Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data enhancement”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rapid Detection of Anomalies in Battery Energy Storage System Data

Data analytics is pivotal in assessing the technical characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy To address the challenge, this paper presents a novel methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. We’ve collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities. These datasets serve dual roles: enabling comprehensive data exploration and analysis for understanding underlying challenges and method development, while also acting as a vital validation resource, demonstrating practical effectiveness. The proposed method detects anomalies and aids in their resolution, improving system performance characterization precision. It also reveals recurring data anomaly sources, offering insights for data collection and handling enhancement. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

Crawford, Aladsair J.↗
DOE OSTI2021

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Improved Estimates of Temporally Coherent Internal Tides and Energy Fluxes from Satellite Altimetry

Satellite altimetry has opened a surprising new avenue to observing internal tides in the open ocean. The tidal surface signatures are very small, a few cm at most, but in many areas they are robust, owing to averaging over many years. By employing a simplified two dimensional wave fitting to the surface elevations in combination with climatological hydrography to define the relation between the surface height and the current and pressure at depth, we may obtain rough estimates of internal tide energy fluxes. Initial results near Hawaii with Topex/Poseidon (T/P) data show good agreement with detailed 3D (three dimensional) numerical models, but the altimeter picture is somewhat blurred owing to the widely spaced T/P tracks. The resolution may be enhanced somewhat by using data from the ERS-1 (ESA (European Space Agency) Remote Sensing) and ERS-2 satellite altimeters. The ERS satellite tracks are much more closely spaced (0.72 deg longitude vs. 2.83 deg for T/P), but the tidal estimates are less accurate than those for T/P. All altimeter estimates are also severely affected by noise in regions of high mesoscale variability, and we have obtained some success in reducing this contamination by employing a prior correction for mesoscale variability based on ten day detailed sea surface height maps developed by Le Traon and colleagues. These improvements allow us to more clearly define the internal tide surface field and the corresponding energy fluxes. Results from throughout the global ocean will be presented.

Ray, Richard D.↗
NASA NTRS1986

Equatorial thermospheric measurements of temperatures and winds at Arequipa, Peru

Enhancement of FPI temperatures above quiet levels was observed a few hours after the start of magnetic activity. Magnitude of this enhancement is about 300 degrees. This is followed by relaxation to prestorm levels. Apparent average offset of RPI temperatures from mass spectrometer incoherent scatter radar by 100 to 200 degrees for relatively quiet times is also observed. There was a definite suggestion of a midnight thermal enhancement for April and August data, with a magnitude of about 100 degrees, seen in both 1983 and 1984 observations. There was a definite enhancement of 6300A surface brightness in the south as compared with other directions, probably connected to the tropical airglow arcs. Meridional winds were small (less than 25 m/s) throughout night. Indication of northward migration of the observed 6300A enhancement in the evening hours as observations approach local winter solstice. This is probably related to the observed poleward (to the south) meridional wind (of magnitude 50 m/s) in this period. Zonal component of winds always eastward, but speed approaches zero sooner near equinox than at summer solstice. Typical magnitude at peak is of order 100 to 150 m/s. Suggestion of zonal wind increase after twilight and recovery of 6300A emission for April data. Origin not clear but may be related to midnight thermal enhancement. Meridional wind virtually zero for equinox in 1983; shows evening flow towards winter hemisphere in early evening for solstice data. Suggestion of post-midnight surge in April 1984 data. No major effects associated with magnetic storm activity, but a suggestion of decrease in zonal component below nominal levels.

