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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

MISSE 6 Stressed Polymers Experiment Atomic Oxygen Erosion Data

Polymers and other oxidizable materials used on the exterior of spacecraft in the low Earth orbit (LEO) space environment can be eroded away by reaction with atomic oxygen (AO). For spacecraft design, it is important to know the LEO AO erosion yield, Ey (volume loss per incident oxygen atom), of materials susceptible to AO erosion. The Stressed Polymers Experiment was developed and flown as part of the Materials International Space Station Experiment 6 (MISSE 6) to compare the AO erosion yields of stressed and non-stressed polymers to determine if erosion is dependent upon stress while in LEO. The experiment contained 36 thin film polymer samples that were exposed to ram AO for 1.45 years. This paper provides an overview of the Stressed Polymers Experiment with details on the polymers flown, the characterization techniques used, the AO fluence, and the erosion yield results. The MISSE 6 data are compared to data for similar samples flown on previous MISSE missions to determine fluence or solar radiation effects on erosion yield.

deGroh, Kim K.↗
DOE OSTI2016

FY2017 Pilot Project Plan for the Nuclear Energy Knowledge and Validation Center Initiative

To prepare for technical development of computational code validation under the Nuclear Energy Knowledge and Validation Center (NEKVAC) initiative, several meetings were held by a group of experts of the Idaho National Laboratory (INL) and the Oak Ridge National Laboratory (ORNL) to develop requirements of, and formulate a structure for, a transient fuel database through leveraging existing resources. It was concluded in discussions of these meetings that a pilot project is needed to address the most fundamental issues that can generate immediate stimulus to near-future validation developments as well as long-lasting benefits to NEKVAC operation. The present project is proposed based on the consensus of these discussions. Analysis of common scenarios in code validation indicates that the incapability of acquiring satisfactory validation data is often a showstopper that must first be tackled before any confident validation developments can be carried out. Validation data are usually found scattered in different places most likely with interrelationships among the data not well documented, incomplete with information for some parameters missing, nonexistent, or unrealistic to experimentally generate. Furthermore, with very different technical backgrounds, the modeler, the experimentalist, and the knowledgebase developer that must be involved in validation data development often cannot communicate effectively without a data package template that is representative of the data structure for the information domain of interest to the desired code validation. This pilot project is proposed to use the legendary TREAT Experiments Database to provide core elements for creating an ideal validation data package. Data gaps and missing data interrelationships will be identified from these core elements. All the identified missing elements will then be filled in with experimental data if available from other existing sources or with dummy data if nonexistent. The resulting hybrid validation data package (composed of experimental and dummy data) will provide a clear and complete instance delineating the structure of the desired validation data and enabling effective communication among the modeler, the experimentalist, and the knowledgebase developer. With a good common understanding of the desired data structure by the three parties of subject matter experts, further existing data hunting will be effectively conducted, new experimental data generation will be realistically pursued, knowledgebase schema will be practically designed; and code validation will be confidently planned.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗
DOE OSTI2014

An algorithm to estimate the object support in truncated images

Purpose: Truncation artifacts in CT occur if the object to be imaged extends past the scanner field of view (SFOV). These artifacts impede diagnosis and could possibly introduce errors in dose plans for radiation therapy. Several approaches exist for correcting truncation artifacts, but existing correction algorithms do not accurately recover the skin line (or support) of the patient, which is important in some dose planning methods. The purpose of this paper was to develop an iterative algorithm that recovers the support of the object. Methods: The authors assume that the truncated portion of the image is made up of soft tissue of uniform CT number and attempt to find a shape consistent with the measured data. Each known measurement in the sinogram is interpreted as an estimate of missing mass along a line. An initial estimate of the object support is generated by thresholding a reconstruction made using a previous truncation artifact correction algorithm (e.g., water cylinder extrapolation). This object support is iteratively deformed to reduce the inconsistency with the measured data. The missing data are estimated using this object support to complete the dataset. The method was tested on simulated and experimentally truncated CT data. Results: The proposed algorithm produces a better defined skin line than water cylinder extrapolation. On the experimental data, the RMS error of the skin line is reduced by about 60%. For moderately truncated images, some soft tissue contrast is retained near the SFOV. As the extent of truncation increases, the soft tissue contrast outside the SFOV becomes unusable although the skin line remains clearly defined, and in reformatted images it varies smoothly from slice to slice as expected. Conclusions: The support recovery algorithm provides a more accurate estimate of the patient outline than thresholded, basic water cylinder extrapolation, and may be preferred in some radiation therapy applications.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2004

