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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Interrelationship between Lab, Space, Astrophysical, Magnetic Fusion, and Inertial Fusion Plasma Experiments (Graduate support for DIII-D Frontier Science Experiments)

The project, to explore the use of the DIII-D facility to investigate questions of value to discovery plasma science and provide a deepened scientific understanding behind basic plasma processes that are outside of the fusion-energy-science research line, began in September 2016 when five discovery-plasma physicists were contacted by General Atomics to form a Frontier Science Experiments (FSE) panel for brainstorming and for implementing the FSE campaign at the DIII-D National Fusion Facility. After advertising the FSE opportunity at the annual plasma physics conference, the panel grew to include six others. By January 2017, the panel for reviewing and ranking proposals was established. Evaluation of FY2017 proposals took place in February 2017. Evaluation of FY2018 proposals took place in January 2018. This proposal began 15 August 2017 and funded two research assistantships for three students who interacted, gained expertise in experimental and computational physics, and shared responsibilities on the following projects within the DIII-D (non-fusion) Frontier Science Experiments (FSE) campaign: (1) "Impact of Magnetic Perturbations on Turbulence - Zonal Flow Interactions and Saturation", (2) "Nonlinear Processes during Toroidal Alfvén Mode-Mode Interaction", (3) "Self-Consistent Chaos in Magnetic Field Dynamics", (4) “Nonmodal Decomposition of Turbulence”, and (5) “Multi-harmonic EMIC Emission -- Species mix and distribution function dependence of instabilities near the ion-cyclotron frequency”. The outcome was a fraction of one doctoral dissertation and the entirety of one master of science thesis. The work took advantage of the unique capabilities of the DIII-D National Fusion Facility to serve as the first phase of the Frontier Science Experiment (FSE) campaign, an innovative concept aiming to advance fundamental understanding in 3 key general plasma science areas - particle acceleration and energetic particles in plasmas, turbulence and transport in plasmas, and nonlinear waves and processes – all at discovery’s cutting edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1985

Introduction to the Space Physics Analysis Network (SPAN)

The Space Physics Analysis Network or SPAN is emerging as a viable method for solving an immediate communication problem for the space scientist. SPAN provides low-rate communication capability with co-investigators and colleagues, and access to space science data bases and computational facilities. The SPAN utilizes up-to-date hardware and software for computer-to-computer communications allowing binary file transfer and remote log-on capability to over 25 nationwide space science computer systems. SPAN is not discipline or mission dependent with participation from scientists in such fields as magnetospheric, ionospheric, planetary, and solar physics. Basic information on the network and its use are provided. It is anticipated that SPAN will grow rapidly over the next few years, not only from the standpoint of more network nodes, but as scientists become more proficient in the use of telescience, more capability will be needed to satisfy the demands.

Green, J. L.↗
DOE OSTI2015

R&D100: IC ID

Supply chain security to detect, deter, and prevent the counterfeiting of networked and stand-alone integrated circuits (ICs) is critical to cyber security. Sandia National Laboratory researchers have developed IC ID to leverage Physically Unclonable Functions (PUFs) and strong cryptographic authentication to create a unique fingerprint for each integrated circuit. IC ID assures the authenticity of ICs to prevent tampering or malicious substitution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Electric fields imbue enzyme reactivity by aligning active site fragment orbitals

It is broadly recognized that intramolecular electric fields, produced by the protein scaffold and acting on the active site, facilitate enzymatic catalysis. This field effect can be described by several theoretical models, each of which is intuitive to varying degrees. In this contribution, we show that a fundamental effect of electric fields is to generate electrostatic potentials that facilitate the energetic alignment of reactant frontier orbitals. We apply this model to demystify the impact of electric fields on high-valent iron–oxo heme proteins: catalases, peroxidases, and peroxygenases/monooxygenases. Specifically, we show that this model easily accounts for the observed field-induced changes to the spin distribution within peroxidase active sites and explains the transition between epoxidation and hydroxylation pathways seen in Cytochrome P450 active site models. Thus, for the intuitive interpretation of the chemical effect of the field, the strategy involves analyzing the response of the orbitals of active site fragments, and their energetic alignment. We note that the energy difference between fragment orbitals involved in charge redistribution acts as a measure for the chemical hardness/softness of the reactive complex. This measure, and its sensitivity to electric fields, offers a single parameter model from which to quantitatively assess the effects of electric fields on reactivity and selectivity. Thus, the model provides an additional perspective to describe electrostatic preorganization and offers ways for its manipulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Computers in neutron science

