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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Reservoir Sediment Management and Monitoring Database

Overview This dataset compiles dam sediment management and monitoring information from surveys, case studies, and journal articles. Additionally, features described by the National Inventory of Dams (i.e., presence of sluice gates) are included to indicate known infrastructure features that may address sediment releases. The location and description of records from downstream monitoring gages are catalogued in order to help with tracking conditions over time (e.g., before and after management actions, as operations change, etc.). The data help address national scale understanding of challenges and solutions related to the accumulation of sediment behind a dam as well as downstream passage. Sediment trapping causes problems as it reduces storage capacity, disrupts dam and reservoir function, impedes access for recreation, alters water quality/habitat conditions, and contributes to riverbank and coastal erosion within the reservoir. Data compilation from a variety of sources is a first step towards assessing system-wide efficacy of management solutions. This dataset was developed under the Water Power Technologies Office funded effort which began as a Seedling on Reservoir Sedimentation Data, and was supported by the Reservoir Sedimentation Modeling Framework and Data Analysis project. These projects have addressed challenges in describing sediment transport, trapping, and management at dams throughout the US. Methodology An outer join on dams/reservoirs with surveys and survey reports (documented in the RESSED database, USBR or USACE databases, project websites, etc.) with the National Inventory of Dams, based on the NIDID to determine dams with documented management and/or sluice gates. Additional dams with documented management activity were identified through review of technical articles from the past 25 years in Journal of Hydrology, Journal of Water Resources Planning and Management, Geomorphology, Journal of Hydraulic Engineering, Water, Journal of Cleaner Production, International Journal of Sediment Research, Nature Scientific Reports, Earth Surface Processes and Landforms, and Environmental Science and Pollution Research. Individual records were created for each survey or management activity documented. To evaluate downstream sediment monitoring records, the nhdPlusTools and dataRetrieval packages in R were used to find gages within 10km of each dam in the management database. Length of record and location of matched gages were retrieved for those parameters relevant to sediment concentration or total sediment discharge.

Hansen, Carly [ORNL] (ORCID:0000000193280838)↗
DOE OSTI2025

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Collar Properties and Measurement Time Confer Minimal Bias Overall on Annual Soil Respiration Estimates in a Global Database

Abstract Measuring the soil‐to‐atmosphere carbon dioxide (CO 2 ) flux (soil respiration, R S ) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual R S . Using a newly updated global R S database (SRDB‐V5), we investigated the annual R S bias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual R S was negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces R S . Annual R S was also negatively related with collar height and collar coverage area, perhaps because uniform head‐space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of ~2% to 10% of mean R S ). We found no correlation of measurement duration or measurement frequency with annual R S . These findings suggest that variation in R S methodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual R S measurements provide a reliable resource for synthesis studies, global annual R S modeling, and investigation of how soil carbon responds to climate change.

Jian, Jinshi↗
DOE OSTI2015

Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint]

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Update on NEA-TDB Database Development and Database Formatting Tool

This progress report (Level 4 Milestone Number M4SF-21LL010302062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline International Collaborations Activity Number SF-21LL01030206. The activity is focused on our long-term commitment of engaging our partners in international nuclear waste repository research. This includes participation in the Nuclear Energy Agency Thermochemical Database (NEA TDB) Project (Cindy Atkins-Duffin) and development of methodologies for integrating US and international thermodynamic databases for use in SFWST Generic Disposal System Assessment (GDSA) efforts. LLNL is supporting the overall objectives of the Disposal Research (DR) Crystalline International Collaborations Research and Development (R&D) control account. The objective of this control account is to advance our understanding of long-term disposal of spent fuel in crystalline rocks (including both granitic and metamorphic rocks) and to develop necessary experimental and computational capabilities to evaluate various disposal concepts in such media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Visualizing the Connections in the EXFOR Database

The EXFOR database contains many datasets (over 6160) in which the measured values are reaction combinations, which means that they are not an absolute measurement of an experimental quantity. Rather, they are ratios of quantities, sums of quantities, or some other mathematical relation of experimental quantities. These reaction combinations couple large numbers of data sets together in non-trivial ways. Here a visualization is presented of the coupled data used to derive cross material covariances for the COMMARA-3 library. Links are provided to other larger visualizations on the NNDC website.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

A comparison of different database technologies for the CMS AsyncStageOut transfer database

AsyncStageOut (ASO) is the component of the CMS distributed data analysis system (CRAB) that manages users transfers in a centrally controlled way using the File Transfer System (FTS3) at CERN. It addresses a major weakness of the previous, decentralized model, namely that the transfer of the user’s output data to a single remote site was part of the job execution, resulting in inefficient use of job slots and an unacceptable failure rate. Currently ASO manages up to 600k files of various sizes per day from more than 500 users per month, spread over more than 100 sites. ASO uses a NoSQL database (CouchDB) as internal bookkeeping and as way to communicate with other CRAB components. Since ASO/CRAB were put in production in 2014, the number of transfers constantly increased up to a point where the pressure to the central CouchDB instance became critical, creating new challenges for the system scalability, performance, and monitoring. This forced a re-engineering of the ASO application to increase its scalability and lowering its operational effort. In this contribution we present a comparison of the performance of the current NoSQL implementation and a new SQL implementation, and how their different strengths and features influenced the design choices and operational experience. We also discuss other architectural changes introduced in the system to handle the increasing load and latency in delivering output to the user.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

M4FT-16LL080302052-Update to Thermodynamic Database Development and Sorption Database Integration

This progress report (Level 4 Milestone Number M4FT-16LL080302052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Disposal R&D Work Package Number FT-16LL08030205. The focus of this research is the thermodynamic modeling of Engineered Barrier System (EBS) materials and properties and development of thermodynamic databases and models to evaluate the stability of EBS materials and their interactions with fluids at various physico-chemical conditions relevant to subsurface repository environments. The development and implementation of equilibrium thermodynamic models are intended to describe chemical and physical processes such as solubility, sorption, and diffusion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

HTGR Validation: NEUP Survey and Database Database Development for HTGR Thermal-Fluid Experiments

This set of slides is aimed to improve access to the High-Temperature Gas-cooled Reactor (HTGR) validation data and optimize the return on the significant investment made by DOE. Supported by the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program. Slides include information from FY2009 to FY2023, there are in total 35 DOE NEUP projects focusing on the thermal-fluid experiments related with High-Temperature Gas-cooled Reactor (HTGR), producing a large amount of high-quality validation data. NEUP Survey and Database Development for HTGR Thermal-Fluid Experiments is distributed at universities and has not been disseminated to the HTGR community. More collaborating with university PIs and refining the HTGR phenomena summary chart continuously is expected in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations

This work is one of a series of articles by the Nanomaterial Data Curation Initiative. Other articles in this series discuss data curation workflows, data completeness and quality, curator responsibilities, and metadata. Many groups within the broad nanotechnology field are already developing data repositories and tools driven by their individual organizational goals. Integrating these data across disciplines, and with other non-nanotechnology resources, can support multiple objectives by reusing the same information, and can serve as the impetus for novel scientific discoveries through deeper data analyses. Based on the results of a community-based survey of organizations that maintain nanomaterial repositories, this article discusses current data integration practices in nanoinformatics and mature fields such as genomics, as well as nanotechnology-specific challenges impacting data integration. Recommendations for achieving integration of existing operational nanotechnology resources, as based on results from a community-wide survey, are presented herein. Nanotechnology-specific data integration challenges, if effectively resolved, can foster the application and validation of nanotechnology within and across disciplines.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

AiiDA: automated interactive infrastructure and database for computational science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

Materials Science↗