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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗
NASA NTRS1992

Simulated families: A test for different methods of family identification

A set of families generated in fictitious impact events (leading to a wide range of 'structure' in the orbital element space have been superimposed to various backgrounds of different densities in order to investigate the efficiency and the limitations of the methods used by Zappala et al. (1990) and by Bendjoya et al. (1990) for identifying asteroid families. In addition, an evaluation of the expected interlopers at different significance levels and the possibility of improving the definition of the level of maximum significant of a given family were analyzed.

Bendjoya, Philippe↗
NASA NTRS2010

Selection Effects in Identifying Magnetic Clouds and the Importance of the Closest Approach Parameter

This study is motivated by the unusually low number of magnetic clouds (MCs) that are strictly identified within interplanetary coronal mass ejections (ICMEs), as observed at 1 AU; this is usually estimated to be around 30% or lower. But a looser definition of MCs may significantly increase this percentage. Another motivation is the unexpected shape of the occurrence distribution of the observers' "closest approach distances" (measured from a MC's axis, and called CA) which drops off somewhat rapidly as |CA| (in % of MC radius) approaches 100%, based on earlier studies. We suggest, for various geometrical and physical reasons, that the |CA|-distribution should be somewhere between a uniform one and the one actually observed, and therefore the 30% estimate should be higher. So we ask, When there is a failure to identify a MC within an ICME, is it occasionally due to a large |CA| passage, making MC identification more difficult, i.e., is it due to an event selection effect? In attempting to answer this question we examine WIND data to obtain an accurate distribution of the number of MCs vs. |CA| distance, whether the event is ICME-related or not, where initially a large number of cases (N=98) are considered. This gives a frequence distribution that is far from uniform, confirming earlier studies. This along with the fact that there are many ICME identification-parameters that do not depend on |CA| suggest that, indeed an MC event selection effect may explain at least part of the low ratio of (No. MCs)/(No. ICMEs). We also show that there is an acceptable geometrical and physical consistency in the relationships for both average "normalized" magnetic field intensity change and field direction change vs. |CA| within a MC, suggesting that our estimates of |CA|, B(sub 0) (magnetic field intensity on the axis), and choice of a proper "cloud coordinate" system (all needed in the analysis) are acceptably accurate. Therefore the MC fitting model (Lepping et al., 1990) is adequate, on average, for our analysis. However, this selection effect is not likely to completely answer our original question, on the unexpected ratio of MCs to ICMEs, so we must look for other factors, such as peculiarities of CME birth conditions. As a by-product of this analysis, we determine that the first order structural effects within a MC due to its interaction with the solar wind, plus the MC's usual expansion at 1 AU (i.e., the non-force free components of the MC's field) are, on average, weakly dependent on radial distance from the MC's axis; that is, in the outer reaches of a typical MC the non-force free effects show up, but even there they are rather weak. Finally, we show that it is not likely that a MC's size distribution statistically controls the occurrence distribution of the estimated |CA|s.

Lepping, R. P.↗
DOE OSTI2025

Selection Algorithm for electron neutrino charged current interactions in SBND

The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.

Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗
DOE OSTI2024

Photocathode characterisation for robust PICOSEC Micromegas precise-timing detectors

The PICOSEC Micromegas detector is a precise-timing gaseous detector based on a Cherenkov radiator coupled with a semi-transparent photocathode and a Micromegas amplifying structure, targeting a time resolution of tens of picoseconds for minimum ionising particles. Initial single-pad prototypes have demonstrated a time resolution below σ = 25 ps, prompting ongoing developments to adapt the concept for High Energy Physics applications, where sub-nanosecond precision is essential for event separation, improved track reconstruction and particle identification. The achieved performance is being transferred to robust multi-channel detector modules suitable for large-area detection systems requiring excellent timing precision. To enhance the robustness and stability of the PICOSEC Micromegas detector, research on robust carbon-based photocathodes, including Diamond-Like Carbon (DLC) and Boron Carbide (B 4 C), is pursued. Results from prototypes equipped with DLC and B 4 C photocathodes exhibited a time resolution of σ ≈ 32 ps and σ ≈ 34.5 ps, respectively. Efforts dedicated to improve detector robustness and stability enhance the feasibility of the PICOSEC Micromegas concept for large experiments, ensuring sustained performance while maintaining excellent timing precision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Single-shot ionization-based monitor for pulsed electron beams

