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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Cybersecurity Vulnerability Assessment Methodologies for Nuclear Power Plants

As any mechanical system ages, broken or outdated parts must be replaced; nuclear power plants (NPPs) are no different. The average age of a NPP in the United States' aging fleet of commercial reactors is 35 years, with many of these plants operating under extensions of their original licenses. These plants, originally designed in the 1960's and 1970's, are controlled by analog instrumentation and control (I and C) systems, and have since been upgraded (to varying degrees) to hybrid analog-digital systems. As these NPPs are upgraded to continue operating and to meet more stringent safety requirements imposed by the Nuclear Regulatory Commission (NRC), the implementation of digital systems to monitor and run the power plant introduces new vulnerabilities that must be addressed. The main objectives of a vulnerability assessment (VA) are to identify, quantify, and prioritize all of the susceptibilities inherent in a system. This allows for all weaknesses to be analyzed, and for the most serious concerns to be addressed first. Traditional VA methodologies deal with the analysis of physical systems associated with the NPP; with the introduction of digital control systems and computer networks, these methodologies must be expanded to include these fundamentally different systems and vulnerability concerns. Digital systems have been used in NPPs since the 1980's, but only for non-safety control systems; attacks on these systems were not deemed consequential, and no dedicated VA methodology was required. However, 'all new NPPs are being designed with integrated digital instrumentation and control systems as the backbone of protection, controls, alarms, and display and monitoring'. This gives rise to new vulnerabilities such as disrupted data displays, the suppression of critical alarms, or unauthorized access to secure systems from connected networks. The NRC released formal cybersecurity regulations in March 2009 (10 CFR 73.54), as well as a guide for the program in January 2010 (RG 5.71). However, the regulations and guide provide limited insight on how to identify these vulnerabilities; the focus is on remediating them once found. Hence, there is a need for a comprehensive, systematic methodology to conduct a VA on digital computer and communications and network systems. In this paper, we review traditional VA methodologies and discuss relevant cases. Then we discuss challenges to vulnerabilities within the context of modernization of the fleet and what needs to be done to address this issue.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common “black box” type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

WTEC Panel Report on International Assessment of Research and Development in Simulation-Based Engineering and Science

This WTEC panel report assesses the international research and development activities in the field of Simulation- Based Engineering and Science (SBE&S). SBE&S involves the use of computer modeling and simulation to solve mathematical formulations of physical models of engineered and natural systems. SBE&S today has reached a level of predictive capability that it now firmly complements the traditional pillars of theory and experimentation/observation. As a result, computer simulation is more pervasive today – and having more impact – than at any other time in human history. Many critical technologies, including those to develop new energy sources and to shift the cost-benefit factors in healthcare, are on the horizon that cannot be understood, developed, or utilized without simulation. A panel of experts reviewed and assessed the state of the art in SBE&S as well as levels of activity overseas in the broad thematic areas of life sciences and medicine, materials, and energy and sustainability; and in the crosscutting issues of next generation hardware and algorithms; software development; engineering simulations; validation, verification, and uncertainty quantification; multiscale modeling and simulation; and SBE&S education. The panel hosted a U.S. baseline workshop, conducted a bibliometric analysis, consulted numerous experts and reports, and visited 59 institutions and companies throughout East Asia and Western Europe to explore the active research projects in those institutions, the computational infrastructure used for the projects, the funding schemes that enable the research, the collaborative interactions among universities, national laboratories, and corporate research centers, and workforce needs and development for SBE&S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations

In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

GPU applications for data processing

Modern experiments that use nuclear photoemulsion imply fast and efficient data acquisition from the emulsion can be performed. The new approaches in developing scanning systems require real-time processing of large amount of data. Methods that use Graphical Processing Unit (GPU) computing power for emulsion data processing are presented here. It is shown how the GPU-accelerated emulsion processing helped us to rise the scanning speed by factor of nine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Variations of ionization potential and electron affinity as a function of surface orientation: The case of orthorhombic SnS

We investigated the dependence of absolute SnS band-edge energies on surface orientation using density functional theory and GW method for all surfaces with Miller indices $-3 ≤ h,k,l ≤ 3$ and found variations as large as 0.9 eV as a function of ($hkl$). Variations of this magnitude may affect significantly the performance of photovoltaic devices based on polycrystalline SnS thin-films and, in particular, may contribute to the relatively low measured open circuit voltage of SnS solar cells. X-ray diffraction measurements confirm that our thermally evaporated SnS films exhibit a wide distribution of different grain orientations, and the results of Kelvin force microscopy support the theoretically predicted variations of the absolute band-edge energies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2014

High Performance Computing and Storage Requirements for Nuclear Physics: Target 2017

In April 2014, NERSC, ASCR, and the DOE Office of Nuclear Physics (NP) held a review to characterize high performance computing (HPC) and storage requirements for NP research through 2017. This review is the 12th in a series of reviews held by NERSC and Office of Science program offices that began in 2009. It is the second for NP, and the final in the second round of reviews that covered the six Office of Science program offices. This report is the result of that review

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Targeting Aspergillus allergen oryzin with a chemical probe at atomic precision

We report the molecular basis of Aspergillus fumigatus oryzin, allergen Asp f 13, or alkaline proteinase ALP1, containing the sequence motif His–Asp–Ser of the subtilisin family, structure, and function at atomic detail. Given the resolution of the data (1.06 Å), we use fragment molecular replacement with ideal polyalanine α-helices to determine the first crystal structure of oryzin. We probe the catalytic serine through formation of an irreversible bond to a small molecule compound, specifically labeling it, describing the amino acid residues performing the catalytic function. Defined by a self-processed pro-peptide, the active site architecture shapes up pocket-like subsites that bind to and unveil the S1′–S4′ substrate binding preferences. We use molecular modeling to dock a model of the pro-peptide in the S1–S4 region and to dock collagen along the active site cleft. Opposite to the face harboring the catalytic serine, the enzyme binds to a calcium ion in a binding site created by backbone flipping. We use thermal unfolding to show that this metal ion provides structural stability. With no known host inhibitor identified thus far, this structure may hasten the progress of developing new therapeutic agents for diseases caused by pathogenic fungi.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Progress in Fast, Accurate Multi-scale Climate Simulations