Meriwether, J. W., Jr.↗
NASA NTRS1995

Enhancements to the SSME transfer function modeling code

This report details the results of a one year effort by Ohio University to apply the transfer function modeling and analysis tools developed under NASA Grant NAG8-167 (Irwin, 1992), (Bartholomew, 1992) to attempt the generation of Space Shuttle Main Engine High Pressure Turbopump transfer functions from time domain data. In addition, new enhancements to the transfer function modeling codes which enhance the code functionality are presented, along with some ideas for improved modeling methods and future work. Section 2 contains a review of the analytical background used to generate transfer functions with the SSME transfer function modeling software. Section 2.1 presents the 'ratio method' developed for obtaining models of systems that are subject to single unmeasured excitation sources and have two or more measured output signals. Since most of the models developed during the investigation use the Eigensystem Realization Algorithm (ERA) for model generation, Section 2.2 presents an introduction of ERA, and Section 2.3 describes how it can be used to model spectral quantities. Section 2.4 details the Residue Identification Algorithm (RID) including the use of Constrained Least Squares (CLS) and Total Least Squares (TLS). Most of this information can be found in the report (and is repeated for convenience). Section 3 chronicles the effort of applying the SSME transfer function modeling codes to the a51p394.dat and a51p1294.dat time data files to generate transfer functions from the unmeasured input to the 129.4 degree sensor output. Included are transfer function modeling attempts using five methods. The first method is a direct application of the SSME codes to the data files and the second method uses the underlying trends in the spectral density estimates to form transfer function models with less clustering of poles and zeros than the models obtained by the direct method. In the third approach, the time data is low pass filtered prior to the modeling process in an effort to filter out high frequency characteristics. The fourth method removes the presumed system excitation and its harmonics in order to investigate the effects of the excitation on the modeling process. The fifth method is an attempt to apply constrained RID to obtain better transfer functions through more accurate modeling over certain frequency ranges. Section 4 presents some new C main files which were created to round out the functionality of the existing SSME transfer function modeling code. It is now possible to go from time data to transfer function models using only the C codes; it is not necessary to rely on external software. The new C main files and instructions for their use are included. Section 5 presents current and future enhancements to the XPLOT graphics program which was delivered with the initial software. Several new features which have been added to the program are detailed in the first part of this section. The remainder of Section 5 then lists some possible features which may be added in the future. Section 6 contains the conclusion section of this report. Section 6.1 is an overview of the work including a summary and observations relating to finding transfer functions with the SSME code. Section 6.2 contains information relating to future work on the project.

Irwin, R. Dennis↗
DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2022

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1979

Steady X-ray emission from MXB 1730-335 /the Rapid Burster/

SAS 3 observations of the Rapid Burster, MXB 1730-335, in three energy channels between 1.3 and 12 keV are used to examine temporary enhancements of the steady X-ray flux from that source following very energetic (long) Type II X-ray bursts. It is found that the average steady X-ray enhancement apparently reaches a maximum value corresponding to about 40% of the time-averaged burst flux, that no significant change in the shape of the X-ray spectrum of the enhancement is evident from the data, and that a certain minimum time interval is needed to obtain enhanced steady emission. The enhanced steady X-ray emission is shown to amount to an average steady contribution of about 5% of the time-averaged burst flux. It is concluded that the observed correlation of the onset and termination of the enhancement with the occurrence of Type II bursts strongly supports the earlier suggestion that the Type II bursts from the Rapid Burster are caused by instabilities in the accretion of matter onto a compact object.

Van Paradijs, J.↗
NASA NTRS1979

Remote sensing analysis of water quality in the San Francisco Bay-delta

Water quality parameters in the San Francisco Bay-delta area using remotely sensed data combined with in situ data are investigated. The parameters included suspended solids, chlorophyll, turbidity, and electrical conductivity; the ocean color scanner (OCS) data were acquired from a NASA U-2 aircraft, and water quality samples were obtained from boats. It was concluded that areas with high biological activity were clearly discernible on enhanced imagery from OCS data, and it was impossible to locate such areas on aerial photography taken with conventional or infrared sensitive color films.

Khorram, S.↗
NASA NTRS1990

Enforcing compatibility and constraint conditions and information retrieval at the design action

The design of complex entities is a multidisciplinary process involving several interacting groups and disciplines. There is a need to integrate the data in such environments to enhance the collaboration between these groups and to enforce compatibility between dependent data entities. This paper discusses the implementation of a workstation based CAD system that is integrated with a DBMS and an expert system, CLIPS, (both implemented on a mini computer) to provide such collaborative and compatibility enforcement capabilities. The current implementation allows for a three way link between the CAD system, the DBMS and CLIPS. The engineering design process associated with the design and fabrication of sheet metal housing for computers in a large computer manufacturing facility provides the basis for this prototype system.

Woodruff, George W.↗
NASA NTRS2007

Apollo 17 Lunar Sounder Data provide Insight into Aitken Crater's Subsurface Structure

In preparation for the forthcoming avalanche of data from Lunar Reconnaissance Orbiter (LRO), we conducted a pilot study to demonstrate integration of multiple geophysical data sets. We applied methods of data integration that are used by the commercial mineral exploration industry to enhance the value of historical data sets and to provide a roadmap for future efforts.

