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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum computing universal thermalization dynamics in a (2 + 1)D Lattice Gauge Theory

Simulating non-equilibrium phenomena in strongly-interacting quantum many-body systems, including thermalization, is a promising application of near-term and future quantum computation. By performing experiments on a digital quantum computer consisting of fully-connected optically-controlled trapped ions, we study the role of entanglement in the thermalization dynamics of a Z 2 lattice gauge theory in 2+1 spacetime dimensions. Using randomized-measurement protocols, we efficiently learn a classical approximation of non-equilibrium states that yields the gap-ratio distribution and the spectral form factor of the entanglement Hamiltonian. These observables exhibit universal early-time signals for quantum chaos, a prerequisite for thermalization. Our work, therefore, establishes quantum computers as robust tools for studying universal features of thermalization in complex many-body systems, including in gauge theories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2019

Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa Workshop Report

In the last few decades, high-throughput technologies using various “omics” have enabled unprecedented views of biological systems at the molecular level. In parallel, the integration of omic datasets using computational modeling has provided new understanding of biological processes in organisms relevant to the U.S. Department of Energy’s (DOE) missions in energy and the environment. Collectively, these developments have spearheaded the advancement of systems biology, which can be viewed as a holistic approach for deciphering the complexity of biological systems. However, as high-throughput omic technologies and integrative systems biology efforts have improved our understanding of some biological systems, analyzing and finding meaningful answers within these massive datasets remain extremely challenging, in large part due to the lack of fundamental knowledge of gene function. Indeed, all sequenced genomes, both microbes and plants, contain large numbers of genes of “unknown function” that significantly limit scientists’ ability to model, predict, and engineer organisms with enhanced functions relevant to DOE. Current methodologies can be employed to decipher gene function, but they are typically slow, laborious, inefficient, and not scalable. This “bottleneck” in genome understanding could be broken with new, innovative, and transformative experimental tools, datasets, and computation that can define gene function on a massive and high-throughput scale compatible with the pace of DNA sequencing. In light of this grand challenge, DOE’s Office of Biological and Environmental Research (BER) convened the Breaking the Bottleneck of Genomes: Understanding Gene Function Across Taxa workshop on November 1–2, 2018. This workshop brought together leaders in microbiology, plant sciences, technology, and computation, who collectively identified the experimental and data analysis gaps preventing large-scale gene function determination as well as opportunities for overcoming these gaps.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Introduction to the Glenn Icing Computational Environment (GlennICE)

The NASA John H. Glenn Research Center at Lewis Field is developing the Glenn Icing Computational Environment (GlennICE) tool to aid those evaluating, designing and certifying aircraft, engines, and aircraft components for flight in icing conditions. This short course will walk through some of the underlying physics involved with GlennICE and how we achieve efficient 3D ice accretion predictions. After an introduction of GlennICE, an analysis of the Common Research Model High-Lift will be demonstrated to showcase the typical workflow for an aircraft icing analysis. Within this walkthrough, capabilities will be highlighted with future planned capabilities being discussed. Finally, we will showcase the impact GlennICE is having on NASA’s icing portfolio and how it is advancing aircraft icing research and safety.

Icing↗
DOE OSTI2023

Benchmarking the PCMCI Causal Discovery Algorithm for Spatiotemporal Systems

Causal discovery algorithms construct hypothesized causal graphs that depict causal dependencies among variables in observational data. While powerful, the accuracy of these algorithms is highly sensitive to the underlying dynamics of the system in ways that have not been fully characterized in the literature. In this report, we benchmark the PCMCI causal discovery algorithm in its application to gridded spatiotemporal systems. Effectively computing grid-level causal graphs on large grids will enable analysis of the causal impacts of transient and mobile spatial phenomena in large systems, such as the Earth’s climate. We evaluate the performance of PCMCI with a set of structural causal models, using simulated spatial vector autoregressive processes in one- and two-dimensions. We develop computational and analytical tools for characterizing these processes and their associated causal graphs. Our findings suggest that direct application of PCMCI is not suitable for the analysis of dynamical spatiotemporal gridded systems, such as climatological data, without significant preprocessing and downscaling of the data. PCMCI requires unrealistic sample sizes to achieve acceptable performance on even modestly sized problems and suffers from a notable curse of dimensionality. This work suggests that, even under generous structural assumptions, significant additional algorithmic improvements are needed before causal discovery algorithms can be reliably applied to grid-level outputs of earth system models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

