasses.org greatly facilitates the comparison of nuclear models with evaluated masses as well as the intercomparison of theoretical models. We demonstrate the utility of this suite by comparing the RMS deviations of 13 different theoretical mass models from the AME2003 evaluated masses over mass ranges appropriate for astrophysics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Comparison of Nuclear Mass Models at Nuclearmasses.org.

Nuclear masses are crucial in many astrophysics studies, as well as other areas of basic and applied nuclear science. There are now many different global theoretical models of nuclear masses - but choosing the best model for a given application can be challenging. The suite of codes online at nuclearmasses.org greatly facilitates the comparison of nuclear models with evaluated masses as well as the intercomparison of theoretical models. We demonstrate the utility of this suite by comparing the RMS deviations of 13 different theoretical mass models from the AME2003 evaluated masses over mass ranges appropriate for astrophysics.

Smith, Michael Scott↗
DOE OSTI2021

Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics

In this paper, we describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Nuclear Matrix Model: A path to nuclear physics from superstrings

We derive nuclear forces and nuclear density saturation from large N{sub c} QCD, by applying AdS/CFT correspondence of string theory, called holographic QCD. This is made possible by a new description of a multi-baryon system in the holographic QCD. The description employs a matrix quantum mechanics which can be derived via the correspondence. This talk is based on collaboration work with N. Iizuka and P. Yi [1], with N. Iizuka [2, 3] and with T. Morita [4].

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Effective matrix model for nuclear physics on a quantum computer

In this project we studied a simplified potential from [1] describing nuclear physics from quark and gluon fields, where spatial degrees of freedom have been integrated over. This potential deals with the phase structure of the strong interaction. We created a matrix representation of the Hamiltonian for this theory using discrete quantum mechanics [2]. We then feed this Hamiltonian to IBM's Qiskit package [3]. We run on IBM's 32-qubit quantum simulator, which approximates the ground state energy using the variational quantum eigensolver (VQE) algorithm [4]. This result is then compared with the exact ground state energy, which we can calculate classically since the matrices we use are still small enough. The bosonic Hamiltonian for a 6-qubit representation took over 1000 Pauli factors to represent fully. To improve our performance, we can look for a more efficient representation of this model's Hamiltonian. We will develop a better variational trial wave function and initial state, as well as include the effect of fermions and heavy quarks on the potential.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