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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]

The ChemCam instrument of Curiosity uses laser-induced breakdown spectroscopy (LIBS). It fires a laser at target and vaporizes rock surfaces, creating a plasma. Three spectrographs divide the plasma light into wavelengths for chemical analysis: ultraviolet, violet, and visible near-infrared. Regression methods (SVR, PCR, CNN) have been employed for calibration (prediction of the elemental composition of samples); however, labeled ChemCam samples are limited. Here, we focus on unsupervised learning and employ generative models from ChemCam analysis. Further, we use labels (supervised) in combination to the generative model to compute uncertainties related to predictions. We report generative modeling can be successfully applied to model real-world data. Normalizing flow models can be efficiently constructed on latent spaces for fast downstream inference. Unsupervised and supervised learning can be combined to form an uncertainty quantification framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

JANA2 LDRD (Final Project Report)

The project sought to develop a second generation of the JANA reconstruction framework that leverages modern programming features (C++11 and C++14) while maintaining its basic user centric design. The goals included making the project easy to deploy to large HPC facilities such as NERSC and to include detailed documentation to allow scientists outside of Jefferson Lab to use it at such a facility. This would position JLab to play a key role in the larger Laboratory based computing ecosystem under the ASCR initiative. The experience gained will help inform the design of Exascale class applications for experimental nuclear physics. Developing a next generation JANA provides an advantage over older serial frameworks or ones retro-fitted to provide some multi-threading support. That technique requires significantly more memory than multi-threading which leads to compute jobs being limited by available RAM, and not fully utilizing the available CPU. The smaller memory footprint of multi-threaded programs also leads to increased efficiency due to fewer cache misses. It is for these and other reasons that multi-threading is the more accepted industry standard in the modern era.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

NEAMS Workbench Workflows

Modeling and simulation efforts frequently involve loosely coupled workflows. Automation of workflows, data exchanges, and simulation software execution is a productive and often desired feature or improvement for enhancing consistency of results and productivity of the analyst. It facilitates cross-collaboration of different analysts of multidisciplinary simulation by streamlining data exchange processes across local and high performance computing resources. This report proposes a high-level, extensible workflow module to enable consistent and efficient use of tools integrated into the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench with as little user intervention as possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Project Description for Student Symposium 2022: Modeling Approach to Critical in the Upcoming CERBERUS Experiment

The Critical Experiment Reflected By copper to BettEr Understand Scattering [CERBERUS] is an integral experiment that will be executed at the end of FY23. The experiment is designed to study the intermediate energy range, in particular elastic and inelastic scattering of neutrons in copper. Approaching criticality safely is of utmost importance, and having a computer mode is a useful tool when determining changes that can safely be made to the experiment. Multiple simulations are needed to analyze the approach to critical, and manually creating MCNP input files is both tedious and error prone. To solve this problem, a python script was created to develop and run these input files.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

PAGOSA Mesh Block Calculator

A method for automatically generating the “mesh namelist block” portion of a PAGOSA input file, which specifies the boundaries and zone sizes of the computational mesh, has been developed. This application standardizes the mesh block for 2-, and 3-dimensional problems and is computed after the user specifies the extent and resolution of the mesh and the number of processors which will be used to run the problem. The inputs may be provided directly through a series of command-line guided questions, or a separate input file. The mesh block is written to both standard output and to disk.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sensitivity-based Similarity Metrics for New Experiment Design Optimization

The nuclear data used in advanced reactor simulations requires validation. Data from nuclear criticality experiments can provide this validation. New nuclear criticality experiment design requires extensive knowledge and expert judgement such that the experimental design parameters are selected in such a way to keep the experiment subcritical. To aide in this experimental design process, professionals can utilize sensitivity and uncertainty analysis. Sensitivity and uncertainty analysis relies on matching new application experiments with currently existing benchmark experiments. Currently, there is functionality in the Whisper 1.1 software package to calculate a similarity metric based on neutron multiplication factor sensitivity coefficients between a new application designed by the user and existing International Criticality Safety Benchmark Experiment Project (ICSBEP) benchmarks. The Whisper 1.1 software package is included in Monte Carlo N-Particle ® Code Version 6.21 (MCNP ® 6.2). This work is geared toward expanding this capability to new similarity metrics based on beta-effective sensitivity coefficients and reactivity coefficient sensitivity coefficients. While the investigation of these sensitivity coefficients is presented in detail in separate works at this same conference, this work will be primarily focused on studying the similarity metrics in more detail. These similarity metrics will then be incorporated into the optimization algorithms used for experiment design in EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data), which is a Los Alamos National Laboratory (LANL) project designed to constrain nuclear data of interest, such that adjustments can be made to possible inaccuracies. A more detailed optimization can be subsequently performed by breaking down these similarity metrics by isotope, reaction, and energy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Celestial soft currents at one-loop and their OPEs

