schliessen

Filtern

 

Bibliotheken

A Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables

Statistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to inco... Full description

Journal Title: Journal of Mechanical Design (Transactions of the ASME) Mar 2010, Vol.132(3), p.031006 (11)
Main Author: Liu, Yu
Other Authors: Yin, Xiaolei , Arendt, Paul , Chen, Wei , Huang, Hong-Zhong
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1050-0472 ; DOI: 10.1115/1.4001211YouarenotloggedintotheASMEDigitalLibrary.
Link: http://search.proquest.com/docview/1671410557/?pq-origsite=primo
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: proquest1671410557
title: A Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
format: Article
creator:
  • Liu, Yu
  • Yin, Xiaolei
  • Arendt, Paul
  • Chen, Wei
  • Huang, Hong-Zhong
subjects:
  • Mathematical Models
  • Sensitivity Analysis
  • Design Engineering
  • Strategy
  • Multilevel
  • Covariance
  • Mathematical Analysis
  • Agglomeration
  • Design Principles (Mt)
  • (An)
ispartof: Journal of Mechanical Design (Transactions of the ASME), Mar 2010, Vol.132(3), p.031006 (11)
description: Statistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. For engineering systems with dependent subsystem responses and shared variables, an extended HSSA method with shared variables (named HSSA-SV) is developed in this work. A top-down strategy, the same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from the lower level submodels in the upper level SSA and the covariance of dependent responses is decomposed into the contributions from individual shared variables. An extended aggregation formulation is developed to integrate local submodel SSA results to estimate the global impact of lower level inputs on the top level response. The effectiveness of the proposed HSSA-SV method is illustrated via a mathematical example and a multiscale design problem.
language: eng
source:
identifier: ISSN: 1050-0472 ; DOI: 10.1115/1.4001211YouarenotloggedintotheASMEDigitalLibrary.
fulltext: no_fulltext
issn:
  • 10500472
  • 1050-0472
url: Link


