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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A hybrid finite volume method and smoothed particle hydrodynamics approach for efficient and accurate blast simulations

Modeling strong shock waves in fluids remains a persistent challenge in computational physics. Essential to research efforts in industry and defense, numerous methods have been devised to improve the accuracy and efficiency of shock simulations. A novel, hybrid Finite Volume Method (FVM)-Smoothed Particle Hydrodynamics (SPH) approach is capable of further improving efficiency and retaining accuracy by exploiting the favorable characteristics of each respective method. This hybrid approach is presented for shock capturing in compressible fluids. The Python framework Pyro2 is employed to simulate a coarse FVM mesh, while the Python framework PySPH is utilized to model the fluid in regions with high gradients through SPH particles. The performance of the hybrid FVM-SPH scheme, compared to the individual FVM and SPH methods, is assessed in 1 kt and 10 kt blast simulations. Our results indicate that the hybrid approach offers higher computational efficiency than SPH while preserving its accuracy and characteristics. The hybrid approach had a relative speedup of 11.3x and 22.3x over the FVM and SPH approaches for the 1 kt simulation and a relative speedup of 14.7x and 20.9x over the FVM and SPH approaches for the 10 kt simulation. The hybrid SPH algorithm enables future compressible fluid simulations with more extensive capabilities than grid-based methods alone, presenting potential applications in modeling fluid-structure interactions and solid deformation and fracturing in blast simulations.

Myers, Conner↗
DOE OSTI2018

An Adaptive Fast Gauss Transform in Two Dimensions

A variety of problems in computational physics and engineering require the convolution of the heat kernel (a Gaussian) with either discrete sources, densities supported on boundaries, or continuous volume distributions. We present a unified fast Gauss transform for this purpose in two dimensions, making use of an adaptive quad-tree discretization on a unit square which is assumed to contain all sources. Our implementation permits either free-space or periodic boundary conditions to be imposed, and is efficient for any choice of variance in the Gaussian.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Proceedings of RIKEN/RBRC Workshop: Scientific Review Committee Meeting: October 30 - November 1, 2013 (Final Report) [Slides]

The thirteenth scientific evaluation of the RIKEN BNL Research Center (RBRC) took place at the Physics Building #510 at Brookhaven National Laboratory. The members of the Scientific Review Committee (SRC), present at the meeting, were: Prof. Sinya Aoki, Prof. Peter Braun-Munzinger, Prof. Kenichi Imai, Prof. Tetsuo Matsui, Prof. Richard Milner (Chair), Prof. Alfred Mueller, Prof. Charles Young Prescott, and Prof. Akira Ukawa. We are pleased that Dr. Hideto En’yo, the Director of the Nishina Institute of RIKEN, Japan, participated in this meeting both in informing the committee of the activities of the RIKEN Nishina Center for Accelerator-Based Science and the role of RBRC and as an observer of this review. In order to illustrate the breadth and scope of the RBRC program, each member of the Center made a presentation on his/her research efforts. This encompassed three major areas of investigation: theoretical, experimental and computational physics. In addition, the committee met privately with the fellows and postdocs to ascertain their opinions and concerns. Although the main purpose of this review is a report to RIKEN management on the health, scientific value, management and future prospects of the Center, the RBRC management felt that a compendium of the scientific presentations are of sufficient quality and interest that they warrant a wider distribution. Therefore we have made this compilation and present it to the community for its information and enlightenment. We thank Brookhaven National Laboratory and the U.S. Department of Energy* for providing the facilities to hold this meeting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2026

Hybrid Modeling Study on Grain Evolution in the Metal Welding Process and Its Potential Lunar Application

Metal is most commonly used structural material in a wide range of spacecraft, and welding is the principal method for joining metal components into functional systems. However, conducting welding experiments under extreme environments—such as microgravity or vacuum conditions in space—is prohibitively expensive and experimentally challenging. To overcome these limitations, multi-physics computational welding models provide a cost-effective and versatile alternative. In this work, the authors have developed a coupled thermal (fluid) microstructure simulation framework to model metal welding under varying gravity conditions. The framework integrates a mixed-mode heat transfer formulation (conduction, convection, and radiation) with molten pool fluid dynamics, enabling accurate prediction of temperature fields and weld-pool geometry. A grain growth model is further incorporated to capture the spatial and temporal evolution of microstructure, including grain size distribution and morphological transitions during solidification. This approach provides detailed insight into molten pool evolution and grain-level microstructure development throughout the welding process. By explicitly parameterizing environmental conditions, the model supports extrapolation to off-Earth manufacturing scenarios such as welding on the lunar surface. Tantalum—chosen in this study due to its high melting point, oxidation resistance, and mechanical stability at elevated temperatures—serves as the material system for model demonstration. Beyond Tantalum, the integrated multi-physics framework offers broad applicability for predictive welding simulations of various structural and refractory metals or alloys used in extreme terrestrial or extraterrestrial environments.

