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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Study of spectral/radiometric characteristics of the Thematic Mapper for land use applications

Progress during the Environmental Research Institute of Michigan-ERIM's and 5 image data quality assessment program for the thematic mapper is described. Analyses of LANDSAT 5 TM radiometric characteristics were performed. Effects which had earlier been found in LANDSAT 4 TM data were found to be present in LANDSAT 5 data as well, including: (1) scan direction related signal droop; (2) scan correlated level shifts; and (3) low frequency coherent noise. Coincident LANDSAT 4 and 5 raw TM data were analyzed, and band by band relationships between the two sensors were derived. Earlier efforts which developed an information theoretic measure of multispectral information content were continued, comparing TM and MSS information content.

Malila, W. A.↗
DOE OSTI2015

The accurate assessment of small-angle X-ray scattering data

Small-angle X-ray scattering (SAXS) has grown in popularity in recent times with the advent of bright synchrotron X-ray sources, powerful computational resources and algorithms enabling the calculation of increasingly complex models. However, the lack of standardized data-quality metrics presents difficulties for the growing user community in accurately assessing the quality of experimental SAXS data. Here, a series of metrics to quantitatively describe SAXS data in an objective manner using statistical evaluations are defined. These metrics are applied to identify the effects of radiation damage, concentration dependence and interparticle interactions on SAXS data from a set of 27 previously described targets for which high-resolution structures have been determined via X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. Studies show that these metrics are sufficient to characterize SAXS data quality on a small sample set with statistical rigor and sensitivity similar to or better than manual analysis. The development of data-quality analysis strategies such as these initial efforts is needed to enable the accurate and unbiased assessment of SAXS data quality.

36 MATERIALS SCIENCE↗
NASA NTRS2017

It's Only a Phase: Applying the 5 Phases of Clinical Trials to the NSCR Model Improvement Process

NASA limits astronaut radiation exposures to a 3% risk of exposure-induced death from cancer (REID) at the upper 95% confidence level. Since astronauts approach this limit, it is important that the estimate of REID be as accurate as possible. The NASA Space Cancer Risk 2012 (NSCR-2012) model has been the standard for NASA's space radiation protection guidelines since its publication in 2013. The model incorporates elements from U.S. baseline statistics, Japanese atomic bomb survivor research, animal models, cellular studies, and radiation transport to calculate astronaut baseline risk of cancer and REID. The NSCR model is under constant revision to ensure emerging research is incorporated into radiation protection standards. It is important to develop guidelines, however, to determine what new research is appropriate for integration. Certain standards of transparency are necessary in order to assess data quality, statistical quality, and analytical quality. To this effect, all original source code and any raw data used to develop the code are required to confirm there are no errors which significantly change reported outcomes. It is possible to apply a clinical trials approach to select and assess the improvement concepts that will be incorporated into future iterations of NSCR. This poster describes the five phases of clinical trials research, pre-clinical research, and clinical research phases I-IV, explaining how each step can be translated into an appropriate NSCR model selection guideline.

Elgart, S. R.↗
NASA NTRS2022

Homogenization of the Observatoire de Haute Provence electrochemical concentration cell (ECC) ozonesonde data record: comparison with lidar and satellite observations

The Observatoire de Haute Provence (OHP) weekly Electrochemical Concentration Cell (ECC) ozonesonde data have been homogenized for the period 1991-2021 according to the recommendations of the Ozonesonde Data Quality Assessment (O3S-DQA) panel. The assessment of the ECC homogenization benefit has been carried out using comparisons with other ozone measuring ground based instruments at the same station (lidar, surface measurements) and with collocated satellite observations of the O3vertical profile by Microwave Limb Sounder (MLS). The major differences between uncorrected and5homogenized ECC data are related to a change of ozonesonde type in 1997, removal of the pressure dependency of the ECC background current and correction of internal pump temperature. The original 3-4 ppbv positive bias between ECC and Lidar in the troposphere is corrected with the homogenization. The ECC 30-years trends of the seasonally adjusted ozone concentrations are also significantly improved in both the troposphere and the stratosphere after the ECC homogenization, as shown by the ECC/lidar or ECC/surface ozone trend comparisons. A -0.19% per year negative trend of the normalization factor (NT)10calculated using independent measurements of the total ozone column (TOC) at OHP disappears after homogenization of the ECC data. There is however a remaining -3.7% negative bias in the TOC which is likely related to an underestimate of the ECC concentrations in the stratosphere above 50 hPa. Differences between TOC measured by homogenized ECC and satellite observations show a smaller bias of -1%. Comparisons between homogenized ECC and OHP stratospheric lidar and MLS observations below 26 km are slightly negative (-2%) or positive (+2%), respectively. Both the comparisons with lidar and15satellite observations suggest that homogenization increases the negative bias of the ECC to values lower than -6% above 28km. The reason for this bias is still unclear, but a possible explanation might be related to freezing or evaporation of the sonde solution in the stratosphere.

Ozonesondes↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of MS data is hindered by proprietary proteomics visualization software designed for particular workflows and academic software that lack visualization tools. We built PSpecteR, an open-source and interactive R Shiny web application to address these issues, with support for several steps of proteomics data processing, including: reading various mass spectrometry files, running open-source database search tools, labelling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing) is available for easy local installation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2014

Resilient Monitoring Systems: Architecture, Design, and Application to Boiler/Turbine Plant

Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this work is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools. The performance of the overall system is evaluated using a simplified boiler/turbine plant. The measure of resiliency is quantified using Kullback-Leibler divergence, and is shown to be sufficiently high in all scenarios considered.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Results of a demonstration of the use of Differential Very Long Baseline Interferometry data for precise navigation of interplanetary spacecraft

A set of experiments in the use of Differential Very Long Baseline Interferometry for spacecraft navigation have been completed. Data using both Voyager spacecraft and a single quasar were acquired during the Jupiter encounter time period. The data were processed and analyzed in order to assess the navigation accuracy of Differential Very Long Baseline Interferometry. The paper focuses on the data reduction and techniques for assessing data quality and consistency.

