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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Sherlock Data Warehouse

Technical Interchange Meeting 2 of 2, with two linked events. Overall Purpose of the Technical Interchange Meeting was to facilitate collaboration on NAS data analytics between FAA and NASA teams, leveraging the great tool developed by NASA, Sherlock Data Warehouse. Conducted the two TIM events, next step is for the leadership to meet and discuss potential future collaborations. Purpose for Technical Interchange Meeting #2 (TIM 2o2); NASA presented data science related use-cases and applications where they have leveraged the NASA Sherlock Data Warehouse to address aviation transportation problems, with priority on aviation safety.

Two TIMs↗
DOE OSTI2021

Field Data Analysis and Modeling of Drillstring Vibrations to Identify Inefficiency in Deep Geothermal Drilling

One challenge that hinders efficient drilling and causes downhole tool failures is severe drillstring vibrations. The objective of this paper is to identify the root cause of drillstring vibrations in deep geothermal wells and investigate their effect on drilling performance using data analytics and vibration modeling. A near-bit sub was utilized to collect vibration data, where the burst data was used to obtain the drillstring torsional natural frequency. The data showed that the highest lateral acceleration and stick-slip severities (SSS) occur at the higher mechanical specific energy (MSE) range, while the low vibration levels were encountered in the optimum range of the MSE curve. The elevated SSS levels consistently occur at the low to mid-range of applied RPM and the highest sonic velocity. Additionally, the lateral acceleration levels are higher at the low to mid-range of the applied RPM. The effect of drillstring vibration on drilling performance was recognized by the presented MSE analysis.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗
DOE OSTI2024

A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build. Dataset A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process has arisen under UT-Battelle, LLC’s Prime Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE) to manage and operate the Oak Ridge National Laboratory. UT-Battelle, LLC will not assert any rights under United States law or under the Prime Contract it has in the dataset against any user of the dataset, including any copyrights or patent rights. UT-Battelle, LLC requests that attribution to the dataset is provided as academically appropriate.

42 ENGINEERING↗
NASA NTRS1980

Approach to Sounding on the VAS Processor

The key features of the sounding software laboratory being installed on the VAS Processor at NASA/GSFC are outlined. Emphasis is on the support data and personal guidance that a meteorological researcher must provide to attune a physically modeled VAS sounding to his experiment. The fundamental aim of the sounding-support effort is to provide a system which makes use of: radiation transfer models based upon laboratory data, analytic inversion schemes, human guidance for quality control, statistically conditioned retrieval methods, and current ancillary data.

Chesters, D.↗
NASA NTRS2007

Revalidation of the Volatile Organic Analyzer Following a Major On-Orbit Maintenance Activity

The Volatile Organic Analyzer (VOA) contributes to the assessment of air quality aboard the International Space Station (ISS) by identifying and quantifying target airborne volatile organic contaminants in the module air. This on-orbit contaminant monitoring capability becomes particularly important during an air quality degradation event such as a system leak. During several ISS air quality degradations, the VOA has generated near real-time data that was used to make decisions or to better understand the contingency. The VOA was operational from January 2002 through June 2003, during which time it was validated by comparing VOA data to simultaneously acquired grab sample containers (GSCs). In January 2003, one of the two analytical channels of the VOA was shutdown because of a component failure, but a redundant channel continued to supply the necessary analytical data. In June 2003, the sole remaining channel was deactivated. Initial assessments of the channel shutdowns pointed to failed fuses or heaters, but neither was considered repairable on orbit. In 2005, it was determined that failed fuses could be replaced on orbit and the crew conducted a diagnostic procedure to identify the failed component. The crew discovered that both channels incurred failed fuses, which lead to a subsequent on orbit maintenance activity and return of the VOA to operational status in December 2005. The VOA has been providing data on the ISS atmosphere since its reactivation in 2005 and this paper will present the VOA data collected during 2006. Special emphasis will be placed upon the revalidation of the repaired VOA using GSCs as well as a summary of the diagnostic and repair procedures.

Limero, Thomas↗
DOE OSTI2021

Large-Scale Trajectory Analysis via Feature Vectors

The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.