Cooper, Bonnie L.↗
NASA NTRS2017

GES DISC Greenhouse Gas Data Sets and Associated Services

NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes rich collections of data on atmospheric greenhouse gases from multiple missions. Hosted data include those from the Atmospheric Infrared Sounder (AIRS) mission (which has observed CO2, CH4, ozone, and water vapor since 2002); legacy water vapor and ozone retrievals from TIROS Operational Vertical Sounder (TOVS); and Upper Atmosphere Research Satellite (UARS) going back to the early 1980s. GES DISC also archives and supports data from seven projects of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program that have ozone and water vapor records. Greenhouse gases data from the A-Train satellite constellation is also available: (1) Aura-Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) ozone, nitrous oxide, and water vapor since 2004; (2) Greenhouse Gases Observing Satellite (GOSAT) CO2 observations since 2009 from the Atmospheric CO2 Observations from Space (ACOS) task; and (3) Orbiting Carbon Observatory-2 (OCO-2) CO2 data since 2014. The most recent related data set that the GES DISC archives is methane flux for North America, as part of NASAs Carbon Monitoring System (CMS) project. This dataset contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by GOSAT observations (Turner et al., 2015). Along with data stewardship, an important focus area of the GES DISC is to enhance the usability of its data and broaden its user base. Users have unrestricted access to a new user-friendly search interface, which includes many services such as variable subsetting, format conversion, quality screening, and quick browse. The majority of the GES DISC data sets are also accessible through Open-source Project for a Network Data Access Protocol (OPeNDAP) and Web Coverage Service (WCS). The latter two services provide more options for specialized subsetting, format conversion, and image viewing. Additional data exploration, data preview, and preliminary analysis capabilities are available via NASA Giovanni, which obviates the need forusers to download the data (Acker and Leptoukh, 2007). Giovanni provides a bridge between the data and science and has been very successful in extending GES DISC data to educational users and to users with limited resources.

data ordering↗
NASA NTRS2021

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scientific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns

ocean↗
NASA NTRS2015

The CEOS Atmospheric Composition Constellation: Enhancing the Value of Space-Based Observations

The Committee on Earth Observation Satellites (CEOS) coordinates civil space-borne observations of the Earth. Participating agencies strive to enhance international coordination and data exchange and to optimize societal benefit. In recent years, CEOS has collaborated closely with the Group on Earth Observations (GEO) in implementing the Global Earth Observing System of Systems (GEOSS) space-based objectives. The goal of the CEOS Atmospheric Composition Constellation (ACC) is to collect and deliver data to improve monitoring, assessment and predictive capabilities for changes in the ozone layer, air quality and climate forcing associated with changes in the environment through coordination of existing and future international space assets. A project to coordinate and enhance the science value of a future constellation of geostationary sensors measuring parameters relevant to air quality supports the forthcoming European Sentinel-4, Korean GEMS, and US TEMPO missions. Recommendations have been developed for harmonization to mutually improve data quality and facilitate widespread use of the data products.

Eckman, Richard↗
DOE OSTI2022

GeoThermalCloud: Machine Learning for Geothermal Resource Exploration

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

machine learning (ML), geothermal energy↗
DOE OSTI2025

HydroBio: Hydropower Capacity and Freshwater Biodiversity in Conterminous United States Sub-basins

This dataset summarizes existing and potential hydropower capacity and freshwater biodiversity at the sub-basin level throughout the conterminous United States (CONUS). It contains descriptive information regarding each sub-basin (e.g., 8-digit hydrologic unit code identifier, name, states, and size) along with sub-basin-level summaries of: 1) existing hydropower capacity (MW), 2) potential nominal non-powered dam (NPD) capacity (MW), 3) potential capacity of new stream reach development (NSD) (MW), and 4) freshwater biodiversity, including the total richness of fish, crayfish, and mussels and metrics that account for how rare and threatened those species tend to be. Hydropower data were obtained from Oak Ridge National Laboratory data resources (Existing Hydropower Assets, Non-Powered Dam Technical Potential, and New Stream Reach Development). Freshwater biodiversity data were obtained from NatureServe. Sub-basin characteristic information was obtained from the United States Geological Survey. Additionally, long data that provide lists of unique elements within each sub-basin for each constituent data resource (e.g., NatureServe, Existing Hydropower Assets) are provided to enhance dataset utility for users. The dataset provides, for the first time, a national-level assessment of existing and potential hydropower capacity in the context of freshwater biodiversity and is a valuable resource for stakeholders tasked with providing affordable, reliable energy to the American public while maintaining or enhancing invaluable freshwater resources. The dataset contains six data files in comma separated (*.csv) format that are within a zipped file.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
NASA NTRS2011

Apollo Lunar Sample Integration into Google Moon: A New Approach to Digitization

The Google Moon Apollo Lunar Sample Data Integration project is part of a larger, LASER-funded 4-year lunar rock photo restoration project by NASA s Acquisition and Curation Office [1]. The objective of this project is to enhance the Apollo mission data already available on Google Moon with information about the lunar samples collected during the Apollo missions. To this end, we have combined rock sample data from various sources, including Curation databases, mission documentation and lunar sample catalogs, with newly available digital photography of rock samples to create a user-friendly, interactive tool for learning about the Apollo Moon samples

Dawson, Melissa D.↗