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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
DOE OSTI2023

Secure Data Logging and Processing with Blockchain and Machine Learning (Final Report)

Secure Data Logging and Processing with Blockchain and Machine Learning (ML) research is focused on the development of a platform to securely log and process sensor data in fossil power plants. The platform integrates two emerging technologies, blockchain and ML, and incorporates several innovative mechanisms to ensure the integrity, reliability, and resiliency of power systems. The goal is to protect the power plant from various cyberattacks such as false data injection and denial of service attacks using these technologies. The research goal was enabled by the following Research Project Objectives: 1) Secure authentication and identity verification of sensor nodes, actuators, and other equipment within a network. 2) Development of mechanisms that ensure only data sent by legitimate sensors are accepted and stored in the data repository. 3) Development of data aggregation methodologies using ML / Deep Learning (DL) algorithms to minimize noise / faulty data. 4) Implementation of the blockchain technologies to provide data security using secured IOTA framework & nodes.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗
DOE OSTI2025

MCPC Friction Stir Welding (FSW) Process Data

Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.

316 Stainless Steel↗
DOE OSTI2024

Utah FORGE: Injection and Production Test results and Reports from August 2024

This dataset includes results and reports from the injection/production test for wells 16A(78)-32 and 16B(78)-32 in August, 2024 at Utah FORGE. Materials include injection, post stimulation production, flow rate, gamma, temperature and pressure survey results. For the respective wells, injection and production profile results and interpretations are provided in .xlsx and .las file formats. Additionally, for both wells, preliminary and final reports are included and provide logging procedures and visualizations of the results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

EGS Collab Experiment 3: 4100 Tensile Stimulation and Thermal Circulation Testing

These data and test descriptions are from a set of primarily tensile hydraulic-fracture stimulations in wells E2-TC and E2-TU and a subsequent chilled water circulation test conducted by injecting in well E2-TU on the 4100 level of the Sandford Underground Research Facility (SURF). Stimulations were carried out between April and May of 2022. The thermal circulation test ran semi-continuously from May 19 through August 26, 2022, though chilled water injection began on June 3. More information about the test, rationale, and processing of data is available on the EGS Collab project page, which is linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Idaho National Laboratory Data Acquisition And Processing System

The INLDAS data acquisition and processing system is designed to develop prototype measurements and real-time processing techniques. The INLDAS hardware and firmware currently consists of National Instruments • NI-DAQmx 14.1 • cDAQ-9184 • 9205 • 9211 INLDAS can perform of standard data acquisition functions as well as novel functions and real-time processing algorithms. There are 3 acquisition modes to choose from: • Continuous mode runs when the user hits start, processing data and logging it to file • SWTrigger mode waits for one or more predefined triggers before acquiring data. It will buffer data as well, so it can record data that happened shortly before the trigger • Wakeup mode waits on predefined timers. When a time goes off, it acquires a preset amount of data There are also 3 Data Processing Modes • Normal mode does no processing besides the rolling average • FFT Mode Performs an FFT on incoming data every time the time window has elapsed • STFFT mode

Smith, JamesA↗
DOE OSTI2026

On Road Testing Data

This dataset provides the following on road testing data: - Videos - In-vehicle dash camera videos during different testing scenarios. - Signal controller data - NTCIP log data and processed signal timing data from the corresponding signal controllers - Vehicle data - Vehicle data recorded during the testing, including GNSS, communication, CAN signals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Utah FORGE: Well 16A(78)-32 and 16B(78)-32 Stimulation Production Logging Tests

These are production logging tests (PLT) results from Halliburton collected during the nine hour circulation test performed at Utah FORGE on April 27th, 2024. The circulation test involved injecting into well 16A(78)-32 and producing out of well 16B(78)-32) and was carried out after the fracturing of both of those wells. Data attached here includes raw and processed PLT logs from both wells, as well as technical reports summarizing the data collection and results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

LANL HPC Data Center Monitoring (Summer 2022 Talk) [Slides]

HPC Monitoring is tasked with the understanding of everything that happens in the Data Center. They get to that understanding by: collecting logs and metrics, processing data, analyzing data, displaying information, and receiving alerts on events. Their ultimate (and probably unreachable) goal is a holistic view of the Data Center and an understanding of how everything is correlated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Utah FORGE: Updated FMI Fracture Log from Well 16A(78)-32

