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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Leveraging Big Data Analysis Techniques for U.S. Vocational Vehicle Drive Cycle Characterization, Segmentation, and Development

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real-world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical drive cycle data captured from medium- and heavy-duty vehicles operating across the United States. The data encompass existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topographies ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. This paper includes the results of the statistical analysis performed by NREL and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work is also included.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Rotating-Coil Measurement System for Small-Bore-Diameter Magnet Characterization

Rotating-coil measurement systems are widely used to measure the multipolar fields of particle accelerator magnets. This paper presents a rotating-coil measurement system that aims at providing a complete data set for the characterization of quadrupole magnets with small bore diameters (26 mm). The PCB magnetometer design represents a challenging goal for this type of transducer. It is characterized by an aspect ratio 30% higher than the state of the art, imposed by the reduced dimension of the external radius of the rotating shaft and the necessity of covering the entire magnet effective length (500 mm or higher). The system design required a novel design for the mechanical asset, also considering the innovation represented by the commercial carbon fiber tube, housing the PCB magnetometer. Moreover, the measurement system is based primarily on standard and commercially available components, with simplified control and post-processing software applications. The system and its components are cross-calibrated using a stretched-wire system and another rotating-coil system. The measurement precision is established in a measurement campaign performed on a quadrupole magnet characterized by an inner bore diameter of 45 mm.

42 ENGINEERING↗
DOE OSTI2018

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. In this work, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. Finally, the NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

58 GEOSCIENCES↗
DOE OSTI2016

The exoplanet mass-ratio function from the MOA-II survey: discovery of a break and likely peak at a neptune mass

We report the results of the statistical analysis of planetary signals discovered in MOA-II microlensing survey alert system events from 2007 to 2012. We determine the survey sensitivity as a function of planet–star mass ratio, q, and projected planet–star separation, s, in Einstein radius units. We find that the mass-ratio function is not a single power law, but has a change in slope at q ∼ 10{sup −4}, corresponding to ∼20 M {sub ⊕} for the median host-star mass of ∼0.6 M{sub ⊙}. We find significant planetary signals in 23 of the 1474 alert events that are well-characterized by the MOA-II survey data alone. Data from other groups are used only to characterize planetary signals that have been identified in the MOA data alone. The distribution of mass ratios and separations of the planets found in our sample are well fit by a broken power-law model of the form dN{sub pl}/(dlogqdlogs)=A(q/q{sub br}){sup n}s{sup m} dex{sup −2} for q > q {sub br} and dN{sub pl}/(dlogqdlogs)=A(q/q{sub br}){sup p}s{sup m} dex{sup −2} for q < q {sub br}, where q {sub br} is the mass ratio of the break. We also combine this analysis with the previous analyses of Gould et al. and Cassan et al., bringing the total sample to 30 planets. This combined analysis yields A=0.61{sub −0.16}{sup +0.21}, n = −0.93 ± 0.13, m=0.49{sub −0.49}{sup +0.47}, and p=0.6{sub −0.4}{sup +0.5} for q {sub br} ≡ 1.7 × 10{sup −4}. The unbroken power-law model is disfavored with a p-value of 0.0022, which corresponds to a Bayes factor of 27 favoring the broken power-law model. These results imply that cold Neptunes are likely to be the most common type of planets beyond the snow line.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

The Exoplanet Mass-Ratio Function From the MOA-II Survey: Discovery of a Break and Likely Peak at a Neptune Mass

We report the results of the statistical analysis of planetary signals discovered in MOA-II microlensing survey alert system events from 2007 to 2012. We determine the survey sensitivity as a function of planet star mass ratio, q, and projected planet star separation, s, in Einstein radius units. We find that the mass-ratio function is not a single power law, but has a change in slope at q approx.10(exp -4), corresponding to approx. 20 Stellar Mass for the median host-star mass of approx. 0.6 M. We find significant planetary signals in 23 of the 1474 alert events that are well-characterized by the MOA-II survey data alone. Data from other groups are used only to characterize planetary signals that have been identified in the MOA data alone. The distribution of mass ratios and separations of the planets found in our sample are well fit by a broken power-law model. We also combine this analysis with the previous analyses of Gould et al. and Cassan et al., bringing the total sample to 30 planets. The unbroken power-law model is disfavored with a p-value of 0.0022, which corresponds to a Bayes factor of 27 favoring the broken power-law model. These results imply that cold Neptunes are likely to be the most common type of planets beyond the snow line.

