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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

On the reason for the kink in the rigidity spectra of cosmic-ray protons and helium nuclei near 230 GV

A three-component phenomenological model describing the specific features of the spectrum of cosmic-ray protons and helium nuclei in the rigidity range of 30–2×10{sup 5} GV is proposed. The first component corresponds to the constant background; the second, to the variable “soft” (30–500 GV) heliospheric source; and the third, to the variable “hard” (0.5–200 TV) source located inside a local bubble. The existence and variability of both sources are provided by the corresponding “surfatron accelerators,” whose operation requires the presence of an extended region with an almost uniform (in both magnitude and direction) magnetic field, orthogonally (or obliquely) to which electromagnetic waves propagate. The maximum energy to which cosmic rays can be accelerated is determined by the source size. The soft source with a size of ∼100 AU is located at the periphery of the heliosphere, behind the front of the solar wind shock wave. The hard source with a size of >0.1 pc is located near the boundary of an interstellar cloud at a distance of ∼0.01 pc from the Sun. The presence of a kink in the rigidity spectra of p and He near 230 GV is related to the variability of the physical conditions in the acceleration region and depends on the relation between the amplitudes and power-law exponents in the dependences of the background, soft heliospheric source, and hard near galactic source. The ultrarelativistic acceleration of p and He by an electromagnetic wave propagating in space plasma across the external magnetic field is numerically analyzed. Conditions for particle trapping by the wave and the dynamics of the particle velocity and momentum components are considered. The calculations show that, in contrast to electrons and positrons (e{sup +}), the trapped protons relatively rapidly escape from the effective potential well and cease to accelerate. Due to this effect, the p and He spectra are softer than that of e{sup +}. The possibility that the spectra of accelerated protons deviate from standard power-law dependences due to the surfatron mechanism is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation

We present FPDetect, a low-overhead approach for detecting logical errors and soft errors affecting stencil computations without generating false positives. We develop an offline analysis that tightly estimates the number of floating-point bits preserved across stencil applications. This estimate rigorously bounds the values expected in the data space of the computation. Violations of this bound can be attributed with certainty to errors. FPDetect helps synthesize error detectors customized for user-specified levels of accuracy and coverage. FPDetect also enables overhead reduction techniques based on deploying these detectors coarsely in space and time. Experimental evaluations demonstrate the practicality of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis

The Software Defined Architectures (SODA) Synthesizer is an open-source compiler-based tool able to automatically generate domain-specialized systems targeting Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) starting from high-level programming. SODA is composed of a frontend, SODA-OPT, which leverages the multilevel intermediate representation (MLIR) framework to interface with productive programming tools (e.g., machine learning frame-works), identify kernels suitable for acceleration, and perform high-level optimizations, and of a state-of-the-art high-level synthesis backend, Bambu from the PandA framework, to generate custom accelerators. One specific application of the SODA Synthesizer is the generation of accelerators to enable ultra-low latency inference and control on autonomous systems for scientific discovery (e.g., electron microscopes, sensors in particle accelerators, etc.). This paper provides an overview of the flow in the context of the generation of accelerators for edge processing to be integrated in transmission electron microscopy (TEM) devices, focusing on use cases from precision material synthesis. We show the tool in action with an example of design space exploration for inference on reconfigurable devices with a conventional deep neural network model (LeNet). Finally, we discuss the research directions and opportunities enabled by SODA in the area of autonomous control for scientific experimental workflows.

Castellana, Vito G.↗
DOE OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2025

AR4IR (Automated Reasoning for Incident Response) [SWR-24-103]

A basic formal methods tool with the ability to aid and/or automate a utilities’ incidence response and instills confidence that the proposed action satisfies the system’s physical constraints, the organization’s cyber policies, and will not cause violations of technical standards.

Etigowni, Sriharsha [National Renewable Energy Lab↗
DOE OSTI2025

A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems

Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.

Najera-Flores, David [ATA Engineering, Inc.]↗
DOE OSTI2015

Pattern ANalytics To Support High-performance Exploitation and Reasoning (PANTHER)

Sandia researchers are developing analytic techniques that efficiently detect patterns in high-volume, noisy, remote sensing and other geospatial data, improving an analysts’ ability to detect possibly off-normal events in complex homeland security, nuclear weapons, and emerging threat environments. High-consequence, national security decisions rely on timely, comprehensive answers to complex questions, yet critical gaps in our approach to these questions remain. PANTHER addresses these gaps through fundamental research on: geospatial-temporal feature extraction via image segmentation and classification; geospatial-temporal graph algorithms and computational geometry; and domain-relevant models of human perception and cognition informing the design of analytic systems. PANTHER’s cross-disciplinary R&D approach uses fundamental science and mathematics to understand how to maximize decision systems through rethinking key aspects of geospatial data analysis. New methods in motion and trajectory analysis support both rapid search for known trajectories as well as rapid clustering and anomaly detection. Thus, the perceptual and visual load on users is reduced by providing pattern analytics with elegant visual cues to spatial and temporal characteristics.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

Reasons for 2011 Release of the Evaluated Nuclear Data Library (ENDL2011.0)

LLNL's Computational Nuclear Physics Group and Nuclear Theory and Modeling Group have collaborated to create the 2011 release of the Evaluated Nuclear Data Library (ENDL2011). ENDL2011 is designed to sup- port LLNL's current and future nuclear data needs. This database is currently the most complete nuclear database for Monte Carlo and deterministic transport of neutrons and charged particles, surpassing ENDL2009.0 [1]. The ENDL2011 release [2] contains 918 transport-ready eval- uations in the neutron sub-library alone. ENDL2011 was assembled with strong support from the ASC program, leveraged with support from NNSA science campaigns and the DOE/Offce of Science US Nuclear Data Pro- gram.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Possible reason for the numerical value of the fine-structure constant

