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

Boyd, Ian M.
http://orcid.org/0000-0002-2594-5196
http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062

Abstract Details

2019, Master of Science in Mechanical Engineering (MSME), Wright State University, Mechanical Engineering.
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.
Harok Bae, Ph.D. (Advisor)
Ahsan Mian, Ph.D. (Committee Member)
Jeffrey Brown, Ph.D. (Committee Member)
89 p.
Mechanical Engineering
Surrogate Modeling; Mistuning; Non-stationary response; Classification; Clustering; Adaptive Sampling

Recommended Citations

Citations

  • Boyd, I. M. (2019). Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062

    APA Style (7th edition)

  • Boyd, Ian. Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty. 2019. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062.

    MLA Style (8th edition)

  • Boyd, Ian. "Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty." Master's thesis, Wright State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567013848919062

    Chicago Manual of Style (17th edition)

wright1567013848919062
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© 2019, some rights reserved.Creative Commons License Adaptive Identification of Classification Decision Boundary of Turbine Blade Mode Shape under Geometric Uncertainty by Ian M. Boyd is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.