In this work, we present an experimental demonstration of a single-shot, nondestructive electron beam diagnostic based on the ionization of a low-density pulsed gas jet. In our study, 7 MeV electron bunches from a radio-frequency photoinjector, carrying up to 100 pC of charge, traversed a localized distribution of nitrogen gas (N 2 ). The interaction of the electron bunches with the N 2 gas generated a correlated signature in the ionized particle distribution, which was spatially magnified using a series of electrostatic lenses and recorded with a microchannel-plate detector. Various modalities, including point-to-point imaging and velocity mapping, are investigated. A temporal trace of the detector current enabled the identification of single- and double-ionization events. The characteristics of the ionization distribution, dependence on gas density, total bunch charge, and other parameters, are described. Approaches to scaling to higher electron bunch density and energy are suggested. Additionally, the instrument proves useful for comprehensive studies of the ionization process itself.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Performance of Muon Neutrino Reconstruction at ICARUS using SPINE Machine Learning.

ICARUS is a liquid argon time projection chamber (LArTPC) neutrino experiment at Fermilab. Located ~600 m from the Booster Neutrino Beam (BNB) target, it serves as the far detector in the Short-Baseline Neutrino (SBN) Program. The primary objective of the SBN Program is to probe neutrino oscillation physics at short baselines using multiple detectors, including measurements of both muon neutrino disappearance and electron neutrino appearance. A key requirement for the neutrino oscillation measurements is robust event reconstruction, including accurate and precise particle identification (PID) and kinematic reconstruction across a wide range of topologies. To achieve the required performance, we employ the SPINE (“Scalable Particle Imaging with Neural Embeddings”) package, a state-of-the-art machine learning reconstruction framework. This talk presents the status of muon neutrino reconstruction using SPINE at ICARUS, including the evaluation of reconstruction performance that will enable the full physics program at ICARUS and at the SBN Program.

Totani, Dante [Colorado State U.] (ORCID:000000019↗
NASA NTRS1979

A new approach to data management and its impact on frequency control requirements

A new approach to data management consisting of spacecraft and data/information autonomy and its impact on frequency control requirements is presented. An autonomous spacecraft is capable of functioning without external intervention for up to 72 hr by enabling the sensors to make observations, maintaining its health and safety, and by using logical safety modes when anomalies occur. Data/information are made autonomous by associating all relevant ancillary data such as time, position, attitude, and sensor identification with the data/information record of an event onboard the spacecraft. This record is so constructed that the record of the event can be physically identified in a complete and self-contained record that is independent of all other data. All data within a packet will be time tagged to the needed accuracy, and the time markings from packet to packet will be coherent to a UTC time scale.

Blanchard, D. L.↗
NASA NTRS1980

Magnetic field directional discontinuities. 2: Characteristics between 0.46 and 1.0 AU

The characteristics of directional discontinuities (DD's) in the interplanetary magnetic field are studied using data from the Mariner 10 primary mission between 1.0 and 0.46 AU. Statistical and visual survey methods for DD identification resulted in a total of 644 events. Two methods were used to estimate the ratio of the number of tangential discontinuities (TD's) to the number of rotational discontinuities (RD's). Both methods show that the ratio of TD's to RD's varied with time and decreased with decreasing radial distance. A decrease in average discontinuity thickness of approx. 40 percent was found between 1.0 and 0.72 AU and approx. 54 percent between 1.0 and 0.46 AU, independent of type (TD or RD). This decrease in thickness for decreasing r is in qualitative agreement with Pioneer 10 observations between 1 and 5 AU. When the individual DD thickness are normalized with respect to the estimated local proton gyroradius (RA sub L), the average thickness at the three locations is nearly constant, 43 + or - 6 R sub L. This also holds true for both RD's and TD's separately. Statistical distributions of other properties, such as normal components and discontinuity plane angles, are presented.