Abstract Neutron scattering provides a sensitive probe for materials structure and dynamics and has had a profound impact on research ranging from quantum topological materials to archaeology to aerospace alloys. These experiments are incredibly data-rich and often challenging to interpret. Computational studies have been integral in the growing success of neutron science. From data collection and reduction to analysis and interpretation, computers are used at every stage of modern neutron experiments. State-of-the-art software allows for a deeper understanding of these powerful unique experiments. This themed issue, guest-edited by Dr. Jeff Armstrong, Dr. Keith T Butler, and Dr. Matthew R Ryder, brings together a collection of research highlighting the state-of-the-art in the application of computers in neutron science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Mechanochemical spinodal decomposition: a phenomenological theory of phase transformations in multi-component, crystalline solids

Here, we present a phenomenological treatment of diffusion-driven martensitic phase transformations in multi-component crystalline solids that arise from non-convex free energies in mechanical and chemical variables. The treatment describes diffusional phase transformations that are accompanied by symmetry-breaking structural changes of the crystal unit cell and reveals the importance of a mechanochemical spinodal, defined as the region in strain-composition space, where the free-energy density function is non-convex. The approach is relevant to phase transformations wherein the structural order parameters can be expressed as linear combinations of strains relative to a high-symmetry reference crystal. The governing equations describing mechanochemical spinodal decomposition are variationally derived from a free-energy density function that accounts for interfacial energy via gradients of the rapidly varying strain and composition fields. A robust computational framework for treating the coupled, higher-order diffusion and nonlinear strain gradient elasticity problems is presented. Because the local strains in an inhomogeneous, transforming microstructure can be finite, the elasticity problem must account for geometric nonlinearity. An evaluation of available experimental phase diagrams and first-principles free energies suggests that mechanochemical spinodal decomposition should occur in metal hydrides such as ZrH 2-2c . The rich physics that ensues is explored in several numerical examples in two and three dimensions, and the relevance of the mechanism is discussed in the context of important electrode materials for Li-ion batteries and high-temperature ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Challenges in materials and devices for resistive-switching-based neuromorphic computing

This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxide materials, which exhibit unique properties that emulate key functionalities needed for this application. In Sec. I, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, and a neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Sec. II, we describe the main components of a possible solid-state implementation. In Sec. III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for neuromorphic computing. In Sec. IV, we describe why transition metal oxides are promising materials for future neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Sec. V, while existing implementations are described in Sec. VI. Section VII presents applications to practical problems. We list in Sec. VIII important basic research challenges and open issues. Here, we discuss issues related to specific implementations, novel materials, devices, and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling, and integration into a neuromorphic computer may bring us closer to the development of a machine that rivals the brain.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Final Technical Report for ARRA Funding

Final technical report of the University of Minnesota experimental high energy physics group for ARRA support. The Cryogenic Dark Matter Experiment (CDMS) used the funds received to construct a new passive shield to protect a high-purity germanium detector located in the Soudan mine in Northern Minnesota from cosmic rays. The BESIII and the CMS groups purchased computing hardware to assemble computer farms for data analysis and to generate large volumes of simulated data for comparison with the data collected.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

A geometric initial guess for localized electronic orbitals in modular biological systems

Recent first-principles molecular dynamics algorithms using localized electronic orbitals have achieved O(N) complexity and controlled accuracy in simulating systems with finite band gaps. However, accurately deter- mining the centers of these localized orbitals during simulation setup may require O(N 3 ) operations, which is computationally infeasible for many biological systems. We present an O(N) approach for approximating orbital centers in proteins, DNA, and RNA which uses non-localized solutions for a set of fixed-size subproblems to create a set of geometric maps applicable to larger systems. This scalable approach, used as an initial guess in the O(N) first-principles molecular dynamics code MGmol, facilitates first-principles simulations in biological systems of sizes which were previously impossible.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Failure Mechanisms in Fored, Layered, and Additive Manufactured Titanium due to Hypervelocity Impact: Experiments and Simulations

This article presents a study of dynamic failure events in forged, layered, and additive manufactured (AM) titanium alloy subject to hypervelocity impact. Experiments were conducted using a two stage light-gas gun, 0.22-caliber Lexan projectiles, and different types of titanium target plates. A four-channel Photonic Doppler Velocimetry (PDV) system was used to study the free surface velocities to provide an understanding of the different failure mechanisms in these materials. The experimental measurements were used to validate computational simulations, which describe the behavior of these materials under shock. It was determined that AM, forged titanium, and multi-layered stacks produce similar velocity profiles during the early stage of impact, with the AM targets exhibiting spall at lower velocities and the multi-layered stacks exhibiting vibrations between plates. Simulations of solid and layered forged materials provide a good match to experimental data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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↗