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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Effects of time-shifted data on flight determined stability and control derivatives

Flight data were shifted in time by various increments to assess the effects of time shifts on estimates of stability and control derivatives produced by a maximum likelihood estimation method. Derivatives could be extracted from flight data with the maximum likelihood estimation method even if there was a considerable time shift in the data. Time shifts degraded the estimates of the derivatives, but the degradation was in a consistent rather than a random pattern. Time shifts in the control variables caused the most degradation, and the lateral-directional rotary derivatives were affected the most by time shifts in any variable.

Steers, S. T.↗
DOE OSTI2026

Methods for evaluation and treatment of data shift

This is a code repository for a set of tools for detecting and mitigating data shifts in machine learning. The goal of the tools is to provide capabilities for determining when new data sets differ from training data sets and for adapting existing models to new data or correcting data shifts (via domain adaptation). The components will be written in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning and signal processing.

Parikh, Nidhi↗
NASA NTRS1987

The detection of global convection on the sun by an analysis of line shift data of the John M. Wilcox Solar Observatory at Stanford University

An analysis of the absorption line shift data of the John M. Wilcox Solar Observatory at Stanford University has yielded signatures of the existence of global convection on the sun. These include persistent periodic time variations in the east-west component of the velocity fields defined by fitting a slope to the line shift data in a certain longitude window at a specified latitude and longitude by the least squares method. The amplitude of the velocity fields estimated from these variations is of the order of 100 m/s. The results of the analysis also suggest that several modes of global convection coexist in the solar convection zone. Details of the analysis are given.

Yoshimura, Hirokazu↗
NASA NTRS1987

The detection of global convection on the sun by an analysis of line shift data of the John M. Wilcox Solar Observatory at Stanford University

Signatures of the existence of the global convection in the sun were found in the absorption line shift data of the John M. Wilcox Solar Observatory at Stanford University. The signatures are characterized by persistent periodic time variations in the east-west component of the velocity fields defined by fitting a slope to the line shift data in a certain longitude window at a specified latitude and longitude by a least square method. The variations indicate that the amplitude of the velocity fields is about 100 m/s. It is suggested that several modes of global convection are coexisting in the solar convection zone.

Yoshimura, Hirokazu↗
DOE OSTI2020

Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion

NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a “sample and select” method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls ( 1 H, 15 N, and 13 C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

EOSDIS CMR: Shifting Data Discovery & Use into a Higher Gear

Earth observation data comes in many forms, formats, and from a multitude of sources; to make the best of a very large and diverse data catalog (data from a dozen different national Distributed Active Archive Centers (DAACs) as well as international sources), NASA has created a one-stop-shop for earth data consumers to view, find, and get the data they need regardless of its original source or format, which is powered by a Common Metadata Repository (CMR). CMR is the underpinning that allows for the visualization, search, discovery, manipulation, and acquisition of a variety of datasets, and as such it is constantly evolving to do more and serve our communities better; CMR has embraced community ownership by making itself an open-source API, being compatible with Catalog Services for the Web (CSW) and OpenSearch APIs, and by encouraging the user community to make and share improvements.

CMR↗
NASA NTRS2018

Shifting Data Collection from a Fixed to an Adaptive Sampling Paradigm

For domains where data are difficult to obtain due to human or resource limitations, an emphasis is needed to efficiently explore the dimensions of information spaces to acquire any given response of interest. Many disciplines are still making the transition from brute force, dense, full factorial exploration of their information spaces to a more efficient design of experiments approach; the latter being in use successfully for many decades in agricultural and automotive applications. Although this transition is still incomplete, groundwork must be laid for incorporating the next generation of algorithms to adaptively explore the information space in response to data collected, as well as any resulting empirical models (i.e., metamodels). The methodology in the present work was to compare metamodel quality using a fixed sampling technique compared to an adaptive sampling technique based on metamodel variance. In order to quantify metamodeling errors, a delta method was used to provide quantitative model variance estimates. The present methodology was applied to a design space with an air-breathing engine performance response. It was shown that competitive metamodel quality with lower associated error could be achieved for an adaptive sampling technique for the same level of effort as a fixed, a priori sampling technique.

Axdahl, Erik L.↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
DOE OSTI2021

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of x-ray diffraction, photoluminescence, Raman spectra, and other data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluating the Utility of Permethylated Polysaccharide Solution NMR Data for Characterization of Insoluble Plant Cell Wall Polysaccharides

Plant cell wall polysaccharide analysis encompasses the utilization of a variety of analytical tools, including gas and liquid chromatography, mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. These methods provide complementary data, which enable confident structural proposals of the many complex polysaccharide structures that exist in the complex matrices of plant cell walls. However, cell walls contain fractions of varying solubilities, and a few techniques are available that can analyze all fractions simultaneously. We have discovered that permethylation affords the complete dissolution of both soluble and insoluble polysaccharide fractions of plant cell walls in organic solvents such as chloroform or acetonitrile, which can then be analyzed by a number of analytical techniques including MS and NMR. In this work, NMR structure analysis of 10 permethylated polysaccharide standards was undertaken to generate chemical shift data providing insights into spectral changes that result from permethylation of polysaccharide residues. This information is of especial relevance to the structure analysis of insoluble polysaccharide materials that otherwise are not easily investigated by solution-state NMR methodologies. The preassigned NMR chemical shift data is shown to be vital for NMR structure analysis of minor polysaccharide components of plant cell walls that are particularly difficult to assign by NMR correlation data alone. With the assigned chemical shift data, we analyzed the permethylated samples of destarched, alcohol-insoluble residues of switchgrass and poplar by two-dimensional NMR spectral profiling. Thus, we identified, in addition to the major polysaccharide components, two minor polysaccharides, namely, <5% 3-linked arabinoxylan (switchgrass) and <2% glucomannan (poplar). In particular, the position of the arabinose residue in the arabinoxylan of the switchgrass sample was confidently assigned based on chemical shift values, which are highly sensitive to local chemical environments. Furthermore, the high resolution afforded by the 1H NMR spectra of the permethylated switchgrass and poplar samples allowed facile relative quantitative analysis of their polysaccharide composition, utilizing only a few milligrams of the cell wall material. As a result, the concepts herein developed will thus facilitate NMR structure analysis of insoluble plant cell wall polysaccharides, more so of minor cell wall components that are especially challenging to analyze with current methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2006