Conformally soft operators and their associated soft theorems on the celestial sphere encode the low energy behaviour of bulk scattering amplitudes. They lead to an infinite dimensional symmetry algebra of the celestial CFT at tree-level. In this paper, focusing our attention to Yang-Mills theory, we introduce new operators in the boundary celestial CFT in order to extend the definition of conformally soft currents to include one-loop effects. We then compute their OPEs with other operators in the theory. We also examine new subtleties that arise in defining OPEs of two conformally soft operators. We elucidate the connection between the new operators and loop corrected soft theorems in the bulk. Finally, we conclude by demonstrating how these operators fit into the framework of a logarithmic CFT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Nearly optimal state preparation for quantum simulations of lattice gauge theories

Here, we present several improvements to the recently developed ground-state preparation algorithm based on the quantum eigenvalue transformation for unitary matrices (QETU), apply this algorithm to a lattice formulation of U(1) gauge theory in (2+1) dimensions, as well as propose an alternative application of QETU, a highly efficient preparation of Gaussian distributions. The QETU technique was originally proposed as an algorithm for nearly optimal ground-state preparation and ground-state energy estimation on early fault-tolerant devices. It uses the time-evolution input model, which can potentially overcome the large overall prefactor in the asymptotic gate cost arising in similar algorithms based on the Hamiltonian input model. We present modifications to the original QETU algorithm that significantly reduce the cost for the cases of both exact and Trotterized implementation of the time evolution circuit. We use QETU to prepare the ground state of a U(1) lattice gauge theory in two spatial dimensions, explore the dependence of computational resources on the desired precision and system parameters, and discuss the applicability of our results to general lattice gauge theories. We also demonstrate how the QETU technique can be utilized for preparing Gaussian distributions and wave packets in a way which outperforms existing algorithms for as little as n q ≳ 2–5 qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Evolution of Two-Phase High Explosive Reactive Flow [Slides]

Reactive hydrodynamics involve rapid conversion of reactants to products along a detonation wave and needs a closure rule: P-T equilibrium. To calculate P-T equilibrium, we use a root finding method, which can be a computational bottleneck. We evolve the guess for the root finder rather than using the previous value to reduce iterations. Evolving products volume fraction Φ p for an initial root finding guess does reduce root finding iterations and seems to give the best results immediately following a detonation wave (reducing root finding iterations from 5 to 2).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

SCEPTRE 2.6 User's Guide

Sandia’s Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) is a computer code that solves the linear Boltzmann transport equation, particularly targeting coupled photon-electron problems. It uses unstructured finite element meshes in space, multigroup in energy, and discrete ordinates (S n ) or other methods in angle. SCEPTRE uses an xml-based input file to specify the problem. This report documents the options and syntax of that input file.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Performance Analysis of the Expected Track Length Estimator

This work investigates whether performance of the MCNP code is improved by the implementation of the expected track length estimator. The expected track length estimator is known to decrease variance when compared to the track length estimator in many classes of Monte Carlo particle transport simulations. However, the computational cost of computing an exponent for every particle collision within a tallied cell has previously been considered prohibitively expensive. Therefore, the goal of this work is to determine whether the variance improvements seen by the expected track length estimator in most cases makes up for the increase in transport time when implemented in the MCNP code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Computational shock formation & development: An arbitrary Lagrangian-Eulerian characteristics approach [Slides]

We propose a new computational shock formation-development algorithm. We use a “good” geometry adapted to the evolving solution, along with a good set of variables defined in this geometry. We are able to: (1) Accurately capture the pre-shock; (2) Track distinguished characteristics and capture weak discontinuities; (3) Approximate solutions to classical Riemann problems; and (4) Accurately solve challenging problems for which standard methods fail.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

SCEPTRE 2.7 User's Guide

Sandia’s Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) is a computer code that solves the linear Boltzmann transport equation, particularly targeting coupled photon-electron problems. It uses unstructured finite element meshes in space, multigroup in energy, and discrete ordinates (Sn) or other methods in angle. SCEPTRE uses an xml-based input file to specify the problem. This report documents the options and syntax of that input file.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Charon User Manual (V.2.1) (Rev.01)

This manual gives usage information for the Charon semiconductor device simulator. Charon was developed to meet the modeling needs of Sandia National Laboratories and to improve on the capabilities of the commercial TCAD simulators; in particular, the additional capabilities are running very large simulations on parallel computers and modeling displacement damage and other radiation effects in significant detail. The parallel capabilities are based around the MPI interface which allows the code to be ported to a large number of parallel systems, including linux clusters and proprietary "big iron" systems found at the national laboratories and in large industrial settings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Coordinate transformation methodology for simulating quasistatic elastoplastic solids

Molecular dynamics simulations frequently employ periodic boundary conditions where the positions of the periodic images are manipulated in order to apply deformation to the material sample. For example, Lees-Edwards conditions use moving periodic images to apply simple shear. Here, we examine the problem of precisely comparing this type of simulation to continuum solid mechanics. We employ a hypo-elastoplastic mechanical model, and develop a projection method to enforce quasi-static equilibrium. We introduce a simulation framework that uses a fixed Cartesian computational grid on a reference domain, and imposes deformation via a time-dependent coordinate transformation to the physical domain. As a test case for our method, we consider the evolution of shear bands in a bulk metallic glass using the shear transformation zone theory of amorphous plasticity. We examine the growth of shear bands in simple shear and pure shear conditions as a function of the initial preparation of the bulk metallic glass.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Error-mitigated data-driven circuit learning on noisy quantum hardware

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