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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Li1−xNiO2 Many-body DMC Benchmark Dataset

The dataset contains all numerical data generated in support of the manuscript “Many‑body Benchmark of Electronic Charge and Spin Densities for Li1–xNiO2​” (Journal of Chemical Theory and Computation, DOI: 10.1021/acs.jctc.5c02097, URL: https://pubs.acs.org/doi/10.1021/acs.jctc.5c02097). The materials included in this repository are: 1. Data files used to produce all figures and tables in the main manuscript and supporting information. 2. Benchmark density‑functional theory (DFT) datasets used for the charge‑ and spin‑density analyses. 3. Reference many‑body diffusion Monte Carlo (DMC) calculations and associated input/output files.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational analysis of the tryptophan cation radical energetics in peroxidase Compound $\mathrm{I}$

Three well-characterized heme peroxidases (cytochrome c peroxidase = CCP, ascorbate peroxidase = APX, and Leishmania major peroxidase = LMP) all have a Trp residue tucked under the heme stacked against the proximal His heme ligand. The reaction of peroxidases with H 2 O 2 to give Compound I results in the oxidation of this Trp to a cationic radical in CCP and LMP but not in APX. Considerable experimental data indicate that the local electrostatic environment controls whether this Trp or the porphyrin is oxidized in Compound I. Attempts have been made to place the differences between these peroxidases on a quantitative basis using computational methods. These efforts have been somewhat limited by the approximations required owing to the computational cost of using fully solvated atomistic models with well-developed forcefields. This now has changed with available GPU computing power and the associated development of software. Here we employ thermodynamic integration and multistate Bennett acceptance ratio methods to help fine-tune our understanding on the energetic differences in Trp radical stabilization in all three peroxidases. These results indicate that the local solvent structure near the redox active Trp plays a significant role in stabilization of the cationic Trp radical.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Kinetic Plasma Simulation in the MOOSE Framework: Verification of Electrostatic Particle In Cell Capabilities

In magnetic confinement nuclear fusion reactors, the interaction between the plasma edge and plasma facing components is extremely important. At the plasma edge, a kinetic representation such as particle-in-cell (rather than a fluid representation) is required to accurately capture the plasma behavior. General purpose particle-in-cell plasma simulation capabilities have been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. This new capability is a part of the development of a new MOOSE-based framework for modeling plasma facing components, the Fusion ENergy Integrated multiphys-X (FENIX) framework. In this work, the verification of foundational particle-in-cell capabilities in FENIX is presented. This new plasma simulation capability has three main components: moving particles in discrete steps on the finite element mesh, mapping charge density from the particle's location to the finite element mesh, and solving for the electrostatic potential based on the charge density mapped from particles to the mesh. In this paper, simple verification problems demonstrating each of these new capabilities are presented, and future work includes electromagnetic capabilities and Monte Carlo collisions with neutral gas particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2024

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Solving key challenges in collider physics with foundation models

Foundation models are neural networks that are capable of simultaneously solving many problems. Large language foundation models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new foundation model for hadronic jets to solve three key challenges in collider physics. In particular, we show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet foundation models beyond proof-of-principle studies and into the toolkit of practitioners.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
DOE OSTI2015

High performance techniques for micro channel plate computations in photo multipliers

Monte Carlo - MC methods, requiring heavy computations of large data volume, can be used to predict the amplitude distribution and variance of outputted electron charge in Micro Channel Plates - MCPs, which are fed with primary emitted electrons in their inputs. The concept of parallel and serial combination of separate stages of such stochastic processes, which can be applied in MCP systems and simulated using MC computations, has recently proposed and shown interesting theorems and results. Separating the electron multiplication procedures, occurring in the channels of MCP systems, into sub-procedures, which are similar in nature and occur either in parallel or in consecutive manners, is equivalent to splitting the relevant stochastic system calculations into smaller ones, which can be performed concurrently and/or sequentially. In this way, calculations similar in nature, can be executed only once, thus, eliminating a great percentage of heavy computations. However, when designing and implementing the calculation inferences of the derived theorems using High Performance Computation - HPC techniques, the overall computation cost can further and significantly be reduced. In this paper, the hardware mappings of the relevant theorem inferences onto parallel computing architectures, resulting in circuits of computing blocks for calculation purposes of MCP system stochastic processes, are investigated. Each of the designed and implemented computing circuits corresponds to a theorem inference or to a combination of inferences. The parallelism of the proposed circuits consists of both the concurrent functioning of all the system blocks and of the simultaneous operation of each block, which is processing pieces of data, for many parts of the whole circuit in which it belongs. All the circuits are designed and tested using Altera Quartus II environment and can be implemented as chips, which can be incorporated in computing devices to assist Monte Carlo computations for the estimation of different MCP electron multiplication system performances.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Tackling Climate Change with Machine Learning

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Nevada National Security Site Site-Directed Research and Development FY 2014 Annual Report

