Abstract

Recent trends in turbomachinery blade technology have led to increased use of monolithically constructed bladed disks (blisks). Although offering a wealth of performance benefits, this construction removes the blade-attachment interface present in the conventional design, thus unintentionally removing a source of friction-based damping needed to counteract large vibrations during resonance passages. This issue is further exacerbated in the presence of blade mistuning that arises from small imperfections from otherwise identical blades and are unavoidable as they originate from manufacturing tolerances and operational wear over the lifespan of the engine. Mistuning is known to induce vibration localization with large vibration amplitudes that render blades susceptible to failure induced by high-cycle fatigue. The resonance frequency detuning (RFD) method reduces vibration associated with resonance crossings by selectively altering the blades' structural response. This method utilizes the variable stiffness properties of piezoelectric materials to switch between available stiffness states at some optimal time as the excitation frequency sweeps through a resonance. For a single-degree-of-freedom (SDOF) system, RFD performance is well defined. This research provides the framework to extend RFD to more realistic applications when the SDOF assumption breaks down, such as in cases of blade mistuning. Mistuning is inherently random; thus, a Monte Carlo analysis performed on a computationally cheap lumped-parameter model provides insight into RFD performance for various test parameters. Application of a genetic algorithm reduces the computational expense required to identify the optimal set of stiffness-state switches. This research also develops a low-order blisk model with blade-mounted piezoelectric patches as a tractable first step to apply RFD to more realistic systems. Application of a multi-objective optimization algorithm produces Pareto fronts that aid in the selection of the optimized patch parameters. Experimental tests utilizing the academic blisk with the optimized patches provides validation.

Notes

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Graduation Date

2019

Semester

Spring

Advisor

Kauffman, Jeffrey L.

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Mechanical Engineering

Format

application/pdf

Identifier

CFE0007488

URL

http://purl.fcla.edu/fcla/etd/CFE0007488

Language

English

Release Date

May 2019

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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