CLIPS on the NeXT computer

This paper discusses the integration of CLIPS into a hybrid expert system neural network AI tool for the NeXT computer. The main discussion is devoted to the joining of these two AI paradigms in a mutually beneficial relationship. We conclude that expert systems and neural networks should not be considered as competing AI implementation methods, but rather as complimentary components of a whole.

Charnock, Elizabeth↗
DOE OSTI2020

Analytical and Computational Advances, Opportunities, and Challenges in Marine Organic Biogeochemistry in an Era of “Omics”

Advances in sampling tools, analytical methods, and data handling capabilities have been fundamental to the growth of marine organic biogeochemistry over the past four decades. There has always been a strong feedback between analytical advances and scientific advances. However, whereas advances in analytical technology were often the driving force that made possible progress in elucidating the sources and fate of organic matter in the ocean in the first decades of marine organic biogeochemistry, today process-based scientific questions should drive analytical developments. Several paradigm shifts and challenges for the future are related to the intersection between analytical progress and scientific evolution. Untargeted “molecular headhunting” for its own sake is now being subsumed into process-driven targeted investigations that ask new questions and thus require new analytical capabilities. However, there are still major gaps in characterizing the chemical composition and biochemical behavior of macromolecules, as well as in generating reference standards for relevant types of organic matter. Field-based measurements are now routinely complemented by controlled laboratory experiments and in situ rate measurements of key biogeochemical processes. And finally, the multidisciplinary investigations that are becoming more common generate large and diverse datasets, requiring innovative computational tools to integrate often disparate data sets, including better global coverage and mapping. Here, we compile examples of developments in analytical methods that have enabled transformative scientific advances since 2004, and we project some challenges and opportunities in the near future. We believe that addressing these challenges and capitalizing on these opportunities will ensure continued progress in understanding the cycling of organic carbon in the ocean.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Meshing, Visualization, and Computational Environments

There was probably no higher profile application of meshing, visualization, and computational software this past year than that performed in support of the space shuttle's Return to Flight activities. After heavy use of simulation during the accident investigation, these same tools are now helping address safety and operational issues. In turn, these applications are raising the bar for computational tools in terms of simulation fidelity and turnaround time.

Chawner, John R.↗
NASA NTRS2018

Unstructured Grid Adaptation and Solver Technology for Turbulent Flows

Unstructured grid adaptation is a tool to control Computational Fluid Dynamics (CFD) discretization error. However, adaptive grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Issues that prevent the use of adaptive grid methods are identified by applying unstructured grid adaptation methods to a series of benchmark cases. Once identified, these challenges to existing adaptive workflows can be addressed. Unstructured grid adaptation is evaluated for test cases described on the Turbulence Modeling Resource (TMR) web site, which documents uniform grid refinement of multiple schemes. The cases are turbulent flow over a Hemisphere Cylinder and an ONERA M6Wing. Adaptive grid force and moment trajectories are shown for three integrated grid adaptation processes with Mach interpolation control and output error based metrics. The integrated grid adaptation process with a finite element (FE) discretization produced results consistent with uniform grid refinement of fixed grids. The integrated grid adaptation processes with finite volume schemes were slower to converge to the reference solution than the FE method. Metric conformity is documented on grid/metric snapshots for five grid adaptation mechanics implementations. These tools produce anisotropic boundary conforming grids requested by the adaptation process.

