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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 OSTI2011

NGNP Risk Management Database: A Model for Managing Risk

The Next Generation Nuclear Plant (NGNP) Risk Management System (RMS) is a database used to maintain the project risk register. The RMS also maps risk reduction activities to specific identified risks. Further functionality of the RMS includes mapping reactor suppliers Design Data Needs (DDNs) to risk reduction tasks and mapping Phenomena Identification Ranking Table (PIRTs) to associated risks. This document outlines the basic instructions on how to use the RMS. This document constitutes Revision 1 of the NGNP Risk Management Database: A Model for Managing Risk. It incorporates the latest enhancements to the RMS. The enhancements include six new custom views of risk data - Impact/Consequence, Tasks by Project Phase, Tasks by Status, Tasks by Project Phase/Status, Tasks by Impact/WBS, and Tasks by Phase/Impact/WBS.

42 ENGINEERING↗
DOE OSTI2011

PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables across different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.

Barnette, DanielW.↗
DOE OSTI2020

Database Management Module Framework for Dynamic Contingency Analysis and Visualization

The Dynamic Contingency Analysis Tool (DCAT) is being developed to mimic the response of generation dynamics, protection, and load components to extreme contingencies in power systems. Currently, DCAT generates a huge data set, with the analysis results stored in multiple subfolders using a great many files in different formats (power flow cases, time series, figures, spreadsheets). To help power system engineers thoroughly understand and analyze system behavior under many scenarios and contingencies, we developed a database management module (DBM) that enables future integration of DCAT with industry-grade Big Data analytical and interactive visualization platforms. In this paper, we demonstrate the DBM framework with interactive analytics and visualization for DCAT on the synthetic South Carolina 500-bus power system model using hurricane contingencies that capture possibilities for an extreme contingency. DCAT’s advanced data management and interactive visualization can help planning engineers to visually explore and analyze system behavior, all in one view.

Cascading, dynamic simulations, database managemen↗
DOE OSTI2016

Development of the ageing management database of PUSPATI TRIGA reactor

Since its first criticality in 1982, PUSPATI TRIGA Reactor (RTP) has been operated for more than 30 years. As RTP become older, ageing problems have been seen to be the prominent issues. In addressing the ageing issues, an Ageing Management (AgeM) database for managing related ageing matters was systematically developed. This paper presents the development of AgeM database taking into account all RTP major Systems, Structures and Components (SSCs) and ageing mechanism of these SSCs through the system surveillance program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Nuclear Material Management Database Development at the Nevada National Security Site

The Nuclear Material Management & Storage (NMM&S) program at the Nevada National Security Site (NNSS) have the responsibility for the life-cycle management of accountable nuclear materials (NM) in accordance with Department of Energy (DOE) Order 410.2, “Management of Nuclear Materials” and for ensuring workers are protected from exposure to nuclear materials stored outside of an authorized shipping package under DOE Manual 441.1-1. To ensure these responsibilities were addressed in an effective, concise, and secure manner the development of NMM&S databases was undertaken. Using the existing database server structure and data acquired from the official Material Control & Accountability (MC&A) accounting program development of two separate applications, one for O 410.2 inventory requirements (NMIA) and one for M441, were begun in September 2015. The databases were deployed onto the production server in January 2017. The inventory database allows for entering NM characterization data, production of O 410.2 reports, and the ability to retain history files. The M441 database screens NM to determine if it is subject to the manual's requirements, ability to pull in unclassified RadCon materials, and provide reports as required by NMM&S, NM owners, and headquarters programs. These two databases provide NMM&S the ability to better ensure safe and secure packaging, storage, stabilization, consolidation, or disposition of NM and achieve life-cycle management goals.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Database Manager for use with PLEXOS XML files [SWR-24-90]

This software is an API for create and modify XML that are used for Plexos. DISCLAIMER: PLEXOS is a registered trademark of Energy Exemplar Pty Ltd ("Owner"). Owner has no affiliation to or participation in this software. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC ("Alliance"). The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.

Sanchez Perez, Pedro Andres↗
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 OSTI2024

CONNECT Toolkit: Carbon Management Projects Database and Explorer

The Carbon Management Projects (CONNECT) Toolkit (exploratory visualization tool and database), developed by U.S. Department of Energy’s (DOE) Office of Fossil Energy and Carbon Management (FECM), aims to provide a one-stop access to authoritative information on a portfolio of federal research, development, and demonstration RD&D projects that have been publicly announced to advance carbon management technologies.

Sharma, Maneesh↗
DOE OSTI2011

Object-Oriented Database for Managing Building Modeling Components and Metadata: Preprint

Building simulation enables users to explore and evaluate multiple building designs. When tools for optimization, parametrics, and uncertainty analysis are combined with analysis engines, the sheer number of discrete simulation datasets makes it difficult to keep track of the inputs. The integrity of the input data is critical to designers, engineers, and researchers for code compliance,validation, and building commissioning long after the simulations are finished. This paper discusses an application that stores inputs needed for building energy modeling in a searchable, indexable, flexible, and scalable database to help address the problem of managing simulation input data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

AWS↗
DOE OSTI2018

Home energy management (HEM) database: A list with coded attributes of 308 devices commercially available in the US

Policymakers worldwide are currently discussing whether to include home energy management (HEM) products in their portfolio of technologies to reduce carbon emissions and improve grid reliability. However, very little data is available about these products. Here we present the results of an extensive review including 308 HEM products available on the US market in 2015–2016. We gathered these data from publicly available sources such as vendor websites, online marketplaces and other vendor documents. A coding guide was developed iteratively during the data collection and utilized to classify the devices. Each product was coded based on 96 distinct attributes, grouped into 11 categories: Identifying information, Product components, Hardware, Communication, Software, Information - feedback, Information - feedforward, Control, Utility interaction, Additional benefits and Usability. The codes describe product features and functionalities, user interaction and interoperability with other devices. A mix of binary attributes and more descriptive codes allow to sort and group data without losing important qualitative information. The information is stored in a large spreadsheet included with this article, along with an explanatory coding guide. This dataset is analyzed and described in a research article entitled “Categories and functionality of smart home technology for energy management” (Ford et al., 2017) [1].

Home automation↗
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↗