58 GEOSCIENCES↗
DOE OSTI2025

Data for Machado-Silva et al. (2024), "Short-Term Groundwater Level Fluctuations Drive Subsurface Redox Variability"

This dataset contains the analytical data reported in Machado-Silva et al. (2024) as part of the COMPASS-FME project, which seeks to advance a scalable, predictive understanding of the fundamental biogeochemical processes, ecological structure, and ecosystem dynamics that distinguish coastal terrestrial-aquatic interfaces from the purely terrestrial or aquatic systems to which they are coupled. The dataset consists of water quality parameters as well as redox potential, water content, and electrical conductivity. These data were collected in 2022 in Crane Creek (CRC), Portage River (PTR), and Old Woman Creek (OWC). Each of these sites included uplands (UP), transitions (TR), wetland-transition edge (WTE), and wetland (W) zones. The sites represent replicates of the Lake Erie terrestrial-aquatic interface under fluctuating water levels and are located in well-preserved areas with natural or restored marsh and forest cover.This dataset consists of a single data file (Machado_Silva_et_al_2024_EST_data.csv) that is in comma-separated value (CSV) format. No special software is required to read it.This dataset uses the ESS-DIVE Hydrologic Monitoring Reporting Format 1.0.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data-centric approach emphasizes leveraging sensor data available throughout the production process to optimize performance. Integration of extensive data analysis provides opportunities for improving precision, reducing waste, and enhancing the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes a comprehensive description of the deposition process, process parameters, welding characteristics and acoustic data collected in-situ, and X-Ray Computed Tomography data of the build.

42 ENGINEERING↗
DOE OSTI2021

AI-Based Integrated Modeling and Observational Framework for Improving Seasonal to Decadal Prediction of Terrestrial Ecohydrological Extremes

Focal Areas: (1) Insight gleaned from complex data (both observed and simulated) using artificial intelligence(AI), big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Elucidating and predicting the dynamic evolution of water and land systems due to natural and energy-related forcings

Focal Area(s): 3. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI; & 1. Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: Interactions between water, land, and energy systems are complex and occur on a variety of scales, ranging from local to basinal to regional. Accurately predicting the behavior of ground water and surface water systems for 5-10 years and beyond requires an understanding of the current system and the ability to model both the natural system at scale and human-induced forcings related to energy and other activities. Artificial intelligence and machine learning (AI/ML) combined with modern compilation and integration efforts for U.S. groundwater and surface water systems present potential solutions to bolstering detailed physics-based models of these systems. Big data tied with ML and physics-based modeling can drive breakthroughs in understanding the earth system, but research is often impeded by data access (e.g., privacy issues), quality, formats, gaps, multi-source, multi-scale, integration, and spatiotemporal challenges. Effective integration of real data and simulated (synthetic) data that fill gaps is critical. Overcoming these complex data and model integration challenges will enable a transformational approach to acquiring enhanced understanding of environmental systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

International interlaboratory compilation of trace element concentrations in the CUP-2 uranium ore concentrate standard

Herein, a nuclear forensics investigation involving a uranium ore concentrate relies on accurate and precise analysis of impurities. Analytical data defensibility requires the use of reference materials as part of quality control. This study presents a compilation of trace element concentration results of the CUP-2 Uranium Ore Concentrate Standard measured by 11 different laboratories. The laboratories employed various dissolution methods, analytical preparation methods, and instrumental platforms. The data presented here contain concentrations of 66 impurities with up to 138 individual data points for each impurity. Consensus values have been assigned to each impurity following a statistical analysis of the data set.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2022

Streaming Statistics

In the context of a larger effort for in situ data analytics, there is a need to calculate basic statistics metrics (e.g., count, mean, median) online as new data points become available. Originally, the code for such online, or in other words streaming, statistics was part of the TALASS (Topological Analysis of Large- Scale Simulations) library. We isolated the relevant code and created a standalone library from it called Streaming Statistics. We also added an ability to serialize and deserialize the statistics objects so that the library can be used in distributed, task-based processing. To use the Streaming Statistics library, the user chooses a statistic, constructs an object for it, and then "adds" values to it, which means the statistic is augmented.

Shudler, Sergei↗
DOE OSTI2021

Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory

Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.

54 ENVIRONMENTAL SCIENCES↗