Park, Michael A.↗
NASA NTRS1974

Enhancing simulation of efficiency with analytical tools

Some means of combining both computer simulation and anlytical techniques are indicated in order to mutually enhance their efficiency as design tools and to motivate those involved in engineering design to consider using such combinations. While the idea is not new, heavy reliance on computers often seems to overshadow the potential utility of analytical tools. Although the example used is drawn from the area of dynamics and control, the principles espoused are applicable to other fields. In the example the parameter plane stability analysis technique is described briefly and extended beyond that reported in the literature to increase its utility (through a simple set of recursive formulas) and its applicability (through the portrayal of the effect of varying the sampling period of the computer). The numerical values that were rapidly selected by analysis were found to be correct for the hybrid computer simulation for which they were needed. This obviated the need for cut-and-try methods to choose the numerical values, thereby saving both time and computer utilization.

Seltzer, S. M.↗
DOE OSTI2024

Tunable Metamaterials for Impact Mitigation

Traditional methods of shielding fragile goods and human tissues from impact energy rely on isotropic foam materials. The mechanical properties of these foams are inferior to an emerging class of metamaterials called plate lattices, which have predominantly been fabricated in simple 2.5-dimensional geometries using conventional methods that constrain the feasible design space. Here in this work, additive manufacturing is used to relax these constraints and realize plate lattice metamaterials with nontrivial, locally varying geometry. The limitations of traditional computer-aided design tools are circumvented and allow the simulation of complex buckling and collapse behaviors without a manual meshing step. By validating these simulations against experimental data from tests on fabricated samples, sweeping exploration of the plate lattice design space is enabled. Numerical and experimental tests demonstrate plate lattices absorb up to six times more impact energy at equivalent densities relative to foams and shield objects from impacts ten times more energetic while transmitting equivalent peak stresses. In contrast to previous investigations of plate lattice metamaterials, designs with nonuniform geometric prebuckling in the out-of-plane direction is explored and showed that these designs exhibit 10% higher energy absorption efficiency on average and 25% higher in the highest-performing design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Generic Advanced Computing Framework for Executing Windows-based Dynamic Contingency Analysis Tool in Parallel on Cluster Machines

Dynamic contingency analysis tool (DCAT) has been developed to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. By including more customized protection models and corrective actions into the Windows-based commercial tools, DCAT can help operators understand the cascading behavior and find mitigation approaches to reduce the risk of cascading outages in a more realistic manner. In order to further enhance the capability of DCAT, this paper presents a design of an advanced computing framework that enables DCAT to run on a cluster machine to improve its computational performance. This framework is generic and can be applied to other Windows-based simulation tools to fill the technical gap of applying advanced computing technology to vendors' Windows-based tools. The preliminary tests using medium to large power systems have shown the effectiveness of this framework and its potential for accelerating the adoption of advanced computing in utilities.