@attributes
ID343624537
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid1671410557
sourceidproquest
recordidTN_proquest1671410557
sourcesystemPC
pqid1671410557
galeid225503082
display
typearticle
titleA Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
creatorLiu, Yu ; Yin, Xiaolei ; Arendt, Paul ; Chen, Wei ; Huang, Hong-Zhong
contributorLiu, Yu (correspondence author)
ispartofJournal of Mechanical Design (Transactions of the ASME), Mar 2010, Vol.132(3), p.031006 (11)
identifierISSN: 1050-0472 ; DOI: 10.1115/1.4001211YouarenotloggedintotheASMEDigitalLibrary.
subjectMathematical Models ; Sensitivity Analysis ; Design Engineering ; Strategy ; Multilevel ; Covariance ; Mathematical Analysis ; Agglomeration ; Design Principles (Mt) ; (An)
descriptionStatistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. For engineering systems with dependent subsystem responses and shared variables, an extended HSSA method with shared variables (named HSSA-SV) is developed in this work. A top-down strategy, the same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from the lower level submodels in the upper level SSA and the covariance of dependent responses is decomposed into the contributions from individual shared variables. An extended aggregation formulation is developed to integrate local submodel SSA results to estimate the global impact of lower level inputs on the top level response. The effectiveness of the proposed HSSA-SV method is illustrated via a mathematical example and a multiscale design problem.
languageeng
source
version3
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
backlink$$Uhttp://search.proquest.com/docview/1671410557/?pq-origsite=primo$$EView_record_in_ProQuest_(subscribers_only)
search
creatorcontrib
0Liu, Yu
1Yin, Xiaolei
2Arendt, Paul
3Chen, Wei
4Huang, Hong-Zhong
titleA Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
descriptionStatistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. For engineering systems with dependent subsystem responses and shared variables, an extended HSSA method with shared variables (named HSSA-SV) is developed in this work. A top-down strategy, the same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from the lower level submodels in the upper level SSA and the covariance of dependent responses is decomposed into the contributions from individual shared variables. An extended aggregation formulation is developed to integrate local submodel SSA results to estimate the global impact of lower level inputs on the top level response. The effectiveness of the proposed HSSA-SV method is illustrated via a mathematical example and a multiscale design problem.
subject
0Mathematical Models
1Sensitivity Analysis
2Design Engineering
3Strategy
4Multilevel
5Covariance
6Mathematical Analysis
7Agglomeration
8Design Principles (Mt)
9(An)
1061
11Yes
general
0English
110.1115/1.4001211YouarenotloggedintotheASMEDigitalLibrary.
2ANTE: Abstracts in New Technology & Engineering
3Mechanical & Transportation Engineering Abstracts
4Engineering Research Database
5Technology Research Database
6ProQuest Engineering Collection
7ProQuest Technology Collection
8ProQuest SciTech Collection
9Materials Science & Engineering Database
10SciTech Premium Collection
11Technology Collection
sourceidproquest
recordidproquest1671410557
issn
010500472
11050-0472
rsrctypearticle
creationdate2010
addtitleJournal of Mechanical Design (Transactions of the ASME)
searchscope
01007421
11007526
21007944
310000013
410000015
510000022
610000041
710000053
810000120
910000203
1010000209
1110000250
1210000260
1310000265
14proquest
scope
01007421
11007526
21007944
310000013
410000015
510000022
610000041
710000053
810000120
910000203
1010000209
1110000250
1210000260
1310000265
14proquest
lsr43
01007421false
11007526false
21007944false
310000013false
410000015false
510000022false
610000041false
710000053false
810000120false
910000203false
1010000209false
1110000250false
1210000260false
1310000265false
contributorLiu, Yu
startdate20100301
enddate20100301
citationpf 031006 (11) pt 031006 (11) vol 132 issue 3
lsr30VSR-Enriched:[doi, galeid, pqid, eissn]
sort
titleA Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
authorLiu, Yu ; Yin, Xiaolei ; Arendt, Paul ; Chen, Wei ; Huang, Hong-Zhong
creationdate20100301
lso0120100301
facets
frbrgroupid8346939112726521152
frbrtype5
languageeng
creationdate2010
topic
0Mathematical Models
1Sensitivity Analysis
2Design Engineering
3Strategy
4Multilevel
5Covariance
6Mathematical Analysis
7Agglomeration
8Design Principles (Mt)
9(An)
collection
0ANTE: Abstracts in New Technology & Engineering
1Mechanical & Transportation Engineering Abstracts
2Engineering Research Database
3Technology Research Database
4ProQuest Engineering Collection
5ProQuest Technology Collection
6ProQuest SciTech Collection
7Materials Science & Engineering Database
8SciTech Premium Collection
9Technology Collection
prefilterarticles
rsrctypearticles
creatorcontrib
0Liu, Yu
1Yin, Xiaolei
2Arendt, Paul
3Chen, Wei
4Huang, Hong-Zhong
jtitleJournal of Mechanical Design (Transactions of the ASME)
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextno_fulltext
addata
aulast
0Liu
1Yin
2Arendt
3Chen
4Huang
aufirst
0Yu
1Xiaolei
2Paul
3Wei
4Hong-Zhong
au
0Liu, Yu
1Yin, Xiaolei
2Arendt, Paul
3Chen, Wei
4Huang, Hong-Zhong
addauLiu, Yu
atitleA Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables
jtitleJournal of Mechanical Design (Transactions of the ASME)
risdate20100301
volume132
issue3
spage031006 (11)
epage031006 (11)
pages031006 (11)
issn1050-0472
formatjournal
genrearticle
ristypeJOUR
abstractStatistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. For engineering systems with dependent subsystem responses and shared variables, an extended HSSA method with shared variables (named HSSA-SV) is developed in this work. A top-down strategy, the same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from the lower level submodels in the upper level SSA and the covariance of dependent responses is decomposed into the contributions from individual shared variables. An extended aggregation formulation is developed to integrate local submodel SSA results to estimate the global impact of lower level inputs on the top level response. The effectiveness of the proposed HSSA-SV method is illustrated via a mathematical example and a multiscale design problem.
doi10.1115/1.4001211
urlhttp://search.proquest.com/docview/1671410557/
eissn15289001
date2010-03-01