MOA-II microlensing survey alert↗
NASA NTRS2012

Using Spatial Density to Characterize Volcanic Fields on Mars

We introduce a new tool to planetary geology for quantifying the spatial arrangement of vent fields and volcanic provinces using non parametric kernel density estimation. Unlike parametricmethods where spatial density, and thus the spatial arrangement of volcanic vents, is simplified to fit a standard statistical distribution, non parametric methods offer more objective and data driven techniques to characterize volcanic vent fields. This method is applied to Syria Planum volcanic vent catalog data as well as catalog data for a vent field south of Pavonis Mons. The spatial densities are compared to terrestrial volcanic fields.

Richardson, J. A.↗
DOE OSTI2024

Characterization of Non-Science Grade DESI CCDs and Creating Additional ICARUS Monitoring Metrics for Data Quality

DESI, otherwise known as the Dark Energy Spectroscopic Instrument, is an astronomical project measuring millions of optical spectra to characterize Dark Energy. The survey has been running for 3 years. My work delves into the data taking/analysis side of the DESI CCDs (Charge-Coupled Devices) which will help in swapping out broken CCDs on the instrument. Additionally, I worked on making new metrics for ICARUS, Imaging Cosmic and Rare Underground Signals for their monitoring of run data. This paper will talk about the types of characterizations done for DESI and additional metrics for data quality and tools used for ICARUS monitoring.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Nonradiative lifetime extraction using power-dependent relative photoluminescence of III-V semiconductor double-heterostructures

A power-dependent relative photoluminescence measurement method is developed for double-heterostructures composed of III-V semiconductors. Analyzing the data yields insight into the radiative efficiency of the absorbing layer as a function of laser intensity. Four GaAs samples of different thicknesses are characterized, and the measured data are corrected for dependencies of carrier concentration and photon recycling. This correction procedure is described and discussed in detail in order to determine the material's Shockley-Read-Hall lifetime as a function of excitation intensity. The procedure assumes 100% internal radiative efficiency under the highest injection conditions, and we show this leads to less than 0.5% uncertainty. The resulting GaAs material demonstrates a 5.7 ± 0.5 ns nonradiative lifetime across all samples of similar doping (2–3 × 10{sup 17 }cm{sup −3}) for an injected excess carrier concentration below 4 × 10{sup 12 }cm{sup −3}. This increases considerably up to longer than 1 μs under high injection levels due to a trap saturation effect. The method is also shown to give insight into bulk and interface recombination.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Hawaii Play Fairway Analysis Results

Probability grid of Hawaii Play Fairway Analysis results. This analysis was aimed at assessing the geothermal resource potential throughout Hawaii.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Annular Core Research Reactor Pulse History Report: 2003-2017

This report documents and describes the tabulation and analysis of historical pulse operation data from the Annular Core Research Reactor (ACRR) at Sandia National Laboratories (SNL). The pulse data was obtained from a combination of pulse log files generated at the control console and pulse diagnostics system data. The pulses presented were performed within the time period of April 2003 and December 2017. A brief analysis of the data is included to characterize the aggregate behavior of ACRR pulses with respect to theoretical treatments based on the point reactor kinetics model. It is expected that the data presented will provide an organized and consolidated resource to reference historical pulse data at the ACRR for use in analyses, verification and validation, and general understanding of the machine. A comprehensive set of data is presented to the reader in the appendices.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1979

Radio frequency interference effects of continuous wave signals on telemetry data, part 2

The results of radio frequency interference tests and the derived telemetry bit SNR degradation model, which includes the telemetry data rate and the telemetry data power as independent variables for characterizing the continuous wave interference effects on telemetry data, are presented. The telemetry bit SNR degradation model was implemented in the second version of the Deep Space Interference Prediction software.