Advanced computing, dynamic contingency analysis, ↗
DOE OSTI2019

Computational and Experimental Benchmarking for Transient Fuel Testing

In 2018 the Transient Reactor Test (TREAT) Facility was restarted. This now-operational facility brings back world-class transient fuel testing capabilities to the U.S. Among a number of parallel efforts prior to the TREAT Facility’s restart a Department of Energy (DoE) Integrated Research Project (IRP) was funded for the purpose of benchmarking the TREAT with several goals in mind: To produce a comprehensive evaluation of existing TREAT Facility neutronics data using the next generation reactor core neutronics codes in accordance with established guidelines per the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhEP). To perform a benchmark using ready-use computational fluid dynamics tools against that of a historically collected sodium loop experiment collected in the TREAT. To design, develop and test a new experimental thermal hydraulic system of a representative TREAT Facility water flow loop. And benchmark the data collected against that produced via RELAP5-3D and TRACE. To develop a comprehensive instrumentation plan for the TREAT Facility by benchmarking performing of advanced in-pile instruments in the Massachusetts Institute of Technology Research Reactor (MITR) and the TREAT. The integrated team brought together collaborating members from three universities including Oregon State University (OSU), the University of Michigan (UM), and Massachusetts Institute of Technology (MIT); three national laboratories, Idaho National Laboratory, Argonne National Laboratory (ANL), and Oak Ridge National Laboratory (ORNL); and two industry partners, TerraPower (TP) and Harris Thermal Transfer Products (HTTP). A brief summary of accomplishments resulting from this IRP includes two filed patents, more than 40 conference papers, two published benchmark reports through IRPhEP, a new representative thermal hydraulic test facility alike the in-pile PWR water flow loop for TREAT and the first advanced instruments tested within the TREAT core upon its restart. The papers presented herein were developed and provide a summary of the significant technical accomplishments which align with the four project’s objectives. The collaborators has significant support from countless parties including the operational and technical staff at TREAT to ensure that their outcomes most impactfully aligned with program needs. Lastly the collaborators would like to acknowledge the fiscal support of the DoE’s Office of Nuclear Energy under work package identification number NU-15-OR-OSU- 0701-01. This report contains selected papers from the Computational and Experimental Benchmarking for Transient Fuel Testing Integrated Research Project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Computational diagnostics for flame acceleration and transition to detonation in a hydrogen/air mixture

A new computational diagnostic method for pressure-induced compressibility is proposed by projecting its local contribution to the chemical explosive mode (CEM) in the chemical explosive mode analysis (CEMA) framework. The new method is validated for the study of detonation development during the deflagration-to-detonation transition (DDT) process. The flame characteristics are identified through the quantification of individual CEM contributions of chemical reaction, diffusion, and pressure-induced compressibility. Numerical simulations are performed to investigate the DDT processes in a stoichiometric hydrogen-air mixture. A Godunov algorithm, fifth-order in space, and third-order in time are used to solve the fully compressible Navier-Stokes equations on a dynamically adapting mesh. A single-step, calibrated chemical diffusive model (CDM) described by Arrhenius kinetics is used for energy release and conservation between the fuel and the product. The new diagnostic method is first applied to onedimensional (1D) canonical flame configurations followed by two-dimensional (2D) simulations of DDT in an obstructed channel where different detonation initiation scenarios are examined using the new CEMA projection formulation. Detailed examinations of the idealized configuration of detonation initiation through shock focusing mechanism at a flame front are also studied using the new formulation. A comparison of the currently proposed CEMA projection and the original formulation by the authors suggests that including the pressure-induced compressibility is essential for the use of CEMA in DDT process. The results also show that the new formulation of CEMA projection can successively capture the detonation initiation through either a gradient mechanism or a direct initiation mechanism, and therefore can be used as an effective local analytical tool for the computational diagnostics of detonation initiation in a DDT process. It was found that detonation development is characterized by a strong contribution of chemistry role to the CEM which is pivotal to the initiation of detonation. The role of compressibility is found enhanced at the edge of the detonation front where diffusion was found to have minimal effects on detonation development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Astronomer's Proposal Tool

Astronomer's Proposal Tool (APT) is a computer program that assists astronomers in preparing their Phase 1 and Phase 2 Hubble Space Telescope science programs. APT is a successor to the Remote Proposal Submission System 2 (RPS2) program, which has been rendered obsolete by more recent advances in computer software and hardware. APT exploits advances associated with widespread use of the Internet, multiplatform visual development software tools, and overall increases in the power of desktop computer hardware, all in such a way as to make the preparation and submission of proposals more intuitive and make observatory operations less cumbersome. APT provides documentation and help that are friendly, up to date, and easily accessible to users of varying levels of expertise, while defining an extensible framework that is responsive to changes in both technology and observatory operations. APT consists of two major components: (1) a set of software tools that are intuitive, visual, and responsive and (2) an integrated software environment that unifies all the tools and makes them interoperable. The APT tools include the Visual Target Tuner, Proposal Editor, Exposure Planner, Bright Object Checker, and Visit Planner.