Making the most of missing values : object clustering with partial data in astronomy

We demonstrate a clustering analysis algorithm, KSC, that a) uses all observed values and b) does not discard the partially observed objects. KSC uses soft constraints defined by the fully observed objects to assist in the grouping of objects with missing values. We present an analysis of objects taken from the Sloan Digital Sky Survey to demonstrate how imputing the values can be misleading and why the KSC approach can produce more appropriate results.

clustering↗
DOE OSTI2014

Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using $\sqrt{s}=8 $ TeV proton-proton collision data

A search for squarks and gluinos in final states containing high-p T jets, missing transverse momentum and no electrons or muons is presented. The data were recorded in 2012 by the ATLAS experiment in $ \sqrt{s} $ = 8 TeV proton-proton collisions at the Large Hadron Collider, with a total integrated luminosity of 20.3 fb -1 . Results are interpreted in a variety of simplified and specific supersymmetry-breaking models assuming that R-parity is conserved and that the lightest neutralino is the lightest supersymmetric particle. An exclusion limit at the 95% confidence level on the mass of the gluino is set at 1330 GeV for a simplified model incorporating only a gluino and the lightest neutralino. For a simplified model involving the strong production of first- and second-generation squarks, squark masses below 850 GeV (440 GeV) are excluded for a massless lightest neutralino, assuming mass degenerate (single light-flavour) squarks. In mSUGRA/CMSSM models with tan β = 30, A 0 = -2m 0 and μ > 0, squarks and gluinos of equal mass are excluded for masses below 1700 GeV. Additional limits are set for non-universal Higgs mass models with gaugino mediation and for simplified models involving the pair production of gluinos, each decaying to a top squark and a top quark, with the top squark decaying to a charm quark and a neutralino. These limits extend the region of supersymmetric parameter space excluded by previous searches with the ATLAS detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRSDate not supplied

Identifying Trends in Landing Profiles After Long Duration Spaceflight

BACKGROUND As NASA’s mission programs begin to shift from Low Earth Orbit (LEO) to planetary mission profiles, the importance of understanding the phenomena a human experience when returning from spaceflight is of increased importance. The purpose of this study was to assess trends across individuals in the symptoms experienced during the landing process of long duration ISS missions. METHODS Data for this study is an aggregation of mission level data from forms used by crew surgeons at R+0, electronic medical records, notes from weekly Space Medicine Operation Team meetings, records in the Shuttle Mission Data Archive, and data collected by research teams in the Human Research Program (HRP). Aggregated data was used in descriptive analysis of the trends in medical events and countermeasure use. The data was also used to identify high value targets for emphasis in on going surveillance efforts. RESULTS Completeness of reporting varied greatly over time and across missions dependent on information sources available for data extraction. Landing forms provided the most complete data but still missed or left unconfirmed, several, common symptoms, and countermeasures. Data aggregation did show to cover gaps within individual data sources. Commonly reported symptoms included nystagmus, vertigo, and nausea. Common protocols for fluid loading and meclizine are reflected within the data set. DISCUSSION The data set generated in this project provides a foundation that show how symptoms of astronauts as they return to Earth varies across the long duration ISS missions. While there is an abundance of information provided within the data set, there are still inconsistencies in reporting especially in the absence of the landing day form. Further data collection practices should be standardized to ease the burden on crew surgeons during R+0 exams and better collect high value variables.

