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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Projection algorithm for state preparation on quantum computers

We present an efficient method to prepare states of a many-body system on quantum hardware, first isolating individual quantum numbers and then using time evolution to isolate the energy. Our method in its simplest form requires only one additional auxiliary qubit. The total time evolved for an accurate solution is proportional to the ratio of the spectrum range of the trial state to the gap to the lowest excited state, a substantial improvement over other projection algorithms, and the accuracy increases exponentially with the time evolved. Isolating the quantum numbers is efficient because of the known eigenvalues and increases the gap thus shortening the propagation time required. The success rate of the algorithm, or the probability of producing the desired state, is a simple function of measurement times and phases and is dominated by the square overlap of the original state to the desired state. We present examples from the nuclear shell model and the Heisenberg model. In conclusion, we compare this algorithm to previous algorithms for short evolution times and discuss potential further improvements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Exascale Co-Design Center for Materials in Extreme Environments (ExMatEx) Annual Report — Year 2

All activities of the Exascale Co-design Center for Materials in Extreme Environments (ExMatEx) are focused on the two ultimate goals of the project: (1) demonstrating and delivering a prototype scale-bridging materials science application based upon adaptive physics refinement, and (2) identifying the requirements for the exascale ecosystem that are necessary to perform computational materials science simulations (both single- and multi-scale). During the first year of ExMatEx, our focus was on establishing how we do computational materials science, by developing an initial suite of flexible proxy applications. These “proxy apps” are the primary vehicle for the co-design process, involving assessments and tradeoff evaluations both within the ExMatEx team, and with the entire exascale ecosystem. These interactions have formed the basis of our second year activities. The set of artifacts from these co-design interactions are the lessons learned, that are used to re-express the applications and algorithms within the context of emerging architectures, programming models, and runtime systems.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Exascale Co-Design Center for Materials in Extreme Environments (ExMatEx) Annual Report - Year 2

All activities of the Exascale Co-design Center for Materials in Extreme Environments (Ex- MatEx) are focused on the two ultimate goals of the project: (1) demonstrating and delivering a prototype scale-bridging materials science application based upon adaptive physics refinement, and (2) identifying the requirements for the exascale ecosystem that are necessary to perform computational materials science simulations (both single- and multi-scale). During the first year of ExMatEx, our focus was on establishing how we do computational materials science, by developing an initial suite of flexible proxy applications. These “proxy apps” are the primary vehicle for the co-design process, involving assessments and tradeoff evaluations both within the ExMatEx team, and with the entire exascale ecosystem. These interactions have formed the basis of our second year activities. The set of artifacts from these co-design interactions are the lessons learned, that are used to re-express the applications and algorithms within the context of emerging architectures, programming models, and runtime systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces

The objective of this project is to develop, and integrate, high-performance simulation tools capable of predicting plasma-facing component (PFC) operating lifetime and the impact of the evolving surface morphology of tungsten-based PFCs on plasma contamination, including the dynamic recycling of fuel species and tritium retention, in future magnetic fusion devices. Establishing a fundamental physical understanding and developing predictive capabilities of plasma-surface interactions (PSI) requires simultaneously addressing complex and diverse physics occurring over a wide range of length (Angstroms to meters) and time (femtoseconds to years) scales, as well as integrating extensive physical processes across the plasma–surface interface. This requires development of not only detailed physics models and computational strategies at each scale, but also algorithms and methods to couple them effectively in a way that can be robustly validated. Deploying these tools requires the continued development and coupling of leadership-scale computational codes to describe the boundary plasma and the evolving PFC surface, as well as a host of simulations that bridge disparate scales to address complex physical and computational issues at the plasma–surface interface in multi-component materials systems for magnetic fusion energy development beyond ITER.

