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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

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NASA NTRS1975

Page composer to translate binary electrical data to optical form

Composer converts binary data to optical form for storage as hologram. Device consists of an array of deformable metal membranes controlled by MOSFET's. Device is fast, produces high contrast ratios, does not degrade with extended use, and can be addressed from diverse angles.

Bailey, G. A.↗
NASA NTRS2001

Apparatus and Method for Effecting Data Transfer Between Data Systems

An apparatus for effecting data transfer between data systems comprising a first transceiver and a second transceiver. The first transceiver has an input for receiving digital data from one of the data systems, an output for serially outputting digital data to one of the data systems, at least one transmitter for converting digital data received at the input into optical signals, and at least one receiver for receiving optical signals and serially converting the received optical signals to digital data for output to the data output. The second transceiver has an input for receiving digital data from another one of the data systems, an output for serially outputting digital data to the another one of the data systems, at least one transmitter for serially converting digital data received at the input of the second transceiver into optical signals, and at least one receiver for receiving optical signals and serially converting the received optical signals to digital data for output to the output of the second transceiver. The apparatus further comprises an optical link connecting the first and second transceivers. The optical link comprising a pair of optical fibers. One of the optical fibers optically links the transmitter of the first transceiver to the receiver of the second transceiver. The other optical fiber optically links the receiver of the first transceiver to the transmitter of the second transceiver.

Kirkpatrick, Joey V.↗
DOE OSTI2015

Cambio

Cambio opens data files from common gamma radiation detectors, displays a visual representation of it, and allows the user to edit the meta-data, as well as convert the data to a different file format.

Johnson, William↗
NASA NTRS1983

NIMBUS-7 ERB MATGEN Science Document

The ERB algorithms and computer software data flow used to convert sensor data into equivalent radiometric data are described in detail. The NIMBUS satellite location, orientation and sensor orientation algorithms are given. The computer housekeeping and data flow and sensor/data status algorithms are also given.

Soule, H. V.↗
NASA NTRS1978

Patient Monitoring

In photo above, the electrocardiogram of a hospitalized patient is being transmitted by telemetry. Widely employed in space operations, telemetry is a process wherein instrument data is converted to electrical signals and sent to a receiver where the signals are reconverted to usable information. In this instance, heart readings are picked up by the electrode attached to the patient's body and delivered by wire to the small box shown, which is a telemetry transmitter. The signals are relayed wirelessly to the console in the background, which converts them to EKG data. The data is displayed visually and recorded on a printout; at the same time, it is transmitted to a central control station (upper photo) where a nurse can monitor the condition of several patients simultaneously. The Patient Monitoring System was developed by SCI Systems, Inc., Huntsville, Alabama, in conjunction with Abbott Medical Electronics, Houston, Texas. In developing the system, SCI drew upon its extensive experience as a NASA contractor. The company applied telemetry technology developed for the Saturn launch vehicle and the Apollo spacecraft; instrumentation technology developed for heart, blood pressure and sleep monitoring of astronauts aboard NASA's Skylab long duration space station; and communications technology developed for the Space Shuttle.

Source record↗
NASA NTRS2008

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

Hegde, Mahabaleshwara↗
NASA NTRS2012

Trends in Surface Temperature at High Latitudes

The earliest signal of a climate change is expected to be found in the polar regions where warming is expected to be amplified on account of ice-albedo feedbacks associated with the high reflectivity of snow and ice. Because of general inaccessibility, there is a general paucity of in situ data and hence the need to use satellite data to observe the large-scale variability and trends in surface temperature in the region. Among the most important sensors for monitoring surface temperature has been the Advanced Very High Resolution Radiometer (AVHRR) which was first launched in 1978 and has provided continuous thermal infrared data since 1981. The top of the atmosphere data are converted to surface temperature data through various schemes that accounts for the unique atmospheric and surface conditions in the polar regions. Among the highest source of error in the data is cloud masking which is made more difficult in the polar region because of similar Signatures of clouds and snow lice covered areas. The availability of many more channels in the Moderate Resolution Imaging Spectroradiometer (MODIS) launched on board Terra satellite in December 1999 and on board Aqua in May 2002 (e.g., 36 visible and infrared channels compared to 5 for AVHRR) made it possible to minimize the error. Further capabilities were introduced with the Advanced Microwave Scanning Radiometer (AMSR) which has the appropriate frequency channels for the retrieval of sea surface temperature (SST). The results of analysis of the data show an amplified warming in the Arctic region, compared with global warming. The spatial distribution of warming is, however, not uniform and during the last 3 decades, positive temperature anomalies have been most pronounced in North America, Greenland and the Arctic basin. Some regions of the Arctic such as Siberia and the Bering Sea surprisingly show moderate cooling but this may be because these regions were anomalously warm in the 1980s when the satellite record started. Also, the SST in the Arctic basin is observed to be anomalously high in 2007 when the perennial ice cover declined dramatically to its lowest extent. In the Antarctic, surface temperature trends are much more moderate with the most positive trends occurring in the Antarctic Peninsula and parts of Western Antarctica while some cooling are observed in the Antarctic Plateau and the Ross Sea. The trends in SST in the region is similar to global averages but precipitation from more evaporation may have a key role in the spatial distribution of surface temperature in the ice covered region