Low, P. W.↗
NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
DOE OSTI2014

Implementation Approach for Plug-in Electric Vehicles at Joint Base Lewis McChord: Task 4

This study focused on Joint Base Lewis McChord (JBLM), which is located in Washington State. Task 1 consisted of a survey of the non-tactical fleet of vehicles at JBLM to begin the review of vehicle mission assignments and the types of vehicles in service. In Task 2, daily operational characteristics of select vehicles were identified and vehicle movements were recorded in data loggers in order to characterize the vehicles’ missions. In Task 3, the results of the data analysis and observations were provided. Individual observations of the selected vehicles provided the basis for recommendations related to PEV adoption (i.e., whether a battery electric vehicle or plug-in hybrid electric vehicle [collectively referred to as PEVs] can fulfill the mission requirements0, as well as the basis for recommendations related to placement of PEV charging infrastructure. This report focuses on an implementation plan for the near-term adoption of PEVs into the JBLM fleet.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1981

A software system for emission spectrometry

A computer system was developed for an emission spectrometry facility consisting of a direct current (DC) argon arc spectrograph optically coupled to an inductively coupled plasma multichannel spectrometer. Custom hardware and software were designed to control analytical functions and perform data acquisition. The software system was designed to make operation of the facility simple for routine operation and flexible for research and development. Special software was written to collect data under controlled conditions to characterize and monitor system response. One sequence collects intensity versus time data on all channels and displays the data graphically. These profiles are useful in studying the effects of operating parameters on measurement precision. Another special sequence performs calibration using a spline curve fit procedure. Routines were also written to measure dark currents and signals from a standard tungsten halogen lamp mounted in place of the DC arc. For quality control purposes, histories of these values are kept and monitored for excess scatter or drift.

Auping, J. V.↗
NASA NTRS2016

Viscoplastic Characterization of Ti-6-4: Experiments

As part of a continued effort to improve the understanding of material time-dependent response, a series of mechanical tests have been conducted on the titanium alloy, Ti-6Al-4V. Tensile, creep, and stress relaxation tests were performed over a wide range of temperatures and strain rates to engage various amounts of time-dependent behavior. Additional tests were conducted that involved loading steps, overloads, dwell periods, and block loading segments to characterize the interaction between plasticity and time-dependent behavior. These data will be used to characterize a recently developed, viscoelastoplastic constitutive model with a goal toward better estimates of aerospace component behavior, resulting in improved safety.

mechanical properties↗
DOE OSTI2014

Data communications in a parallel active messaging interface of a parallel computer

Data communications in a parallel active messaging interface ('PAMI') or a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution of a compute node, including specification of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications instruction, the instruction characterized by instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance witht the instruction type, the transfer data from the origin endpoin to the target endpoint.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A sparse Gaussian sigmoid basis function approximation of hyperspectral data for detection of solids

We define a new characterization of emissivity and reflectance curves for compositional exploitation of hyperspectral data. Here, our method decomposes each spectrum into a sparse set of Gaussian sigmoid components using penalized regression. Detection is based on the combination of Gaussian sigmoid components unique to a target material. Focusing on the presence of spectral upslopes and downslopes rather than spectral correlations makes detection more robust to both target variation and spectral variability from atmosphere and background encountered during the collection process. We present simulation studies that demonstrate the potential to reduce false positive rates without compromising sensitivity. Characterization of long–wave infrared (LWIR) experimental data validates our method using minerals of different particle sizes, measurement angles, and collection conditions.

97 MATHEMATICS AND COMPUTING↗