Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7530
Title: Stochastic analysis and validation under aleatory and epistemic uncertainties
Authors: McKeand, Austin M.
Görgülüarslan, Recep Muhammet
Choi, Seung-Kyum
Keywords: Uncertainty quantification
Stochastic upscaling
Validation
Turbine blade
Issue Date: 2021
Publisher: Elsevier Sci Ltd
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.
URI: https://doi.org/10.1016/j.ress.2020.107258
https://hdl.handle.net/20.500.11851/7530
ISSN: 0951-8320
1879-0836
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

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