Sam Jacobs↗
DOE OSTI2014

Sensor Data Management, Validation, Correction, and Provenance for Building Technologies

Oak Ridge National Laboratory (ORNL) conducts research on technologies that use a wide range of sensors to develop and characterize building energy performance. The management of high-resolution sensor data, analysis, and tracing lineage of such activities is challenging. Missing or corrupt data due to sensor failure, fouling, drifting, calibration error, or data logger failure is another issue. This paper focuses on sensor data management, validation, correction, and provenance to combat these issues, ensuring complete and accurate sensor datasets for building technologies applications and research. The design and development of two integrated software products are discussed: Sensor Data Validation and Correction (SensorDVC) and the Provenance Data Management System (ProvDMS) platform.

Data management↗
NASA NTRS1996

Packaging and distributing ecological data from multisite studies

Studies of global change and other regional issues depend on ecological data collected at multiple study areas or sites. An information system model is proposed for compiling diverse data from dispersed sources so that the data are consistent, complete, and readily available. The model includes investigators who collect and analyze field measurements, science teams that synthesize data, a project information system that collates data, a data archive center that distributes data to secondary users, and a master data directory that provides broader searching opportunities. Special attention to format consistency is required, such as units of measure, spatial coordinates, dates, and notation for missing values. Often data may need to be enhanced by estimating missing values, aggregating to common temporal units, or adding other related data such as climatic and soils data. Full documentation, an efficient data distribution mechanism, and an equitable way to acknowledge the original source of data are also required.

Information Systems↗
NASA NTRS2010

Thermal Control Materials on MISSE-6 with Comparison to Earlier Flight Data

This slide presentation reviews the results of exposing thermal control materials as studied during Materials International Space Station Experiment-6 (MISSE-6). It discusses the location of the materials, and the type of environmental exposure. It then reviews the effect on the various types of coatings, showing the results with graphs and photos of the samples after the experimental exposure to the low earth orbital environment for comparison with the control sample and with earlier flights.

Finckenor, Miria M.↗
DOE OSTI2023

Observation of large missing-momentum ( e , e ' p ) cross-section scaling and the onset of correlated-pair dominance in nuclei

Herein we report the first measurement of $x_B$-scaling in $(e,e'p)$ cross-section ratios off nuclei relative to deuterium at large missing-momentum of $350 \leq p_{miss} \leq 600$ MeV/c. The observed scaling extends over a kinematic range of $0.7 \leq x_B \leq 1.8$, which is significantly wider than $1.4 \leq x_B \leq 1.8$ previously observed for inclusive $(e,e')$ cross-section ratios. The $x_B$-integrated cross-section ratios become constant (i.e., scale) beginning at $p_{miss}\approx k_F$, the nuclear Fermi momentum. Comparing with theoretical calculations we find good agreement with Generalized Contact Formalism calculations for high missing-momentum ($> 375$ MeV/c), suggesting the observed scaling results from interacting with nucleons in short-range correlated (SRC) pairs. For low missing-momenta, mean-field calculations show good agreement with the data for $p_{miss}\le k_F$, and suggest that contributions to the measured cross-section ratios from scattering off single, un-correlated, nucleons are non-negligible up to $p_{miss}\approx 350$ MeV/c. Therefore, SRCs become dominant in nuclei at $p_{miss}\approx 350$ MeV/c, well above the nuclear Fermi Surface of $k_F \approx 250$ MeV/c.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

First Observation of Large Missing-Momentum (e,e'p) Cross-Section Scaling and the onset of Correlated-Pair Dominance in Nuclei

