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Uncertainty Quantification for Safet...
~
Boafo, Emmanuel.
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Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants./
Author:
Boafo, Emmanuel.
Published:
Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:
165 p.
Notes:
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:
Dissertation Abstracts International78-10B(E).
Subject:
Engineering. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10608954
ISBN:
9781369800869
Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants.
Boafo, Emmanuel.
Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants.
- Ann Arbor : ProQuest Dissertations & Theses, 2016 - 165 p.
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Thesis (Ph.D.)--University of Ontario Institute of Technology (Canada), 2016.
There is an increasing interest in computational reactor safety analysis to systematically replace the conservative calculations by best estimate calculations augmented by quantitative uncertainty analysis methods. This has been necessitated by recent regulatory requirements that have permitted the use of such methods in reactor safety analysis. Stochastic uncertainty quantification methods have shown great promise, as they are better suited to capture the complexities in real engineering problems. This study proposes a framework for performing uncertainty quantification based on the stochastic approach, which can be applied to enhance safety analysis. (Abstract shortened by ProQuest.).
ISBN: 9781369800869Subjects--Topical Terms:
586835
Engineering.
Uncertainty Quantification for Safety Verification Applications in Nuclear Power Plants.
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There is an increasing interest in computational reactor safety analysis to systematically replace the conservative calculations by best estimate calculations augmented by quantitative uncertainty analysis methods. This has been necessitated by recent regulatory requirements that have permitted the use of such methods in reactor safety analysis. Stochastic uncertainty quantification methods have shown great promise, as they are better suited to capture the complexities in real engineering problems. This study proposes a framework for performing uncertainty quantification based on the stochastic approach, which can be applied to enhance safety analysis. (Abstract shortened by ProQuest.).
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10608954
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