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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The fall, recovery, classification, and initial characterization of the Hamburg, Michigan H4 chondrite

Abstract The Hamburg meteorite fell on January 16, 2018, near Hamburg, Michigan, after a fireball event widely observed in the U.S. Midwest and in Ontario, Canada. Several fragments fell onto frozen surfaces of lakes and, thanks to weather radar data, were recovered days after the fall. The studied rock fragments show no or little signs of terrestrial weathering. Here, we present the initial results from an international consortium study to describe the fall, characterize the meteorite, and probe the collision history of Hamburg. About 1 kg of recovered meteorites was initially reported. Petrology, mineral chemistry, trace element and organic chemistry, and O and Cr isotopic compositions are characteristic of H4 chondrites. Cosmic ray exposure ages based on cosmogenic 3 He, 21 Ne, and 38 Ar are ~12 Ma, and roughly agree with each other. Noble gas data as well as the cosmogenic 10 Be concentration point to a small 40–60 cm diameter meteoroid. An 40 Ar‐ 39 Ar age of 4532 ± 24 Ma indicates no major impact event occurring later in its evolutionary history, consistent with data of other H4 chondrites. Microanalyses of phosphates with LA‐ICPMS give an average Pb‐Pb age of 4549 ± 36 Ma. This is in good agreement with the average SIMS Pb‐Pb phosphate age of 4535.3 ± 9.5 Ma and U‐Pb Concordia age of 4535 ± 10 Ma. The weighted average age of 4541.6 ± 9.5 Ma reflects the metamorphic phosphate crystallization age after parent body formation in the early solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2022

Exploring the universality of hadronic jet classification

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Compound Data Poisoning Technique with Significant Adversarial Effects on Transformer-based Sentiment Classification Tasks

Transformer-based models have demonstrated much success in various natural language processing tasks. However, they are often vulnerable to adversarial attacks, such as data poisoning, which can intentionally fool the model into generating incorrect results. In this article, we present a novel, compound variant of a data poisoning attack on a transformer-based model that maximizes the poisoning effect while minimizing the scope of poisoning. Here we do so by combining the established data poisoning technique (label flipping) with a novel adversarial artifact selection and insertion technique aimed at minimizing detectability and the scope of the poisoning footprint. We find that by using a combination of these two techniques, we achieve a state-of-the-art attack success rate of approximately 90% while poisoning only 0.5% of the original training set, thus minimizing the scope and detectability of the poisoning action. These findings have the potential to advance the development of better data poisoning detection methods.

97 MATHEMATICS AND COMPUTING↗