Paradigm Shift in Data Content and Informatics Infrastructure Required for Generalized Constitutive Modeling of Materials Behavior

Materials property information such as composition and thermophysical/mechanical properties abound in the literature. Oftentimes, however, the corresponding response curves from which these data are determined are missing or at the very least difficult to retrieve. Further, the paradigm for collecting materials property information has historically centered on (1) properties for materials comparison/selection purposes and (2) input requirements for conventional design/analysis methods. However, just as not all materials are alike or equal, neither are all constitutive models (and thus design/ analysis methods) equal; each model typically has its own specific and often unique required materials parameters, some directly measurable and others indirectly measurable. Therefore, the type and extent of materials information routinely collected is not always sufficient to meet the current, much less future, needs of the materials modeling community. Informatics has been defined as the science concerned with gathering, manipulating, storing, retrieving, and classifying recorded information. A key aspect of informatics is its focus on understanding problems and applying information technology as needed to address those problems. The primary objective of this article is to highlight the need for a paradigm shift in materials data collection, analysis, and dissemination so as to maximize the impact on both practitioners and researchers. Our hope is to identify and articulate what constitutes "sufficient" data content (i.e., quality and quantity) for developing, characterizing, and validating sophisticated nonlinear time- and history-dependent (hereditary) constitutive models. Likewise, the informatics infrastructure required for handling the potentially massive amounts of materials data will be discussed.

Arnold, S. M.↗
DOE OSTI2020

National-Tribal Critical Infrastructure Protection: Collaboration for Extraordinary National Security Benefit

This paper examines national and tribal collaborative opportunities to get ahead of the critical infrastructure insecurity problem. Recommendations are viewed through the lens of the Sandia Labs Tribal Cyber-Energy initiative and national security projects. Recommendations include 1) Collaboratively address national priority and shared challenges to gain faster and better solutions to national priority problems on a smaller yet comprehensive American Indian and Alaskan Native sovereign single-point of authority scale 2) Utilize newer standards-based technologies to provide scalable, capable, and manageable solutions for greatly expanded and connected national critical infrastructures 3) Employ Cyber-Physical-Resilient design preliminary analysis to define concept- to-disposition design requirements for preemptive critical infrastructure risk mitigation and baked-in security; 4) Develop data-centric protection to provide increased information asset protection as data shifts from data-owner operated on-premises infrastructure to virtual service provider data-steward owned and operated off-premises infrastructure; and 5) Balance shared solutions with the National Institute of Science and Technology (NIST) Cybersecurity and Risk Management frameworks, and the System Security Engineering Guidelines. As yet unallocated federal funding would support research, development, the timely application of National-Tribal critical infrastructure protection, and critical infrastructure Cyber disruption response and recovery with extraordinary mutual benefits for the foreseeable future. The Critical Infrastructure Insecurity Problem: Rapid modernization and expansive connectivity are due to advances in Information and Communications Technologies that have sweeping cyber impact across all critical infrastructure sectors. Supervisory Control and Data Acquisition and Industrial Control Systems are particularly impacted as systems long separated from the Internet are now being connected and computerized. Virtualization and mobility create a Data Everywhere-User Anywhere paradigm that has evaporated the enterprise network perimeter. There are multi-front technological challenges at play, where long depended on technologies simply don't scale to current needs resulting in a digital dichotomy of competing old and new standards. New standards-based technologies scale but are not as well-known or as widely deployed, which leaves decision makers, stakeholders, and the workforce in a quandary, caught mid-stream between the technological past and the virtual future. Rapid and expansive cyber threat accompanies disruptive change in connectivity and computational dependencies. A lack of action will exacerbate the problem if new technologies roll out without baked-in security design. The Risk: If National-Tribal CIP collaboration to design in security is not done, then an ongoing state of insufficient bolt-on security and elevated threat exposure will remain for years to come.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2003

Methodology for Generating Conflict Scenarios by Time Shifting Recorded Traffic Data

A methodology is presented for generating conflict scenarios that can be used as test cases to estimate the operational performance of a conflict probe. Recorded air traffic data is time shifted to create traffic scenarios featuring conflicts with characteristic properties similar to those encountered in typical air traffic operations. First, a reference set of conflicts is obtained from trajectories that are computed using birth points and nominal flight plans extracted from recorded traffic data. Distributions are obtained for several primary properties (e.g., encounter angle) that are most likely to affect the performance of a conflict probe. A genetic algorithm is then utilized to determine the values of time shifts for the recorded track data so that the primary properties of conflicts generated by the time shifted data match those of the reference set. This methodology is successfully demonstrated using recorded traffic data for the Memphis Air Route Traffic Control Center; a key result is that the required time shifts are less than 5 min for 99% of the tracks. It is also observed that close matching of the primary properties used in this study additionally provides a good match for some other secondary properties.

Paglione, Mike↗