Christensen, C. S.↗
NASA NTRS1980

Differential Very Long Baseline Interferometry (delta VLBI) spacecraft tracking system demonstration. Part 2: Data acquisition and processing

A set of experiments in the use of Differential Very Long Baseline Interferometry (delta VLBI) for spacecraft navigation were completed. Data using both Voyager spacecraft and a single quasar were acquired during the Jupiter encounter time period. The data were processed and analyzed to assess the navigation accuracy of delta VLBI. The data reduction and techniques for assessing data quality and consistency are discussed.

Christensen, C. S.↗
NASA NTRS1984

The applications developmental data system

This paper describes a research and development system under development at NASA Goddard Space Flight Center (GSFC) for processing Landsat-4 Thematic Mapper (TM) data at high throughput rates. This system, called the Applications Developmental Data System (ADDS) is being developed with 2 objectives. First, during the initial year of Landsat-4 operations, ADDS provides an essential link in processing the TM images for image data quality assessment. The second objective is to demonstrate the ability to produce a radiometrically corrected TM image in 8 minutes and a geometrically correct image in 16 minutes. The processing rates currently achieved are presented.

Mocarsky, W. L.↗
NASA NTRS2010

Experimental Space Shuttle Orbiter Studies to Acquire Data for Code and Flight Heating Model Validation

In an experimental study to obtain detailed heating data over the Space Shuttle Orbiter, CUBRC has completed an extensive matrix of experiments using three distinct models and two unique hypervelocity wind tunnel facilities. This detailed data will be employed to assess heating augmentation due to boundary layer transition on the Orbiter wing leading edge and wind side acreage with comparisons to computational methods and flight data obtained during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM during STS-119 reentry. These comparisons will facilitate critical updates to be made to the engineering tools employed to make assessments about natural and tripped boundary layer transition during Orbiter reentry. To achieve the goals of this study data was obtained over a range of Mach numbers from 10 to 18, with flight scaled Reynolds numbers and model attitudes representing key points on the Orbiter reentry trajectory. The first of these studies were performed as an integral part of Return to Flight activities following the accident that occurred during the reentry of the Space Shuttle Columbia (STS-107) in February of 2003. This accident was caused by debris, which originated from the foam covering the external tank bipod fitting ramps, striking and damaging critical wing leading edge heating tiles that reside in the Orbiter bow shock/wing interaction region. During investigation of the accident aeroheating team members discovered that only a limited amount of experimental wing leading edge data existed in this critical peak heating area and a need arose to acquire a detailed dataset of heating in this region. This new dataset was acquired in three phases consisting of a risk mitigation phase employing a 1.8% scale Orbiter model with special temperature sensitive paint covering the wing leading edge, a 0.9% scale Orbiter model with high resolution thin-film instrumentation in the span direction, and the primary 1.8% scale Orbiter model with detailed thin-film resolution in both the span and chord direction in the area of peak heating. Additional objectives of this first study included: obtaining natural or tripped turbulent wing leading edge heating levels, assessing the effectiveness of protuberances and cavities placed at specified locations on the orbiter over a range of Mach numbers and Reynolds numbers to evaluate and compare to existing engineering and computational tools, obtaining cavity floor heating to aid in the verification of cavity heating correlations, acquiring control surface deflection heating data on both the main body flap and elevons, and obtain high speed schlieren videos of the interaction of the orbiter nose bow shock with the wing leading edge. To support these objectives, the stainless steel 1.8% scale orbiter model in addition to the sensors on the wing leading edge was instrumented down the windward centerline, over the wing acreage on the port side, and painted with temperature sensitive paint on the starboard side wing acreage. In all, the stainless steel 1.8% scale Orbiter model was instrumented with over three-hundred highly sensitive thin-film heating sensors, two-hundred of which were located in the wing leading edge shock interaction region. Further experimental studies will also be performed following the successful acquisition of flight data during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM on STS-119 at specific data points simulating flight conditions and geometries. Additional instrumentation and a protuberance matching the layout present during the STS-119 boundary layer transition flight experiment were added with testing performed at Mach number and Reynolds number conditions simulating conditions experienced in flight. In addition to the experimental studies, CUBRC also performed a large amount of CFD analysis to confirm and validate not only the tunnel freestream conditions, but also 3D flows over the orbiter acreage, wing leading edge, and controlurfaces to assess data quality, shock interaction locations, and control surface separation regions. This analysis is a standard part of any experimental program at CUBRC, and this information was of key importance for post-test data quality analysis and understanding particular phenomena seen in the data. All work during this effort was sponsored and paid for by the NASA Space Shuttle Program Office at the Johnson Space Center in Houston, Texas.

Wadhams, T. P.↗
NASA NTRS2016

Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photo-assimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be up-regulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS (Auxin-Regulated Gene Involved in Organ Size)-like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm up-regulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

venation↗
NASA NTRS2016

Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASAs GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-boxkelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASAs VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

Weitzeal, A. J.↗
NASA NTRS2016

Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

Weitzeal, A. J.↗