The reports contained herein are for project activities that occurred from October 2013 through September 2014. Project life cycle is indicated under the title as well as the original proposal number (in the following format: site abbreviation--ID #--originating fiscal year; e.g., STL-03-14). Each of the reports describes in detail the discoveries, achievements, and challenges encountered by our principal investigators. As SDRD, by definition, invests in “high-risk” and hopefully “high-payoff” research, the element of uncertainty is inherent. While many of our efforts are “successful” and result in positive outcomes or technology utilization, some fall short of expectations, but cannot be construed as “failure” in the negative sense. The latter is a natural and valid part of the process of advanced research and often leads to unforeseen new pathways to future discovery. Regardless, either result advances our knowledge base and increases our ability to identify solutions and/or avoid costly and unwarranted paths for future challenges. In summary, the SDRD program continues to provide an unfettered mechanism for innovation that returns multifold to our customers, to national security, and to the general public. The program is a vibrant R&D innovation engine, benefited by its discretionary pedigree, enhanced mission spectrum, committed resources, and sound competitiveness to yield maximum taxpayer benefit. The 25 projects described exemplify the creativity and ability of a diverse scientific and engineering talent base. The efforts also showcase an impressive capability and resource that can be brought to find solutions to a broad array of technology needs and applications relevant to the NNSS mission and national security. Further SDRD performance metrics can be found in the appendix at the end of this report.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum Computing for High-Energy Physics: State of the Art and Challenges

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Uncertainty Quantification of Activation Due to Cross Section Data in Neutron Shielding Calculation

Quantification of activation in structure materials around the nuclear reactor through calculation is necessary before start of decommissioning. Then a neutron shielding calculation is carried out to estimate activation of structural materials. Neutron shielding calculation is performed out for spatially large regions around a reactor through deep penetration calculation, calculation result would have significant uncertainty. In order to minimize total amount of structure materials treated as radioactive waste, quantitative estimation on uncertainty of structural activation and its reduction is important. The uncertainties due to calculation arises from that of input data and that of calculation method. Examples of uncertainty of input data is those for nuclear reaction cross section and atom density of the nuclide in the structure. In this study, we focus on the uncertainty of cross section, which would be one of the dominant uncertainties, and try to estimate uncertainty of activation due to cross section data in a neutron shielding calculation. The random sampling method (RS) is used as the uncertainty quantification method. With the RS method, uncertainty estimation can be carried out without modification of computer codes for a neutron shielding calculation. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2017

Basic Research Needs for Energy and Water: Report of the Office of Basic Energy Sciences Basic Research Needs Workshop for Energy and Water

Water is precious; it has no substitute as a vital resource. It is essential not only for life, but also for practically all forms of economic activity. The food we eat, the clothes we wear, the goods we use, and the electricity we consume all require water. Water — in both quantity and quality — is one of the world’s most critical resources. The global importance of water presents enormous opportunity for innovation spurred by basic science to better understand at a fundamental level energy, water, and their interrelationship. Energy-water systems, whether manufactured or part of the natural world, exhibit extraordinary dynamics and complexities. To make possible a transformation to a future where fundamental processes underlying energy-water systems are quantified and harnessed for societal benefits, understanding of the chemical processes and materials underlying the interdependence of energy and water must be advanced. These interdependent energy and water issues (energy-water issues) intersect with national and international priorities. The challenges we face for energy-water systems are, in fact, an opportunity to shape a sustainable future for the nation and the world and present an enormous opportunity for fundamental energy science. Basic research — spanning chemistry, physics, geosciences, biosciences, and materials sciences — is essential to answering many questions related to energy and water systems. It is now clear that ensuring robust and secure energy and water systems will require a significant basic scientific research effort. Science questions that underpin water resiliency and water-energy systems span 12 orders of magnitude in length scale, from the nature and implications of hydrogen bonding, to the state and movement of water through aquifers. Addressing challenging questions requires advances and integration of basic chemical, geological, biological, material, and computational sciences. Recognizing the tremendous scope and scale of basic research needs in the energy-water domain, the U.S. Department of Energy’s Office of Science, Office of Basic Energy Sciences (BES), sponsored a workshop on “Basic Research Needs for Energy and Water,” which was held near Washington, D.C., January 4–6, 2017. Attended by more than 100 leading national and international scientific experts in chemistry, physics, materials sciences, biosciences, and geosciences related to the energy-water system, the workshop addressed basic research necessary to improve water use for industrial applications and energy production and to increase fit-for-purpose water availability and energy efficiency as well as crosscutting scientific research needs. The workshop identified four Priority Research Directions (PRDs) that will lay the foundation to better understand materials and processes relevant to energy-water issues and harness their potential for technological impact.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Requirements for a network storage service

Sandia National Laboratories provides a high performance classified computer network as a core capability in support of its mission of nuclear weapons design and engineering, physical sciences research, and energy research and development. The network, locally known as the Internal Secure Network (ISN), was designed in 1989 and comprises multiple distributed local area networks (LAN's) residing in Albuquerque, New Mexico and Livermore, California. The TCP/IP protocol suite is used for inner-node communications. Scientific workstations and mid-range computers, running UNIX-based operating systems, compose most LAN's. One LAN, operated by the Sandia Corporate Computing Directorate, is a general purpose resource providing a supercomputer and a file server to the entire ISN. The current file server on the supercomputer LAN is an implementation of the Common File System (CFS) developed by Los Alamos National Laboratory. Subsequent to the design of the ISN, Sandia reviewed its mass storage requirements and chose to enter into a competitive procurement to replace the existing file server with one more adaptable to a UNIX/TCP/IP environment. The requirements study for the network was the starting point for the requirements study for the new file server. The file server is called the Network Storage Services (NSS) and is requirements are described in this paper. The next section gives an application or functional description of the NSS. The final section adds performance, capacity, and access constraints to the requirements.

Kelly, Suzanne M.↗