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Analysis of Process Criticality Accident Risk Using a Metamodel-Driven Bayesian Network.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of Process Criticality Accident Risk Using a Metamodel-Driven Bayesian Network./
Author:	Zywiec, William John.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	220 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
Contained By:	Dissertations Abstracts International82-10B.
Subject:	Nuclear physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28413879
ISBN:	9798708722386

Analysis of Process Criticality Accident Risk Using a Metamodel-Driven Bayesian Network.
Zywiec, William John.

Analysis of Process Criticality Accident Risk Using a Metamodel-Driven Bayesian Network. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 220 p.

Source: Dissertations Abstracts International, Volume: 82-10, Section: B.

Thesis (Ph.D.)--The George Washington University, 2021.

This item must not be sold to any third party vendors.

Since the discovery of fission and subsequent first criticality of Chicago Pile-1, more than 60 criticality accidents have occurred throughout the world. These accidents are divided into two categories: those that occur during critical experiments or operations with research reactors, and those that occur in production facilities, more commonly referred to as process criticality accidents. This dissertation focuses on the development of a methodology that uses a coupled Bayesian network and neural network metamodel to estimate process criticality accident risk. This methodology is essentially a generalized software-based framework that was written in the R programming language and applied to fissile material operations in the Plutonium Facility (Building 332) at Lawrence Livermore National Laboratory. The most significant benefit of using Building 332 as a case study was that it enabled a comprehensive review of the entire process of building a coupled Bayesian network and neural network metamodel and estimating process criticality accident risk. This review also enabled iterative improvements to be made to the methodology as it was being developed. Overall, the coupled Bayesian network and neural network metamodel worked extremely well and has several advantages over existing methodologies.

ISBN: 9798708722386Subjects--Topical Terms:

517741
Nuclear physics.
Subjects--Index Terms:

Process criticality accident

Analysis of Process Criticality Accident Risk Using a Metamodel-Driven Bayesian Network.
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300 $a 220 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-10, Section: B.
500 $a Advisor: Sarkani, Shahram;Mazzuchi, Thomas A.
502 $a Thesis (Ph.D.)--The George Washington University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Since the discovery of fission and subsequent first criticality of Chicago Pile-1, more than 60 criticality accidents have occurred throughout the world. These accidents are divided into two categories: those that occur during critical experiments or operations with research reactors, and those that occur in production facilities, more commonly referred to as process criticality accidents. This dissertation focuses on the development of a methodology that uses a coupled Bayesian network and neural network metamodel to estimate process criticality accident risk. This methodology is essentially a generalized software-based framework that was written in the R programming language and applied to fissile material operations in the Plutonium Facility (Building 332) at Lawrence Livermore National Laboratory. The most significant benefit of using Building 332 as a case study was that it enabled a comprehensive review of the entire process of building a coupled Bayesian network and neural network metamodel and estimating process criticality accident risk. This review also enabled iterative improvements to be made to the methodology as it was being developed. Overall, the coupled Bayesian network and neural network metamodel worked extremely well and has several advantages over existing methodologies.
590 $a School code: 0075.
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650 4 $a Nuclear engineering. $3 595435
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653 $a Accident risk
653 $a Nuclear reactors
653 $a Neural network
653 $a Plutonium facility
653 $a Lawrence Livermore National Laboratory
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690 $a 0756
710 2 $a The George Washington University. $b Systems Engineering. $3 1032058
773 0 $t Dissertations Abstracts International $g 82-10B.
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28413879