This dataset consists of an Excel spreadsheet detailing the fracture picks from a reinterpretation of the formation micro-imaging (FMI) log from Utah FORGE well 16A(78)-32. The provided information details fracture location, geometry, and type. Also included here is a link to the original raw and processed FMI logs, as well as other data from the 2021 well logging.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

HERO WEC V1 Upgrade - 2023 Laboratory Testing (processed data)

The following submission includes processed laboratory data from NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of MATLAB workspaces. This dataset was created using NREL's Large Amplitude Motion Platform (LAMP) and collected between August and September 2023. Included with this submission is a test log of all the processed data "HERO WEC LAMP test run log.xlsx" so that the user can easily find the data of interest. Additionally, more detailed descriptions of the type of data and how it was processed, or calculated, can be found in the document titled "Lamp Data Description.docx". The MATLAB workspaces can be visualized using the file "LAMP_Data_Viewer_Ver2.m/mlx". The user simply needs to upload the workspace of interest and run the file "LAMP_Data_Viewer_Ver2.m/mlx". Both the .m and .mlx file format has been provided depending on the user's preference. The MATLAB workspaces have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water sine wave profile (not run in drivetrain configuration) - Heave - Heave only sine wave profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) For those interested in the raw, unprocessed, data the authors have created a separate submission, linked below. This submission includes the raw TDMS files and associated files necessary to translate the data into either python or MATLAB formats. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗
DOE OSTI2022

April 2020 Darshan counters from the Summit supercomputer

This dataset is the Darshan counters collected from the Summit supercomputer in a month of April 2020. 1. Description of methods used for collection/generation of data: Job submitted on Summit HPC system when completed successfully and has made I/O calls (captured by Darshan tool) writes a Darshan log file on alpine filesystem. One job can have multiple `jsrun` commands and Darshan will generate separate logs each log corresponding to an `jsrun` command, so a job can have one or more Darshan logs associated with it. 2. Methods for processing the data: To process the data, we first use `darshan-util` tool to parse the Darshan logs. Then we restructure the logs and merge data from multiple Darshan logs if they belong to the same Summit job.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Utah FORGE: Well 16B(78)-32 Drilling Data

This drilling data for Utah FORGE well 16B(78)-32 include a well survey, core summary, mud and mud temperature logs, daily reports of the drilling process, and additional data from the Pason oil and gas company. Well 16B(78)-32 serves as the production well for reservoir creation, fluid circulation, and demonstration of heat extraction for the FORGE project. It has been drilled as a doublet approximately 300 feet parallel to and above the injection well 16A(78)-32. The proposed total depth was 10,658 feet, which was exceeded. Spudding began on April 26th, 2023. As of June 20th, 2023, the total depth measured 10,947 feet and the vertical depth measured 8,357 feet. Drilling included the trial use of insulated drill pipe (IDP) from Eavor Technologies, which was considered a success. IDP restricts counter-current heat transfer between drilling fluid inside the drill string and hotter returning fluid in the annulus, thereby ensuring the bottomhole assembly (BHA) remains submerged in cool fluid. Eavor rented 350 joints of IDP to FORGE which were run in two consecutive BHAs at the well. The results of this trial are included here.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology

Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.

Klasky, Hilda↗
DOE OSTI2022

NREL Fleet Analysis Support Through Technology Integration Collaboration

This study leveraged the partnership between the United States Department of Energy's (DOE) Clean Cities Coalition Network and the Association for the Work Truck Industry (NTEA) to launch a vehicle and fleet analysis project that assisted fleets in identifying opportunities to save energy, improve efficiency, reduce costs, and meet environmental goals via short term data logging and analysis. The National Renewable Energy Laboratory (NREL) sought to establish a process that included initial data acquisition, provided data storage, and developed analytic methods to inform fleets of areas of opportunity based on approximately 30 days of in use vehicle performance data. However, long-term the project will require ongoing funding to fully develop and maintain the data sharing platform and to produce more complex analysis.

33 ADVANCED PROPULSION SYSTEMS↗