Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/7530
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | McKeand, Austin M. | - |
dc.contributor.author | Görgülüarslan, Recep Muhammet | - |
dc.contributor.author | Choi, Seung-Kyum | - |
dc.date.accessioned | 2021-09-11T15:57:38Z | - |
dc.date.available | 2021-09-11T15:57:38Z | - |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 0951-8320 | - |
dc.identifier.issn | 1879-0836 | - |
dc.identifier.uri | https://doi.org/10.1016/j.ress.2020.107258 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7530 | - |
dc.description.abstract | An uncertainty quantification and validation framework is presented to account for both aleatory and epistemic uncertainties in stochastic simulations of turbine engine components. The spatial variability of the uncertain geometric parameters obtained from coordinate measuring machine data of manufactured parts is represented as aleatory uncertainty. Porosity and defects in the manufactured parts based on micro CT-scanned images are represented as epistemic uncertainty. A stochastic upscaling method and probability box approach are integrated to propagate both the epistemic and aleatory uncertainties from fine models to coarse models to quantify the homogenized elastic modulus uncertainties. The framework is applied for a turbine blade example and validated by modal frequency experiments of the manufactured blade samples. A validation approach, called mean curve validation method, is utilized to effectively compare the p-box of the predictions with the experimental results. The application results show that the proposed framework can significantly reduce the complexity of the engineering problem as well as produce accurate results when both aleatory and epistemic uncertainties exist in the problem. | en_US |
dc.description.sponsorship | NSF I/UCRC, USA [NSF-1822141]; OAI, USA [OAI-VATO2-17005] | en_US |
dc.description.sponsorship | This work was partially supported by NSF I/UCRC, USA (NSF-1822141) and OAI, USA (OAI-VATO2-17005). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Sci Ltd | en_US |
dc.relation.ispartof | Reliability Engineering & System Safety | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Uncertainty quantification | en_US |
dc.subject | Stochastic upscaling | en_US |
dc.subject | Validation | en_US |
dc.subject | Turbine blade | en_US |
dc.title | Stochastic Analysis and Validation Under Aleatory and Epistemic Uncertainties | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Mechanical Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 205 | en_US |
dc.identifier.wos | WOS:000589091300041 | en_US |
dc.identifier.scopus | 2-s2.0-85092525876 | en_US |
dc.institutionauthor | Görgülüarslan, Recep Muhammet | - |
dc.identifier.doi | 10.1016/j.ress.2020.107258 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.7. Department of Mechanical Engineering | - |
Appears in Collections: | Makine Mühendisliği Bölümü / Department of Mechanical Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
2
checked on Dec 21, 2024
WEB OF SCIENCETM
Citations
19
checked on Dec 21, 2024
Page view(s)
98
checked on Dec 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.