A simple picture of the electron emerges if there is a cross section of πλ 2 for vacuum virtual photons to stimulate an isolated electron to emit an additional virtual photon. This reaction mechanism leads to the storage of the rest-mass energy in the surrounding cloud of virtual photons. The Lamb shift can be obtained if one out of every 1/α of the stimulated emissions is preceded by the absorption and re-emission of the incident vacuum virtual photon including the corresponding electron recoil. This suggests a “doorway” cross section of απλ 2 plays a key role in some QED processes. Additional support for this suggestion is obtained via a simple semi-classical picture of Compton scattering. Several speculative semi-classical reaction mechanism choices, including near-field effects, give the anomalous magnetic moment of the free electron. These choices are partially justified by their ability to give the magnetic moment of electrons in hydrogen-like atoms as a function of the atomic number of the corresponding nucleus, close to the correct result. The stimulated-emission process used to obtain the rest-mass energy allows for the possibility that stimulated virtual photons can be exchanged between an electron pair. This exchange can be used to estimate a repulsive force between electrons, and thus allows for a calculation of the fine-structure constant. A result near to the experimental value is obtained with minor modifications to the assumptions that give the rest mass, Lamb shift, and anomalous magnetic moment of the electron. Additional studies are required to confirm or negate the suggestions made here.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Exascale and Beyond: Configuring, Reasoning, Scaling. Report of the 2011 Workshop on Architectures II: Exascale and Beyond, held August 8-10, Albuquerque, N.M.

The push toward exascale computing (1) and beyond is torn between contradictory and possibly incompatible goals. On the one hand, one wants a system that will be available within a short span of time, on a relatively small development budget, and with the least changes possible to the current software stack. On the other hand, one wants a system that is orders of magnitude more efficient in its energy consumption. An exascale system could be as different from current systems as scalable parallel systems were from vector machines at the time of the last major transition in the field, but the amount of application software developed at DOE in the last decades makes it undesirable to take a “clean sheet of paper” approach, if at all possible. Furthermore, one wishes to explore and develop distinct exascale architectures to reduce risk while maintaining some unity so that application codes easily port among them. Finally, the design of an exascale system may involve a large number of vendors, some of which have limited experience in the field of High-Performance Computing (HPC). A proper integration of these multiple contributions requires a proper definition of many new interfaces, as well as consistent design principles for the handling of cross-cutting issues such as resilience, performance, and power management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Reasons to read this book (hint: it’ll help your weapons research) Q and A with author highlights Lab’s collections, importance of early weapons work

Michael Bernardin’s retirement actually looks a little more like a second career. This one, though, as an author of books. Bernardin retired last year after 35 years as a nuclear physicist that culminated with his role as the Associate Lab Director for Weapons Physics. Since then, he’s continued accessing the collections of the National Security Research Center, which is the Lab’s classified library.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

The Role of State Utility Regulators in a Just and Reasonable Energy Transition: Examining Regulatory Approaches to the Economic Impacts of Coal Retirements

The ongoing trend of coal retirements has disproportionately affected the socioeconomic health of communities where power plants are located. For many public utility commissions, these impacts have led to important questions around the scope of PUCs’ statutory authority to analyze, consider and mitigate losses to host communities within their role as utility regulators. Due largely to variations in state legislative charges, commissions have taken different approaches to articulating what lies within the public interest.This report examines the authority of PUCs across the country to consider non-energy economic impacts beyond direct ratepayer effects, and summarizes approaches that PUCs, utilities and other stakeholders have used to mitigate the economic fallout of coal retirements, specifically.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Structures via Reasoning - Applying AI to Cryo Electron Microscopy to Reveal Structural Variability

There have been breakthroughs of latest cryo electron microscopy (cryo-EM) data analysis algorithms to classify cryo-EM image data. However, most of these cryo-EM reconstruction methods have focused on classifying distinctly different biomolecule structures. Here, we present our approaches of deep learning to differentiate homologous structures that are distinguishable only with inner morphological differences. We succeeded supervised classification of these subtly different homologues. However, we could not differentiate them with unsupervised methods. Here we discuss what further approaches are likely needed for successful unsupervised classification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Assurance of Reasoning Enabled Systems (ARES)

ARES was in part motivated by the determination of President’s Council of Advisors on Science and Technology (PCAST) on May 13th, 2023 that published a set of inquiries: In an era in which convincing images, audio, and text can be generated with ease on a massive scale, how can we ensure reliable access to verifiable, trustworthy information? How can we be certain that a particular piece of media is genuinely from the claimed source? What technologies, policies, and infrastructure can be developed to detect and counter AI-generated disinformation? In an effort to automatically analyze and patch/optimize code the work in this report describes various neural Machine Learning (ML) analysis engine implementations to assist in situations where source code is deficient or completely lacking to decompile (lift) binary code to ’C’. The goal is to gradually reduce human intervention. To this end, two Large Language Model (LLM) variants (Code LLama 2, LLama 3.1 and Starcoder1, Starcoder 2) where finetuned with ’before/after’ code pairs on the OpenBLAS library. LLama trained on the lowering process, Starcoder trained on the lifting process with National Security Agency’s (NSA) open-source Ghidra decompiler assist. The inferencing test results indicate correctness for only very short sequences for Starcoder 2. Moving forward, the experiments conclude with a set of recommendations of required resources and technologies

97 MATHEMATICS AND COMPUTING↗