We present a survey of physical and computational techniques that have the potential to con- tribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allow more complete representations of climate features at the global scale. At the same time, part- nerships with computer science teams have focused on taking advantage of evolving computer architectures, such as many-core processors and GPUs, so that these approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

climate modeling↗
DOE OSTI2017

Liquid Sulfur Impregnation of Microporous Carbon Accelerated by Nanoscale Interfacial Effects

Impregnation of porous carbon matrices with liquid sulfur has been exploited to fabricate composite cathodes for lithium-sulfur batteries, aimed at confining soluble sulfur species near conducting carbon to prevent both loss of active material into the electrolyte and parasitic reactions at the lithium metal anode. Here, through extensive computer simulations, we uncover the strongly favorable interfacial free energy between liquid sulfur and graphitic surfaces that underlies this phenomenon. Previously unexplored curvature-dependent enhancements are shown to favor the filling of smaller pores first and effect a quasi-liquid sulfur phase in microporous domains (diameters <2 nm) that persists ~30° below the expected freezing point. Evidence of interfacial sulfur on carbon is shown to be a 0.3 eV red shift in the simulated and measured interfacial X-ray absorption spectra. Our results elucidate the critical morphology and thermodynamic properties necessary for future cathode design and highlight the importance of molecular-scale details in defining emergent properties of functional nanoscale interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Computational modeling of drug-resistant bacteria. Final report

Initial proposal summary: The evolution of antibiotic-resistant mutants among bacteria (superbugs) is a persistent and growing threat to public health. In many ways, we are engaged in a war with these microorganisms, where the corresponding arms race involves chemical weapons and biological targets. Just as advances in microelectronics, imaging technology and feature recognition software have turned conventional munitions into smart bombs, the long-term objectives of this proposal are to develop highly effective antibiotics using next-generation biomolecular modeling capabilities in tandem with novel subatomic feature detection software. Using model compounds and targets, our design methodology will be validated with correspondingly ultra-high resolution structure-determination methods at premier DOE facilities (single-crystal X-ray diffraction at Argonne National Laboratory, and neutron diffraction at Oak Ridge National Laboratory). The objectives and accomplishments are summarized.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

DL_POLY Quantum 2.0: A modular general-purpose software for advanced path integral simulations

DL_POLY Quantum 2.0, a vastly expanded software based on DL_POLY Classic 1.10, is a highly parallelized computational suite written in FORTRAN77 with a modular structure for incorporating nuclear quantum effects into large-scale/long-time molecular dynamics simulations. This is achieved by presenting users with a wide selection of state-of-the-art dynamics methods that utilize the isomorphism between a classical ring polymer and Feynman’s path integral formalism of quantum mechanics. Here, the flexible and user-friendly input/output handling system allows the control of methodology, integration schemes, and thermostatting. DL_POLY Quantum is equipped with a module specifically assigned for calculating correlation functions and printing out the values for sought-after quantities, such as dipole moments and center-of-mass velocities, with packaged tools for calculating infrared absorption spectra and diffusion coefficients.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Graph-theoretical KMC Framework for Calculating Effective Diffusivity in TPBAR Components: Effective Diffusivity of Tritium in α-Zr Grain Boundaries

We report the development of a computer simulation tool based on the Graph-theoretical kinetic Monte Carlo (GT-KMC) approach that can be used to simulate diffusion and derive effective diffusivity of species in complex structures of TPBAR components. The GT-KMC framework was implemented in AKSOME, an on-lattice self-learning kinetic Monte Carlo tool developed at PNNL and was previously used to study solute diffusion in metal alloys. This newly developed tool will be helpful in simulating atom diffusion and extracting their effective diffusivities in grain boundaries and interfaces in various TPBAR components. Once benchmarked, the tool will be used to simulate the diffusion of tritium (hydrogen) along grain boundaries in α-Zr using the activation energy barrier data previously obtained from Density Functional Theory calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Impact of cross-linking of polymers on transport of salt and water in polyelectrolyte membranes: A mesoscopic simulation study

Our recent atomistic simulation studies demonstrated that the transport properties of salt ions and water in non-crosslinked polymer electrolyte membrane exhibit an intriguing dependence on salt concentration that is opposite to that seen in electrolyte solutions. Here in this paper, we extend our study to probe the influence of the degree of cross-linking of the polymer on the transport properties of salt and water in polymer electrolyte membranes. Towards this objective, we use a coarse-grained model embedded within dissipative particle dynamics (DPD) mesoscale simulations, which allows us to access time scales necessary for studying crosslinked polymer systems. Our DPD simulations on non-crosslinked membranes reproduce results that are in qualitative agreement with our atomistic simulations. For the case of crosslinked membranes, our results demonstrate that the diffusion of salt ions and water is reduced significantly relative to crosslinked systems. However, the trends exhibited by the salt concentration dependence of diffusivities and the coordination of the cations with anions and with the polymer backbone remain qualitatively similar to those observed in non-crosslinked membranes.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop: August 4-5, 2015, Washington, D.C.

This report details the impact exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the DOE applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The workshop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing.

97 MATHEMATICS AND COMPUTING↗