Comiso, Josefino C.↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

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NASA NTRS1974

S-193 scatterometer transfer function analysis for data processing

A mathematical model for converting raw data measurements of the S-193 scatterometer into processed values of radar scattering coefficient is presented. The argument is based on an approximation derived from the Radar Equation and actual operating principles of the S-193 Scatterometer hardware. Possible error sources are inaccuracies in transmitted wavelength, range, antenna illumination integrals, and the instrument itself. The dominant source of error in the calculation of scattering coefficent is accuracy of the range. All other ractors with the possible exception of illumination integral are not considered to cause significant error in the calculation of scattering coefficient.

Johnson, L.↗
DOE OSTI2016

Most Likely Extreme Response Analysis of an Ellipsoid Float Wave Energy Converter

Input data and heave results (unsteady RANS-VOF overset simulations performed in Star-CCM+) for a float with an ellipsoid geometry. Five extreme sea states were considered, as detailed in the conference paper "Application of the Most Likely Extreme Response Method for Wave Energy Converters" by Quon et al (see resource below). These sea states were extrapolated from conditions near Humboldt Bay, California. Focused waves were generated using the MLER module of the Wave Design Response Toolbox (WDRT) and specified at the inlet boundary conditions. The device was constrained to heave only and a PTO was not modeled.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

High speed, real-time, camera bandwidth converter

Image data from a CMOS sensor with 10 bit resolution is reformatted in real time to allow the data to stream through communications equipment that is designed to transport data with 8 bit resolution. The incoming image data has 10 bit resolution. The communication equipment can transport image data with 8 bit resolution. Image data with 10 bit resolution is transmitted in real-time, without a frame delay, through the communication equipment by reformatting the image data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

SU-G-BRA-05: Application of a Feature-Based Tracking Algorithm to KV X-Ray Fluoroscopic Images Toward Marker-Less Real-Time Tumor Tracking

Purpose: To detect target position on kV X-ray fluoroscopic images using a feature-based tracking algorithm, Accelerated-KAZE (AKAZE), for markerless real-time tumor tracking (RTTT). Methods: Twelve lung cancer patients treated with RTTT on the Vero4DRT (Mitsubishi Heavy Industries, Japan, and Brainlab AG, Feldkirchen, Germany) were enrolled in this study. Respiratory tumor movement was greater than 10 mm. Three to five fiducial markers were implanted around the lung tumor transbronchially for each patient. Before beam delivery, external infrared (IR) markers and the fiducial markers were monitored for 20 to 40 s with the IR camera every 16.7 ms and with an orthogonal kV x-ray imaging subsystem every 80 or 160 ms, respectively. Target positions derived from the fiducial markers were determined on the orthogonal kV x-ray images, which were used as the ground truth in this study. Meanwhile, tracking positions were identified by AKAZE. Among a lot of feature points, AKAZE found high-quality feature points through sequential cross-check and distance-check between two consecutive images. Then, these 2D positional data were converted to the 3D positional data by a transformation matrix with a predefined calibration parameter. Root mean square error (RMSE) was calculated to evaluate the difference between 3D tracking and target positions. A total of 393 frames was analyzed. The experiment was conducted on a personal computer with 16 GB RAM, Intel Core i7-2600, 3.4 GHz processor. Results: Reproducibility of the target position during the same respiratory phase was 0.6 +/− 0.6 mm (range, 0.1–3.3 mm). Mean +/− SD of the RMSEs was 0.3 +/− 0.2 mm (range, 0.0–1.0 mm). Median computation time per frame was 179 msec (range, 154–247 msec). Conclusion: AKAZE successfully and quickly detected the target position on kV X-ray fluoroscopic images. Initial results indicate that the differences between 3D tracking and target position would be clinically acceptable.

60 APPLIED LIFE SCIENCES↗