Lepping, R. P.↗
NASA NTRS2016

Improvements to Lunar BRDF-Corrected Nighttime Satellite Imagery: Uses and Applications

Observations made by the VIIRS day/night band (DNB) provide daily, nighttime measurements to monitor Earth surface processes.However, these observations are impacted by variations in reflected solar radiation on the moon's surface. As the moon transitions from new to full phase, increasing radiance is reflected to the Earth's surface and contributes additional reflected moonlight from clouds and land surface, in addition to emissions from other light sources observed by the DNB. The introduction of a bi-directional reflectance distribution function (BRDF) algorithm serves to remove these lunar variations and normalize observed radiances. Provided by the Terrestrial Information Systems Laboratory at Goddard Space Flight Center, a 1 km gridded lunar BRDF-corrected DNB product and VIIRS cloud mask can be used for a multitude of nighttime applications without influence from the moon. Such applications include the detection of power outages following severe weather events using pre-and post-event DNB imagery, as well as the identification of boat features to curtail illegal fishing practices. This presentation will provide context on the importance of the lunar BRDF correction algorithm and explore the aforementioned uses of this improved DNB product for applied science applications.

NWS↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Applications of Anomaly Detection and Precursor Identification in Airspace Operations

As we continue to advance the U.S. National Airspace into the next generation of air traffic, we face challenges in both increase in complexity, as well as, a significant growth in traffic volume. Addressing these challenges, while maintaining the same level of safety is an important application of data mining. Because of these significant shifts in airspace design and usage there is a need to identify current and emergent safety risks along with their potential precursors. In recent years NASA has made advancements in developing scalable methods to address this effort in the Big Data paradigm. Multiple kernel anomaly detection approaches have been employed on both surveillance radar data and flight operational quality assurance data to identify operationally significant safety risks. Additionally, events have been explored with a recently developed precursor identification tool to discover states that reveal an increased probability of a safety event. These tools can be used to discover emerging safety risks that may not be currently monitored, which allows for mitigation tactics to be employed and ultimately make the overall airspace safer. This talk will discuss an overview of these methods and a discussion of the findings.

anomaly detection↗
DOE OSTI2025

Monitoring Methods for Early Detection of Inadvertent Fission Product Release at the Advanced Test Reactor

Isotope effluent data obtained during three instances of experiment failures at the Advanced Test Reactor (ATR) are analyzed to provide an overview of the methods used to detect initial signs of unintended fission product release. The data is contextualized with the operational experience, including means of identification and subsequent mitigation strategies, gained during these events. General trends as well as variations in isotopic behavior between the three failures are explored. Background on the Real Time Monitor, a High Purity Germanium detector, and other fission product monitoring systems utilized at the Advanced Test Reactor is also provided. The presented analysis was used to establish administrative action levels which are currently utilized by ATR for early detection of experiment fission product release. Early identification provides time to make programmatic decisions before approaching safety and environmental limits.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Dual particle imaging using time-of-flight neutron classification

