Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty

Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty

Bibliographic Details
Main Author: Boyd, Ian M.
Language:English
Published: Wright State University / OhioLINK 2019
Subjects:
Mechanical Engineering
Surrogate Modeling
Mistuning
Non-stationary response
Classification
Clustering
Adaptive Sampling
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-wright15670138489190622021-08-03T07:12:54Z Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty Boyd, Ian M. Mechanical Engineering Surrogate Modeling Mistuning Non-stationary response Classification Clustering Adaptive Sampling Integrally Bladed Rotors (IBR) of aircraft turbine engines suffer from fluctuations in the dynamic response that occurs due to blade to blade geometric deviations. The Stochastic Approach for Blade and Rotor Emulation (SABRE) framework has been used to enable a probabilistic study of mistuned blades in which a reduced order modeling technique is applied in conjunction with sets of surrogate models, called emulators, to make predictions of mistuned mode shapes. SABRE has proven useful for non-switching mode shapes. However, switching mode shapes have non-stationary or discontinuous response surfaces which reduce the accuracy of the surrogate models used in SABRE. To improve emulator accuracy, the methodology proposed in this thesis was developed. This methodology improves prediction quality by identifying and eliminating non-stationary and discontinuous portions of the response with the classification decision boundary methodology, efficiently identifying areas of inaccuracy while improving the surrogate as efficiently as possible with adaptive sampling, and alleviating the computational burden associated with large numbers of finite element samples required to build accurate emulators. 2019-08-30 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062 http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.
collection NDLTD
language English
sources NDLTD
topic Mechanical Engineering
Surrogate Modeling
Mistuning
Non-stationary response
Classification
Clustering
Adaptive Sampling
spellingShingle Mechanical Engineering
Surrogate Modeling
Mistuning
Non-stationary response
Classification
Clustering
Adaptive Sampling
Boyd, Ian M.
Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
author Boyd, Ian M.
author_facet Boyd, Ian M.
author_sort Boyd, Ian M.
title Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
title_short Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
title_full Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
title_fullStr Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
title_full_unstemmed Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty
title_sort adaptive identification of classification decision boundary of turbine blade mode shape under geometric uncertainty
publisher Wright State University / OhioLINK
publishDate 2019
url http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062
work_keys_str_mv AT boydianm adaptiveidentificationofclassificationdecisionboundaryofturbineblademodeshapeundergeometricuncertainty
_version_ 1719456431897313280

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