skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: STATISTICAL ANALYSIS OF HERITAGE DATA OF 9Cr-STEELS, USING A ROBUST, OPEN-SOURCE, DATA ANALYTICS DESIGN APPROACH

Authors:
;
Publication Date:
Research Org.:
National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). In-house Research
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1400385
Report Number(s):
NETL-PUB-20451
Resource Type:
Conference
Resource Relation:
Conference: Amit Kumar Verma, Mohamed Elsaeiti, Laura Bruckman, Roger H French, Jennifer L W Carter, Vyacheslav Romanov, Jeffrey A. Hawk (2016) "Statistical Analysis of Heritage Data of 9Cr-steels, Using a Robust, Open-source, Data Analytics Design Approach" Presented at MS&T16 Technical Meeting and Exhibition. MS&T Partner Societies, Salt Lake City, UT, October 2016. DOI: 0-87339-764-9
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 36 MATERIALS SCIENCE; 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; data analytics; 9Cr steel; creep; fatigue; Hadoop; NoSQL database; uncertainty quantification

Citation Formats

Romanov, Vyacheslav, and Hawk, Jeffrey A. STATISTICAL ANALYSIS OF HERITAGE DATA OF 9Cr-STEELS, USING A ROBUST, OPEN-SOURCE, DATA ANALYTICS DESIGN APPROACH. United States: N. p., 2016. Web.
Romanov, Vyacheslav, & Hawk, Jeffrey A. STATISTICAL ANALYSIS OF HERITAGE DATA OF 9Cr-STEELS, USING A ROBUST, OPEN-SOURCE, DATA ANALYTICS DESIGN APPROACH. United States.
Romanov, Vyacheslav, and Hawk, Jeffrey A. 2016. "STATISTICAL ANALYSIS OF HERITAGE DATA OF 9Cr-STEELS, USING A ROBUST, OPEN-SOURCE, DATA ANALYTICS DESIGN APPROACH". United States. doi:. https://www.osti.gov/servlets/purl/1400385.
@article{osti_1400385,
title = {STATISTICAL ANALYSIS OF HERITAGE DATA OF 9Cr-STEELS, USING A ROBUST, OPEN-SOURCE, DATA ANALYTICS DESIGN APPROACH},
author = {Romanov, Vyacheslav and Hawk, Jeffrey A.},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month =
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of thismore » integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings. Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725. Copyright This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.« less
  • Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, wemore » propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components i) GeoData Cloud a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.« less
  • Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve,more » a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.« less