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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Optical production systems using neural networks and symbolic substitution

Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

Botha, Elizabeth↗
DOE OSTI2023

The INTERSECT Open Federated Architecture for the Laboratory of the Future

A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.

Engelmann, Christian↗
DOE OSTI2026

Source-Resolved Inversion of Elemental Carbon Emissions in California Using Log-Space Bayesian Inference

Elemental carbon (EC), operationally quantified by thermal-optical analysis, is widely used as a proxy for black carbon (BC) relevant to short-term climate forcing and public health. Current EC emission inventories remain highly uncertain, with persistent discrepancies between bottom-up and top-down estimates. In this study, we develop a source-resolved, log-space Bayesian inversion framework applied to estimate California’s statewide EC emissions in 2019. By integrating surface EC measurements from the EPA’s Air Quality System network with high-resolution source contributions simulated by a chemical transport model, we identify a one-third underestimation in the existing statewide EC inventory, requiring an increase of the total from a prior of 8.58 [5.49–13.75] Gg year–1 to a posterior estimate of 12.78 [10.71–15.37] Gg year–1. This discrepancy is primarily driven by substantial underestimations in the power and industrial and off-road mobile sectors. Furthermore, population-weighted exposure analysis reveals a marked sectoral divergence between emission mass and health burden: off-road mobile sources dominate both emissions and exposure, accounting for 31% of statewide exposure, while residential wood combustion contributes 26% of total exposure despite comprising only 19% of total emissions, due to its source proximity to population. These findings underscore the need to update sector-specific EC speciation profiles and demonstrate that mitigation strategies targeting off-road mobile sources and residential wood combustion are critical for reducing EC-related health impacts in California.

Zhang, Jie↗
DOE OSTI2023

Prompt Photofission Neutron Detection in Depleted Uranium

The detection of prompt photofission neutrons during active interrogation is a strong indication of the presence of special nuclear materials. However, the high-energy photons used for interrogation create a very challenging radiation environment for the detection of prompt fission signatures. These challenges include detector saturation and pulse pile-up. Additionally, there is an elevated neutron background that further challenges the detection of prompt fission neutrons. This background is produced because of (γ, Xn) photonuclear reactions in the surrounding high-Z materials. Here, we demonstrate the detection of prompt photofission neutrons in these challenging environments. Depleted uranium (DU) and lead targets are interrogated with bremsstrahlung photons produced by a 9-MV electron linear accelerator. Fast neutrons are detected with trans-stilbene organic detectors, and scintillation pulses are analyzed using a previously developed and demonstrated artificial neural network system. We observe a 5 times higher photoneutron count rate when the lead target is replaced with the DU target. Additionally, we observe a difference in the photoneutron light-output distributions of lead and DU. Here, this difference in the measured distributions is due to the difference in the photoneutron-energy spectra; DU photoneutrons are emitted with (γ, n) and watt-energy spectra, whereas lead photoneutrons are emitted with only the (γ, n) spectrum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

AI-based Cyber Event OSINT via Twitter Data

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.

Dale, Dakota↗
DOE OSTI2024

Science Use Case Design Patterns for Autonomous Experiments

Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This paper introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further offers an overview of the 12 defined patterns and 4 examples of patterns of 2 different pattern classes. It also provides insight into building solutions from these patterns. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.

Engelmann, Christian↗
DOE OSTI2024

FIREWHEEL

SAND2024-16910O FIREWHEEL is a tool for experiment orchestration that assists a user in building and controlling repeatable experiments of distributed network systems at any scale. This version also includes FIREWHEEL v2.7 and its associated companion model components. With FIREWHEEL, users can easily construct modular parts of a cyber system, called Model Components. The Model Components can be combined to form an experiment. Most experiments are then instantiated using an emulation tool, such as minimega.

Kajder, Thomas↗
DOE OSTI2024

Long-Term Infrasound Sensor Calibration and Characterization

Previous testing has shown that infrasound sensors deployed in the field can exhibit notable deviations from their nominal, lab-based calibrations. These variations may be due to changes in environmental conditions, long-term sensor drift, or other unresolved features. In early 2018, we installed two identical infrasound elements with five infrasound sensors at each element (Chaparral M50A, Chaparral M64LN, CEA/Martec MB2005, CEA/Seismowave MB3a, and Hyperion IFS-5113A). These sensors were accepted or under consideration for use in the International Monitoring System network of the Comprehensive Nuclear-Test-Ban Treaty. Each element had all sensors connected to a single digitizer and port to the atmosphere. We also recorded instrument enclosure air temperature and humidity and external air temperature. Using the MB2005 as the reference, we examine the relative sensor response (both magnitude and phase) as a function of time and frequency and compare it with quarterly laboratory calibrations and environmental conditions. Here, we find that the magnitude response for all sensors exhibits some variability in both the lab and field, with the amplitude variations often >5%. The field-based variations are more severe and occur on both long-term (months) and short-term (hours) timescales. Short-term variability correlates with changes in environmental conditions and is considerable (up to 25%) for the Chaparral M50A and noticeable (∼5%) for the French Alternative Energies and Atomic Energy Commission (CEA) MB3a. Long-term magnitude variability for the Chaparral M50A was up to 50% and does not clearly correlate with environmental conditions. The other sensors show some long-term magnitude offsets, but they have relatively stable responses in the conditions we examined. The MB3a also displays some frequency-dependent magnitude variability and shows a minor dependence on temperature. Phase estimates are stable and near zero for all sensors tested. These results strongly suggest sensor response and variability due to environmental conditions should be considered in future infrasound data interpretation and sensor selection and development.

Fee, David↗
DOE OSTI2022

Potential for Evaporation and In Situ Reaction of Organic Compounds in Hanford Supplemental LAW

The presence of regulated organic species in Hanford nuclear tank waste is assessed in this report, along with fate of the organics and possible evaporative treatment. A narrowed list of regulated organic species of concern is developed based upon published analyses, chemical properties like aqueous solubility, and chemical reactivity under alkaline conditions. Published analyses include tank headspace and liquid samples, review of chemical reagents formerly used at Hanford, and the Tank Waste Information Network System (TWINS). This work supports possible stabilization of Hanford Supplemental Low Activity Waste (SLAW) by grouting.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Potential for Evaporation and In Situ Reaction of Organic Compounds in Hanford Supplemental LAW

The presence of regulated organic species in Hanford nuclear tank waste is assessed in this report, along with fate of the organics and possible evaporative treatment. A narrowed list of regulated organic species of concern is developed based upon published analyses, chemical properties like aqueous solubility, and chemical reactivity under alkaline conditions. Published analyses include tank headspace and liquid samples, review of chemical reagents formerly used at Hanford, and the Tank Waste Information Network System (TWINS). This work supports possible stabilization of Hanford Supplemental Low Activity Waste (SLAW) by grouting.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

INTERSECT Architecture Specification: Use Case Design Patterns (V.0.9)

Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This document introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further details the 12 defined patterns and provides insight into building solutions from these patterns. The document also describes the application of these patterns in the context of several INTERSECT autonomous laboratories. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

ACOPF↗