kinetic Monte Carlo (SPPARKS)↗
DOE OSTI2022

Nuclear Physics Exascale Requirements Review: An Office of Science Review sponsored jointly by Advanced Scientific Computing Research and Nuclear Physics, June 15 - 17, 2016, Gaithersburg, Maryland

Imagine being able to predict — with unprecedented accuracy and precision — the structure of the proton and neutron, and the forces between them, directly from the dynamics of quarks and gluons, and then using this information in calculations of the structure and reactions of atomic nuclei and of the properties of dense neutron stars (NSs). Also imagine discovering new and exotic states of matter, and new laws of nature, by being able to collect more experimental data than we dream possible today, analyzing it in real time to feed back into an experiment, and curating the data with full tracking capabilities and with fully distributed data mining capabilities. Making this vision a reality would improve basic scientific understanding, enabling us to precisely calculate, for example, the spectrum of gravity waves emitted during NS coalescence, and would have important societal applications in nuclear energy research, stockpile stewardship, and other areas. This review presents the components and characteristics of the exascale computing ecosystems necessary to realize this vision.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

PPT-GPU: Scalable GPU Performance Modeling

Performance modeling is a challenging problem due to the complexities of hardware architectures. In this paper, we present PPT-GPU, a scalable and accurate simulation framework that enables GPU code developers and architects to predict the performance of applications in a fast, and accurate manner on different GPU architectures. PPT-GPU is part of the open source project, Performance Prediction Toolkit (PPT) developed at the Los Alamos National Laboratory. We extend the old GPU model in PPT that predict the runtimes of computational physics codes to offer better prediction accuracy, for which, we add models for different memory hierarchies found in GPUs and latencies for different instructions. To further show the utility of PPT-GPU, we compare our model against real GPU device(s) and the widely used cycle-accurate simulator, GPGPU-Sim using different workloads from RODINIA and Parboil benchmarks. The results indicate that the predicted performance of PPT-GPU is within a 10% error compared to the real device(s). In addition, PPT-GPU is highly scalable, where it is up to 450x faster than GPGPU-Sim with more accurate results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Overview of the MCNP6® SQA Plan and Requirements [Memorandum]

For all X Computational Physics Division (XCP) software under the Associate Laboratory Directorate for Weapons Physics (ALDX), the Weapons Research Services Secure Networks and Assurance Group (WRS-SNA) manages the software quality assurance (SQA) plan, requirements and guidance with respect to development processes and tools to meet the broader LANL SQA requirements. Each XCP software product is categorized into one of three software types: Safety Software, Non-Safety Risk Significant Software, and Non-Safety Commercially Controlled Software. In 2018, using LANL Form 2033, the MCNP6 code was categorized by the XCP division as Non-Safety Commercially Controlled Software, provided in Appendix A. Using WRSFORM- 0001U, the MCNP6 code was graded as a Medium Impact software product, provided in Appendix B. Given these determinations, the WRS-AD-0010U SQA plan is followed for all MCNP6 developments, documentation and code releases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

NECP-Cypress: Development and Benchmarking of a 3D PWR Core-Analysis Code

As one of the key components of nuclear reactor design and safety analysis software for Pressurized Water Reactor (PWR), the advance of the core-analysis code indicates the nuclear power development of the corresponding country. Hence, Nuclear Engineering Computational Physics Lab. (NECP) developed a PWR core-analysis code NECP-Cypress. Currently, it contains several modules including a generalized cross section module, a diffusion module using the Variational Nodal Method (VNM), a depletion-calculation using the traditional macroscopic module, a thermal-hydraulics module solving the single channel heat transfer equation and 1D heat conduction equation. With the VNM employed, NECP-Cypress can eliminate the control rod cusping effect and provide a better pin power reconstruction. In this paper, we firstly introduce the modules of the code NECP-Cypress. Secondly, two large-scale PWR core benchmarks were employed to demonstrate the ability of the code by comparing with SIMULATE-3, including the BEAVRS problem and a pseudo CAP1400 reactor core problem. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations

One of the most promising applications of machine learning in computational physics is to accelerate the solution of partial differential equations (PDEs). The key objective of machine-learning-based PDE solvers is to output a sufficiently accurate solution faster than standard numerical methods, which are used as a baseline comparison. Here, we first perform a systematic review of the ML-for-PDE-solving literature. Out of all of the articles that report using ML to solve a fluid-related PDE and claim to outperform a standard numerical method, we determine that 79% (60/76) make a comparison with a weak baseline. Second, we find evidence that reporting biases are widespread, especially outcome reporting and publication biases. We conclude that ML-for-PDE-solving research is overoptimistic: weak baselines lead to overly positive results, while reporting biases lead to under-reporting of negative results. To a large extent, these issues seem to be caused by factors similar to those of past reproducibility crises: researcher degrees of freedom and a bias towards positive results. We call for bottom-up cultural changes to minimize biased reporting as well as top-down structural reforms to reduce perverse incentives for doing so.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Model Structural Inference Using Local Dynamic Operators

