Total pressure non-uniformities in an axial flow compressor can contribute to losses in aerodynamic operability through a reduction in stall margin, pressure rise and mass flow, and to loss of structural integrity through means of high cycle fatigue (HCF). HCF is a primary mechanism of blade failure caused by vibrations at levels exceeding material endurance limits. Previous research has shown total pressure distortions to be the dominant HCF driver in aero engines, and has demonstrated the damaging results of total pressure distortion induced HCF on first stage fan and compressor blade rows [Manwaring et al., 1997]. It is, however, also of interest to know how these distortion patterns propagate through a rotor stage and impact subsequent downstream stages and engine components. With current modeling techniques, total pressure distortion magnitudes can be directly correlated to induced blade vibratory levels and modes. The ability to predict downstream distortion patterns then allows for the inference of blade vibratory response of downstream blades to inlet distortion patterns. Given a total pressure distortion excitation entering a blade row, the nonlinear Volterra series can serve as a predictor of the downstream total pressure profile and therefore provide insight into the potential for HCF in downstream blade rows.
This report presents the adaption of nonlinear Volterra theory to the prediction of the transport of non-uniform total pressure distortions through an axial flow compressor. The use of Volterra theory in nonlinear system modeling relies on the knowledge of Volterra kernels, which capture the behavior of a system's response characteristics. Here an empirical method is illustrated for identifying these kernels based on total pressure distortion patterns measured both upstream and downstream of a transonic rotor of modern design. A Volterra model based on these kernels has been applied to the prediction of distortion transfer at new operating points of the same rotor with promising results. Methods for improving Volterra predictions by training Volterra kernels along individual streamlines and normalizing total pressure data sets by physics-based parameters are also investigated. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/35995 |
| Date | 07 December 2001 |
| Creators | Luedke, Jonathan Glenn |
| Contributors | Mechanical Engineering, O'Brien, Walter F. Jr., Dancey, Clinton L., King, Peter S., Thole, Karen A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | thesisfinal.pdf |
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