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Parametric modeling and optimization of thermal systems with design uncertainties
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
Parametric modeling and optimization of thermal systems with design uncertainties
Identifier
ETD_2487
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000053116
Language
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(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
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(authority = RUETD)
Topic
Mechanical and Aerospace Engineering
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Structural optimization
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Chemical vapor deposition
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Monte Carlo method
Subject
(ID = SBJ-5);
(authority = ETD-LCSH)
Topic
Reliability (Engineering)
Abstract
(type = abstract)
Thermal systems play significant roles in the engineering practices and our lives. To improve those thermal systems, it is necessary to model and optimize the operating conditions. More importantly, the design uncertainties should be considered because the failures of the thermal systems may be very dangerous and produce large loss. This study focuses on the parametric modeling and the optimization of the thermal systems with the considerations of the design uncertainties. As an example, the material processing thermal system, the Chemical Vapor Deposition (CVD), is simulated with different inlet velocities and susceptor temperatures. Several responses are considered to represent the performance of the thin-film deposition, including the percentage of the working area, the mean of the deposition rate, the root mean square of the deposition, and the surface kurtosis. Those responses are then parametrically modeled by one of the Response Surface Method (RSM), the Radial Basis Function (RBF), and utilized to formulate the optimization problems to enhance the system performances. However, it is not until the design uncertainties are considered that the thermal system designs have high risk of the violations of the performance constraints. One of the Reliability-Based Design Optimization (RBDO) algorithms, the Reliability Index Approach (RIA), is used to solve the optimization problems with the design uncertainties However, the algorithm suffers from a convergence problem when the design point falls into the infeasible domain during the optimization process. A Modified Reliability Index Approach (MRIA) is proposed with a modified definition of the reliability index, and utilized to solve the RBDO problems of the CVD process. The MRIA converts the design space to the standard normal space and finds the Most Probable Points (MPPs) to evaluate the failure probabilities of the performance constraints. The probabilistic optimization problem is then solved using the approximate probabilistic constraints generated in terms of the MPPs. The MRIA has been used to solve several different optimization formulations with both normally and lognormally distributed random variables. The Monte Carlo Simulation (MCS) results verify that the optimal solutions have acceptable failure probabilities. As a result, the proposed strategy of parametrically modeling and optimizing with design uncertainties can be applied to the experiments or the simulations of other thermal systems to improve their productivity, maintain the quality control, and reduce the probability of system failure.
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electronic resource
Extent
xviii, 155 p. : ill.
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application/pdf
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text/xml
Note
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Ph.D.
Note
Includes abstract
Note
Vita
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Po Ting Lin
Name
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Lin
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Po Ting
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1981-
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author
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Po Ting Lin
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Gea
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Hae Chang
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chair
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Advisory Committee
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Hae Chang Gea
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(type = family)
Jaluria
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(type = given)
Yogesh
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co-chair
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Advisory Committee
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Yogesh Jaluria
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(type = family)
Tse
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(type = given)
Stephen
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internal member
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Advisory Committee
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Stephen Tse
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Pham
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Hoang
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outside member
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Advisory Committee
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Hoang Pham
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
DateCreated
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2010
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2010
Place
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xx
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/T3R49QVM
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
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(type = personal)
Name
FamilyName
Lin
GivenName
Po Ting
Role
Copyright Holder
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Type
Permission or license
DateTime
2010-03-09 15:47:11
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Copyright holder
Name
Po Ting Lin
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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ETD
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application/pdf
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application/x-tar
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6799360
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