Krueger, Tony↗
NASA NTRS2000

Atomistic Method Applied to Computational Modeling of Surface Alloys

The formation of surface alloys is a growing research field that, in terms of the surface structure of multicomponent systems, defines the frontier both for experimental and theoretical techniques. Because of the impact that the formation of surface alloys has on surface properties, researchers need reliable methods to predict new surface alloys and to help interpret unknown structures. The structure of surface alloys and when, and even if, they form are largely unpredictable from the known properties of the participating elements. No unified theory or model to date can infer surface alloy structures from the constituents properties or their bulk alloy characteristics. In spite of these severe limitations, a growing catalogue of such systems has been developed during the last decade, and only recently are global theories being advanced to fully understand the phenomenon. None of the methods used in other areas of surface science can properly model even the already known cases. Aware of these limitations, the Computational Materials Group at the NASA Glenn Research Center at Lewis Field has developed a useful, computationally economical, and physically sound methodology to enable the systematic study of surface alloy formation in metals. This tool has been tested successfully on several known systems for which hard experimental evidence exists and has been used to predict ternary surface alloy formation (results to be published: Garces, J.E.; Bozzolo, G.; and Mosca, H.: Atomistic Modeling of Pd/Cu(100) Surface Alloy Formation. Surf. Sci., 2000 (in press); Mosca, H.; Garces J.E.; and Bozzolo, G.: Surface Ternary Alloys of (Cu,Au)/Ni(110). (Accepted for publication in Surf. Sci., 2000.); and Garces, J.E.; Bozzolo, G.; Mosca, H.; and Abel, P.: A New Approach for Atomistic Modeling of Pd/Cu(110) Surface Alloy Formation. (Submitted to Appl. Surf. Sci.)). Ternary alloy formation is a field yet to be fully explored experimentally. The computational tool, which is based on the BFS (Bozzolo, Ferrante, and Smith) method for the calculation of the energetics, consists of a small number of simple PCbased computer codes that deal with the different aspects of surface alloy formation. Two analysis modes are available within this package. The first mode provides an atom-by-atom description of real and virtual stages 1. during the process of surface alloying, based on the construction of catalogues of configurations where each configuration describes one possible atomic distribution. BFS analysis of this catalogue provides information on accessible states, possible ordering patterns, and details of island formation or film growth. More importantly, it provides insight into the evolution of the system. Software developed by the Computational Materials Group allows for the study of an arbitrary number of elements forming surface alloys, including an arbitrary number of surface atomic layers. The second mode involves large-scale temperature-dependent computer 2. simulations that use the BFS method for the energetics and provide information on the dynamic processes during surface alloying. These simulations require the implementation of Monte-Carlo-based codes with high efficiency within current workstation environments. This methodology capitalizes on the advantages of the BFS method: there are no restrictions on the number or type of elements or on the type of crystallographic structure considered. This removes any restrictions in the definition of the configuration catalogues used in the analytical calculations, thus allowing for the study of arbitrary ordering patterns, ultimately leading to the actual surface alloy structure. Moreover, the Monte Carlo numerical technique used for the large-scale simulations allows for a detailed visualization of the simulated process, the main advantage of this type of analysis being the ability to understand the underlying features that drive these processes. Because of the simplicity of the BFS method for e energetics used in these calculations, a detailed atom-by-atom analysis can be performed at any point in the simulation, providing necessary insight on the details of the process. The main objective of this research program is to develop a tool to guide experimenters in understanding and interpreting often unexpected results in alloy formation experiments. By reducing the computational effort without losing physical accuracy, we expect that powerful simulation tools will be developed in the immediate future, which will allow material scientists to easily visualize and analyze processes at a level not achievable experimentally.

Bozzolo, Guillermo H.↗
DOE OSTI2022

HL-LHC Computing Review Stage 2, Common Software Projects: Data Science Tools for Analysis

This paper was prepared by the HEP Software Foundation (HSF) PyHEP Working Group as input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which took place in November, 2021. It describes the adoption of Python and data science tools in HEP, discusses the likelihood of future scenarios, and recommendations for action by the HEP community.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