We report the first measurement of $x_B$-scaling in $(e,e'p)$ cross-section ratios off nuclei relative to deuterium at large missing-momentum of $350 \leq p_{miss} \leq 600$ MeV/c. The observed scaling extends over a kinematic range of $0.7 \leq x_B \leq 1.8$, which is significantly wider than $1.4 \leq x_B \leq 1.8$ previously observed for inclusive $(e,e')$ cross-section ratios. The $x_B$-integrated cross-section ratios become constant (i.e., scale) beginning at $p_{miss}\approx k_F$, the nuclear Fermi momentum. Comparing with theoretical calculations we find good agreement with Generalized Contact Formalism calculations for high missing-momentum ($> 375$ MeV/c), suggesting the observed scaling results from interacting with nucleons in short-range correlated (SRC) pairs. For low missing-momenta, mean-field calculations show good agreement with the data for $p_{miss}\le k_F$, and suggest that contributions to the measured cross-section ratios from scattering off single, un-correlated, nucleons are non-negligible up to $p_{miss}\approx 350$ MeV/c. Therefore, SRCs become dominant in nuclei at $p_{miss}\approx 350$ MeV/c, well above the nuclear Fermi Surface of $k_F \approx 250$ MeV/c.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Improving Quality of Observational Streaming Medical Data by Using Long Short-Term Memory Networks (LSTMs)

We present an exploration of the encoder-decoder structured Long Short-Term Memory Network (LSTM) as a detector of the anomalous missing observations in streaming medical data by using the difference between the LSTM-reconstructed and observed values as the anomaly detector. We experiment with time-series data from bedside monitoring devices from the available Medical Information Mart for Intensive Care Database (MIMIC). Our results show that not only encoder-decoder LSTM approach works well for detecting the difference between anomalous and normal missing observations in streaming medical data, but also has an imputation potential for the missing data.

Bowie, Michael B.↗
DOE OSTI2016

A DARK ENERGY CAMERA SEARCH FOR AN OPTICAL COUNTERPART TO THE FIRST ADVANCED LIGO GRAVITATIONAL WAVE EVENT GW150914

We report the results of a deep search for an optical counterpart to the gravitational wave (GW) event GW150914, the first trigger from the Advanced LIGO GW detectors. We used the Dark Energy Camera (DECam) to image a 102 deg{sup 2} area, corresponding to 38% of the initial trigger high-probability sky region and to 11% of the revised high-probability region. We observed in the i and z bands at 4–5, 7, and 24 days after the trigger. The median 5 σ point-source limiting magnitudes of our search images are i = 22.5 and z = 21.8 mag. We processed the images through a difference-imaging pipeline using templates from pre-existing Dark Energy Survey data and publicly available DECam data. Due to missing template observations and other losses, our effective search area subtends 40 deg{sup 2}, corresponding to a 12% total probability in the initial map and 3% in the final map. In this area, we search for objects that decline significantly between days 4–5 and day 7, and are undetectable by day 24, finding none to typical magnitude limits of i = 21.5, 21.1, 20.1 for object colors ( i − z ) = 1, 0, −1, respectively. Our search demonstrates the feasibility of a dedicated search program with DECam and bodes well for future research in this emerging field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2020

Multimodal Data Fusion via Entropy Minimization

The use of gradient-based data-driven models to solve a range of real-world remote sensing problems can in practice be limited by the uniformity of available data. Use of data from disparate sensor types, resolutions, and qualities typically requires compromises based on assumptions that are made prior to model training and may not necessarily be optimal given over-arching objectives. For example, while deep neural networks (NNs) are state-of-the-art in a variety of target detection problems, training them typically requires either limiting the training data to a subset over which uniformity can be enforced or training independent models which subsequently require additional score fusion. The method we introduce here seeks to leverage the benefits of both approaches by allowing correlated inputs from different data sources to co-influence preferred model solutions, while maintaining flexibility over missing and mismatching data. In this work we propose a new data fusion technique for gradient updated models based on entropy minimization and experimentally validate it on a hyperspectral target detection dataset. We demonstrate superior performance compared to currently available techniques using a range of realistic data scenarios, where available data has limited spacial overlap and resolution.

97 MATHEMATICS AND COMPUTING↗