This paper focuses on the problem of quantifying the effects of model-structure uncertainty in the context of time-evolving dynamical systems. This is motivated by multi-model uncertainty in computer physics simulations: developers often make different modeling choices in numerical approximations and process simplifications, leading to different numerical codes that ostensibly represent the same underlying dynamics. We consider model-structure inference as a two-step methodology: the first step is to perform system identification on numerical codes for which it is possible to observe the full state; the second step is structural uncertainty quantification, in which the goal is to search candidate models "close" to the numerical code surrogates for those that best match a quantity of interest (QOI) from some empirical data sets. Here, we (1) define a discrete, local representation of the structure of a partial differential equation, which we refer to as the "local dynamical operator" (LDO); (2) identify model structure nonintrusively from numerical code output; (3) nonintrusively construct a reduced-order model (ROM) of the numerical model through POD-DEIM-Galerkin projection; (4) perturb the ROM dynamics to approximate the behavior of alternate model structures; and (5) apply Bayesian inference and energy conservation laws to calibrate a LDO to a given QOI. We demonstrate these techniques using the two-dimensional rotating shallow water equations as an example system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient Space–Time Reduced Order Model for Linear Dynamical Systems in Python Using Less than 120 Lines of Code

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Large Eddy Simulation Study of Flow Turbulence, Alumina Transport, and Bath Temperature Evolution in Conventional Aluminum-Smelting Cell Using OpenFOAM

In this study, a Large Eddy Simulation (LES) of the aluminum-smelting process is performed using OpenFOAM. To understand the coupled behavior of heat transfer, mass transfer, and flow of the smelting process, a multi-physics computational fluid dynamics (CFD) model based on the Eulerian–Eulerian multi-fluid approach is adopted. The model accounts for CO 2 bubble and magnetohydrodynamics (MHD)-driven flow, along with alumina dissolution, transport, and bath temperature evolution. The simulation predictions show small-scale turbulent vortical structures in the anode–cathode space caused by combined effect of MHD and CO 2 bubble-bath interactions and relatively large-scale asymmetric vortices in the inter-anode space caused by the CO 2 bubble-bath interactions. The vortex formation at the edges of the anodes evidently aids in transporting alumina from the central channel to the bottom of the anodes and prevents accumulation of gas bubbles in the periphery of the anode bottom. Symmetric bath cold spots are observed in the vicinity of the feeder. Cold spots are also observed in the anode–cathode distance space below the anode bottom due to the transport of undissolved solid to this region by the flow. The findings from the work are useful in developing and designing alumina-feeding strategy leading to reduced anode effects and smooth operation of the cell. The work also highlights the important flow structures in conventional aluminum-smelting cell.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Benchmark solution of the dynamic response of a spherical shell at finite strain

Our paper describes the development of high fidelity solutions for the study of homogeneous (elastic and inelastic) spherical shells subject to dynamic loading and undergoing finite deformations. The goal of the activity is to provide high accuracy results that can be used as benchmark solutions for the verification of computational physics codes. Furthermore, the equilibrium equations for the geometrically non-linear problem are solved through mode expansion of the displacement field and the boundary conditions are enforced in a strong form. Time integration is performed through high-order implicit Runge–Kutta schemes. Finally, we evaluate accuracy and convergence of the proposed method by means of numerical examples with finite deformations and material non-linearities and inelasticity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Generalized fiducial inference on differentiable manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Code-verification techniques for hypersonic reacting flows in thermochemical nonequilibrium

The study of hypersonic flows and their underlying aerothermochemical reactions is particularly important in the design and analysis of vehicles exiting and reentering Earth's atmosphere. Computational physics codes can be employed to simulate these phenomena; however, code verification of these codes is necessary to certify their credibility. To date, few approaches have been presented for verifying codes that simulate hypersonic flows, especially flows reacting in thermochemical nonequilibrium. In this work, we present our code-verification techniques for verifying the spatial accuracy and thermochemical source term in hypersonic reacting flows in thermochemical nonequilibrium. Additionally, we demonstrate the effectiveness of these techniques on the Sandia Parallel Aerodynamics and Reentry Code (SPARC).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Data preservation at the Fermilab Tevatron

The Fermilab Tevatron collider's data-taking run ended in September 2011, yielding a dataset with rich scientific potential. The CDF and D0 experiments each have approximately 9 PB of collider and simulated data stored on tape. A large computing infrastructure consisting of tape storage, disk cache, and distributed grid computing for physics analysis with the Tevatron data is present at Fermilab. The Fermilab Run II data preservation project intends to keep this analysis capability sustained through the year 2020 and beyond. To achieve this goal, we have implemented a system that utilizes virtualization, automated validation, and migration to new standards in both software and data storage technology and leverages resources available from currently-running experiments at Fermilab. Lastly, these efforts have also provided useful lessons in ensuring long-term data access for numerous experiments, and enable high-quality scientific output for years to come.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