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Title: Sustaining knowledge in the neutron generator community and benchmarking study.

Abstract

In 2004, the Responsive Neutron Generator Product Deployment department embarked upon a partnership with the Systems Engineering and Analysis knowledge management (KM) team to develop knowledge management systems for the neutron generator (NG) community. This partnership continues today. The most recent challenge was to improve the current KM system (KMS) development approach by identifying a process that will allow staff members to capture knowledge as they learn it. This 'as-you-go' approach will lead to a sustainable KM process for the NG community. This paper presents a historical overview of NG KMSs, as well as research conducted to move toward sustainable KM.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
933224
Report Number(s):
SAND2008-1777
TRN: US0803745
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
07 ISOTOPES AND RADIATION SOURCES; KNOWLEDGE MANAGEMENT; NEUTRON GENERATORS; RESEARCH PROGRAMS; HISTORICAL ASPECTS; Neutron generators.; Knowledge management.

Citation Formats

Barrentine, Tameka C., Kennedy, Bryan C., Saba, Anthony W., Turgeon, Jennifer L., Schneider, Julia Teresa, Stubblefield, William Anthony, and Baldonado, Esther. Sustaining knowledge in the neutron generator community and benchmarking study.. United States: N. p., 2008. Web. doi:10.2172/933224.
Barrentine, Tameka C., Kennedy, Bryan C., Saba, Anthony W., Turgeon, Jennifer L., Schneider, Julia Teresa, Stubblefield, William Anthony, & Baldonado, Esther. Sustaining knowledge in the neutron generator community and benchmarking study.. United States. doi:10.2172/933224.
Barrentine, Tameka C., Kennedy, Bryan C., Saba, Anthony W., Turgeon, Jennifer L., Schneider, Julia Teresa, Stubblefield, William Anthony, and Baldonado, Esther. Sat . "Sustaining knowledge in the neutron generator community and benchmarking study.". United States. doi:10.2172/933224. https://www.osti.gov/servlets/purl/933224.
@article{osti_933224,
title = {Sustaining knowledge in the neutron generator community and benchmarking study.},
author = {Barrentine, Tameka C. and Kennedy, Bryan C. and Saba, Anthony W. and Turgeon, Jennifer L. and Schneider, Julia Teresa and Stubblefield, William Anthony and Baldonado, Esther},
abstractNote = {In 2004, the Responsive Neutron Generator Product Deployment department embarked upon a partnership with the Systems Engineering and Analysis knowledge management (KM) team to develop knowledge management systems for the neutron generator (NG) community. This partnership continues today. The most recent challenge was to improve the current KM system (KMS) development approach by identifying a process that will allow staff members to capture knowledge as they learn it. This 'as-you-go' approach will lead to a sustainable KM process for the NG community. This paper presents a historical overview of NG KMSs, as well as research conducted to move toward sustainable KM.},
doi = {10.2172/933224},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Mar 01 00:00:00 EST 2008},
month = {Sat Mar 01 00:00:00 EST 2008}
}

Technical Report:

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  • This report documents the second phase of work under the Sustainable Knowledge Management (SKM) project for the Neutron Generator organization at Sandia National Laboratories. Previous work under this project is documented in SAND2008-1777, Sustaining Knowledge in the Neutron Generator Community and Benchmarking Study. Knowledge management (KM) systems are necessary to preserve critical knowledge within organizations. A successful KM program should focus on people and the process for sharing, capturing, and applying knowledge. The Neutron Generator organization is developing KM systems to ensure knowledge is not lost. A benchmarking study involving site visits to outside industry plus additional resource research wasmore » conducted during this phase of the SKM project. The findings presented in this report are recommendations for making an SKM program successful. The recommendations are activities that promote sharing, capturing, and applying knowledge. The benchmarking effort, including the site visits to Toyota and Halliburton, provided valuable information on how the SEA KM team could incorporate a KM solution for not just the neutron generators (NG) community but the entire laboratory. The laboratory needs a KM program that allows members of the workforce to access, share, analyze, manage, and apply knowledge. KM activities, such as communities of practice (COP) and sharing best practices, provide a solution towards creating an enabling environment for KM. As more and more people leave organizations through retirement and job transfer, the need to preserve knowledge is essential. Creating an environment for the effective use of knowledge is vital to achieving the laboratory's mission.« less
  • The eddy current examination of steam generator tubes is a very demanding process. Challenges include: complex signal analysis, massive amount of data to be reviewed quickly with extreme precision and accuracy, shortages of data analysts during peak periods, and the desire to reduce examination costs. One method to address these challenges is by incorporating automation into the data analysis process. Specific advantages, which automated data analysis has the potential to provide, include the ability to analyze data more quickly, consistently and accurately than can be performed manually. Also, automated data analysis can potentially perform the data analysis function with significantlymore » smaller levels of analyst staffing. Despite the clear advantages that an automated data analysis system has the potential to provide, no automated system has been produced and qualified that can perform all of the functions that utility engineers demand. This report investigates the current status of automated data analysis, both at the commercial and developmental level. A summary of the various commercial and developmental data analysis systems is provided which includes the signal processing methodologies used and, where available, the performance data obtained for each system. Also, included in this report is input from seventeen research organizations regarding the actions required and obstacles to be overcome in order to bring automatic data analysis from the laboratory into the field environment. In order to provide assistance with ongoing and future research efforts in the automated data analysis arena, the most promising approaches to signal processing are described in this report. These approaches include: wavelet applications, pattern recognition, template matching, expert systems, artificial neural networks, fuzzy logic, case based reasoning and genetic algorithms. Utility engineers and NDE researchers can use this information to assist in developing automated data analysis systems in an efficient and cost effective manner, by gaining an understanding of those methods that have produced the most promising results to date, as well as by learning of those approaches that have not been successful.« less
  • A generalized theoretical model has been developed and numerically implemented to predict the thermal-hydraulic response of a U-tube steam generator during various transients such as steam line break, steam generator tube rupture, loss of feedwater, and primary side transients. A two-region model is used with a simple and fast running code to calculate essential state variables. Comparisons are made to the data of the Westinghouse Model Boiler 2 test for five types of transient. Good agreement is observed between the model predictions and the test data.