36 MATERIALS SCIENCE↗
DOE OSTI2022

hPIC2: A hardware-accelerated, hybrid particle-in-cell code for dynamic plasma-material interactions

The exascale era of high performance computing promises to bring the field of computational plasma physics ever closer to the goal of accurate multiscale modeling. Such computers will rely on hardware acceleration to offload work to dedicated components, notably general-purpose graphics processing units (GPUs). However, devices from different manufacturers require software to be written with different parallel programming models, greatly increasing the code maintenance burden of applications designed to perform on more than one such device. hPIC2 is a hybrid plasma simulation code developed with the Kokkos performance portability framework to target the architectures that will drive exascale computing for the foreseeable future. As a hybrid simulation code, hPIC2 investigates the simultaneous use of various plasma models on the same domain, at the same time. hPIC2 also optionally couples to RustBCA, which accurately models ion-material interactions using the binary collision approximation (BCA) method. In conclusion, hPIC2 therefore achieves scalable performance on a variety of computing architectures when simulating complex and diverse plasmas, particularly near plasma-material interfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Diversity in computing technologies and strategies for dynamic resource allocation

Here, High Energy Physics (HEP) is a very data intensive and trivially parallelizable science discipline. HEP is probing nature at increasingly finer details requiring ever increasing computational resources to process and analyze experimental data. In this paper, we discuss how HEP provisioned resources so far using Grid technologies, how HEP is starting to include new resource providers like commercial Clouds and HPC installations, and how HEP is transparently provisioning resources at these diverse providers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Preliminary Phase Field Computational Model Development

This interim report presents progress towards the development of meso-scale models of magnetic behavior that incorporate microstructural information. Modeling magnetic signatures in irradiated materials with complex microstructures (such as structural steels) is a significant challenge. The complexity is addressed incrementally, using the monocrystalline Fe (i.e., ferrite) film as model systems to develop and validate initial models, followed by polycrystalline Fe films, and by more complicated and representative alloys. In addition, the modeling incrementally addresses inclusion of other major phases (e.g., martensite, austenite), minor magnetic phases (e.g., carbides, FeCr precipitates), and minor nonmagnetic phases (e.g., Cu precipitates, voids). The focus of the magnetic modeling is on phase-field models. The models are based on the numerical solution to the Landau-Lifshitz-Gilbert equation. From the computational standpoint, phase-field modeling allows the simulation of large enough systems that relevant defect structures and their effects on functional properties like magnetism can be simulated. To date, two phase-field models have been generated in support of this work. First, a bulk iron model with periodic boundary conditions was generated as a proof-of-concept to investigate major loop effects of single versus polycrystalline bulk iron and effects of single non-magnetic defects. More recently, to support the experimental program herein using iron thin films, a new model was generated that uses finite boundary conditions representing surfaces and edges. This model has provided key insights into the domain structures observed in magnetic force microscopy (MFM) measurements. Simulation results for single crystal thin-film iron indicate the feasibility of the model for determining magnetic domain wall thickness and mobility in an externally applied field. Because the phase-field model dimensions are limited relative to the size of most specimens used in experiments, special experimental methods were devised to create similar boundary conditions in the iron films. Preliminary MFM studies conducted on single and polycrystalline iron films with small sub-areas created with focused ion beam have correlated quite well qualitatively with phase-field simulations. However, phase-field model dimensions are still small relative to experiments thus far. We are in the process of increasing the size of the models and decreasing specimen size so both have identical dimensions. Ongoing research is focused on validation of the phase-field model. Validation is being accomplished through comparison with experimentally obtained MFM images (in progress), and planned measurements of major hysteresis loops and first order reversal curves. Extrapolation of simulation sizes to represent a more stochastic bulk-like system will require sampling of various simulations (i.e., with single non-magnetic defect, single magnetic defect, single grain boundary, single dislocation, etc.) with distributions of input parameters. These outputs can then be compared to laboratory magnetic measurements and ultimately to simulate magnetic Barkhausen noise signals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Energy and physical resource impacts of quantum computing merit greater attention

Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry

Here we present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric magnetic field with a target rotational transform on the magnetic axis. This approach differs from the traditional two-stage approach in which first a magnetic configuration with desirable physics properties is found, and then coils to approximately realize this magnetic configuration are designed. The proposed single-stage approach allows us to find a compromise between confinement and engineering requirements, i.e., find easy-to-build coils with good confinement properties. Using forward and adjoint sensitivities, we derive derivatives of the physical quantities in the objective, which is constrained by a nonlinear periodic differential equation. In two numerical examples, we compare different gradient-based descent algorithms and find that incorporating approximate second-order derivative information through a quasi-Newton method is crucial for convergence. We also explore the optimization landscape in the neighborhood of a minimizer and find many directions in which the objective is mostly flat, indicating ample freedom to find simple and thus easy-to-build coils.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Constant-temperature anemometry measurements in hypersonic boundary layers

One of the major unresolved issues in fluid dynamics is the nature of apparent stresses, called Reynolds stresses, which occur in turbulent boundary layers. In hypersonic boundary layers, the flow physics is further complicated by the large temperature and density fluctuations and the concomitant contamination of the Reynold stresses by fictitious terms. Because of the severe flow environment and the extraordinary demands on sensors and instruments, the turbulence characteristics of hypersonic boundary layers were studied in only a cursory fashion. The plans for supersonic (HSCT) and hypersonic (NASP) vehicles made supersonic flow physics one of the critical pacing technologies in aerospace science. In particular, experimental data are needed to verify candidate computer models and to reach an important understanding of the turbulence physics. The presented research is the start of a substantial effort to refine existing instrumentation and develop experimental techniques to measure the various component of the Reynolds stress in hypersonic boundary layers.

Spina, Eric F.↗
DOE OSTI2017

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iterates between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. As a result, equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Quantum Information Science: Factual Document for the Office of Basic Energy Sciences at the Department of Energy

For decades, scientists have known that devices to store and manipulate information in quantum physical systems such as atoms or photons would provide radical new capabilities in computing, communication, and sensing. These novel capabilities include speed-ups in scientific calculations of quantum materials and chemical systems, enhanced resolution in imaging and detection systems, and greater security for encrypted communication systems. Building these devices requires a detailed understanding of how quantum materials behave, accurate knowledge of how to integrate them, and high-fidelity control of the physical qubits that store information. These goals are fundamental research challenges for materials synthesis, fabrication, and characterization; and the science of quantum information intersects heavily with ongoing basic research into materials science, chemistry, and quantum physics. These systems represent state-of-the-art approaches to building and controlling the basic physical elements of quantum information. Research into quantum sensing and quantum communication strongly leverages these advances for next-generation technologies, e.g., measurements beyond the Heisenberg limit, while applications of quantum computing (QC) as a platform for understanding future quantum material systems highlight its potential for advanced scientific discovery. This factual status document for Quantum Information Science has been developed for the Department of Energy Office of Science, Basic Energy Sciences (BES). The following are several seminal or key references for interested readers; the reference section gives a more exhaustive, but still not complete, list of important papers in this area.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Flexible Computer Science Infrastructure (FleCSI)

FleCSI is a compile-time configurable framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. Current support includes multi-dimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures. FleCSI also introduces a functional programming model with control, execution, and data abstractions that are consistent with both MPI and state-of-the-art task-based runtimes such as Legion and Charm++. The FleCSI abstraction layer provides the developer with insulation from the underlying runtime, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. The control and execution models in FleCSI also provide formal nomenclature for describing poorly understood concepts like kernels and tasks.

Moss, Nicholas↗
DOE OSTI2024

Brochure on the 2024 ASCR Workshop on Analog Computing for Science

Analog computing fundamentally differs from digital by representing data with fully continuous physical quantities, such as voltages, probabilities, chemical concentrations, or light intensities, rather than encoding values in discrete binary states. While digital computing has historically excelled in precision, scalability, and noise resistance, newer analog approaches are gaining interest for their potential to dramatically improve energy efficiency and processing speed. Analog systems can inherently solve mathematical problems through their physical behavior and offer distinct advantages in scenarios where continuous operations are more effective than Boolean logic.

97 MATHEMATICS AND COMPUTING↗