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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 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↗
DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

DUNE: 100 Billion Reasons

20-slide Ignite Off! Challenge: I am presenting a 5-minute overview of the DUNE project along with my work in data validation of the TMS. I begin with some unique facts regarding neutrinos, then I move into a brief explanation of what DUNE is, and finally close off with a mention of my contributions to the experiment.

Vershaw, Andre [Unlisted, US, IL] (ORCID:000900084↗
DOE OSTI2022

The (Un)reasonable Effectiveness of Neural Network in Cherenkov Calorimetry

We report a greater than factor of two improvement in the hadronic energy resolution of a simulated Cherenkov calorimeter by estimating the energy with machine learning over traditional techniques. The prompt signal formation and energy threshold properties of Cherenkov radiation provide identifiable features that machine learning techniques can exploit to produce a superior model for energy reconstruction. We simulated a quartz-fiber calorimeter via the GEANT4 framework to study the reconstruction techniques in single events. We compared the machine learning-based reconstruction performance to the traditional simple sum of signal and dual-readout techniques that use both Cherenkov and scintillation signals. We describe why this game-changing approach to Cherenkov hadron calorimetry excels and our plans for a dedicated beam test to validate these findings with a fast, radiation-hard hadron calorimeter prototype.

Akchurin, Nural↗