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A kriging-enhanced aeroelastic stability prediction tool for radial turbomachinery using piston theory
Iskandar, Vincent
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https://hdl.handle.net/2142/120546
Description
- Title
- A kriging-enhanced aeroelastic stability prediction tool for radial turbomachinery using piston theory
- Author(s)
- Iskandar, Vincent
- Issue Date
- 2023-04-26
- Director of Research (if dissertation) or Advisor (if thesis)
- Bodony, Daniel J
- Department of Study
- Aerospace Engineering
- Discipline
- Aerospace Engineering
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- Aeroelasticity
- turbocharger
- flutter
- vibration
- ROM
- CFD
- interpolation
- Kriging
- confidence levels
- Abstract
- Aircraft intermittent combustion engines often incorporate turbochargers adapted from ground-based applications to improve their efficiency and performance. These turbochargers operate in off-design conditions and experience blade failures brought on by aerodynamically-induced blade vibrations. A previously developed reduced-order model leveraging piston theory to compute the aeroelastic stability of general fluid-structural configurations is first presented and summarized. The reduced-order model has been applied to the high-pressure turbine of a dual-stage turbocharger and the results are reviewed as a baseline for new predictions considered in this work. For each operating condition that is investigated, a computational fluid dynamic simulation must be performed to inform the fluid loading predicted by piston theory. Interpolation-based approaches are considered to minimize the numerical expense associated with this requirement. The Gaussian-based Kriging interpolation method is presented and explored. The method provides more accurate estimates for the non-linear behavior of the quantities of interest. Kriging also estimates uncertainty and provides confidence intervals as part of the interpolation process.
- Graduation Semester
- 2023-05
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/120546
- Copyright and License Information
- Copyright 2023 Vincent Iskandar
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