Fast-neutron imaging technology is well-suited for passive nuclear material monitoring, secondary inspection of flagged cargo, and wide-area search for lost neutron sources. However, imaging systems that use pulse shape discrimination for event classification require complex pulse waveform analysis. In this work, we evaluate time-of-flight (TOF) based particle classification as an alternative solution for fast-neutron imaging by classifying all events with a TOF above a maximum threshold as neutrons. We measured a Cf-252 source next to Cs-137 using a 12-bar organic-glass scintillator array. By varying the TOF thresholds for neutron identification, we demonstrate a clear trade-off between event yield and backprojection image fidelity, with stricter thresholds improving precision at the cost of statistics, TOF thresholded data generated an image that predicted the neutron source direction with 20% reduced mean central angle prediction error compared to a traditional pulse shape discrimination (PSD) method with comparable event count. Time-of-flight particle classification shows promise as an alternative to pulse shape discrimination systems for fast neutron imaging systems looking to minimize costs and size of electronics with comparable imaging quality. The sources used demonstrate that the method is effective in classifying measured neutrons in a measurement environment with 150 μCi Cs-137 and 1.6 × 10 6 n/s Cf-252 sources positioned at distances of 66 cm and 81 cm from the detector. Additionally, the method classifies low-energy neutron events that pulse shape discrimination removes, so a combination of both methods would result in a higher overall neutron event efficiency.

Heriot, William [Univ. of Michigan, Ann Arbor, MI ↗
NASA NTRS2014

Microbiology and Crew Medical Events on the International Space Station

The closed environment of the International Space Station (ISS) creates an ideal environment for microbial growth. Previous studies have identified the ubiquitous nature of microorganisms throughout the space station environment. To ensure safety of the crew, microbial monitoring of air and surface within ISS began in December 2000 and continues to be monitored on a quarterly basis. Water monitoring began in 2009 when the potable water dispenser was installed on ISS. However, it is unknown if high microbial counts are associated with inflight medical events. The microbial counts are determined for the air, surface, and water samples collected during flight operations and samples are returned to the Microbiology laboratory at the Johnson Space Center for identification. Instances of microbial counts above the established microbial limit requirements were noted and compared inflight medical events (any non-injury event such as illness, rashes, etc.) that were reported during the same calendar-quarter. Data were analyzed using repeated measures logistic regression for the forty-one US astronauts flew on ISS between 2000 and 2012. In that time frame, instances of microbial counts being above established limits were found for 10 times for air samples, 22 times for surface samples and twice for water. Seventy-eight inflight medical events were reported among the astronauts. A three times greater risk of a medical event was found when microbial samples were found to be high (OR = 3.01; p =.007). Engineering controls, crew training, and strict microbial limits have been established to mitigate the crew medical events and environmental risks. Due to the timing issues of sampling and the samples return to earth, identification of particular microorganisms causing a particular inflight medical event is difficult. Further analyses are underway.

Oubre, Cherie↗
NASA NTRS2014

Microbiology and Crew Medical Events on the International Space Station

The closed environment of the International Space Station (ISS) creates an ideal environment for microbial growth. Previous studies have identified the ubiquitous nature of microorganisms throughout the space station environment. To ensure safety of the crew, microbial monitoring of air and surface within ISS began in December 2000 and continues to be monitored on a quarterly basis. Water monitoring began in 2009 when the potable water dispenser was installed on ISS. However, it is unknown if high microbial counts are associated with inflight medical events. The microbial counts are determined for the air, surface, and water samples collected during flight operations and samples are returned to the Microbiology laboratory at the Johnson Space Center for identification. Instances of microbial counts above the established microbial limit requirements were noted and compared inflight medical events (any non-injury event such as illness, rashes, etc.) that were reported during the same calendar-quarter. Data were analyzed using repeated measures logistic regression for the forty-one US astronauts flew on ISS between 2000 and 2012. In that time frame, instances of microbial counts being above established limits were found for 10 times for air samples, 22 times for surface samples and twice for water. Seventy-eight inflight medical events were reported among the astronauts. A three times greater risk of a medical event was found when microbial samples were found to be high (OR = 3.01; p =.007). Engineering controls, crew training, and strict microbial limits have been established to mitigate the crew medical events and environmental risks. Due to the timing issues of sampling and the samples return to earth, identification of particular microorganisms causing a particular inflight medical event is difficult. Further analyses are underway.

Oubre, Cherie M.↗