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Proceedings Paper

Data collection and analysis software development for rotor dynamics testing in spin laboratory
Author(s): Ali Abdul-Aziz; Daniel Arble; Mark Woike
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Paper Abstract

Gas turbine engine components undergo high rotational loading another complex environmental conditions. Such operating environment leads these components to experience damages and cracks that can cause catastrophic failure during flights. There are traditional crack detections and health monitoring methodologies currently being used which rely on periodic routine maintenances, nondestructive inspections that often times involve engine and components dis-assemblies. These methods do not also offer adequate information about the faults, especially, if these faults at subsurface or not clearly evident. At NASA Glenn research center, the rotor dynamics laboratory is presently involved in developing newer techniques that are highly dependent on sensor technology to enable health monitoring and prediction of damage and cracks in rotor disks. These approaches are noninvasive and relatively economical. Spin tests are performed using a subscale test article mimicking turbine rotor disk undergoing rotational load. Non-contact instruments such as capacitive and microwave sensors are used to measure the blade tip gap displacement and blade vibrations characteristics in an attempt develop a physics based model to assess/predict the faults in the rotor disk. Data collection is a major component in this experimental-analytical procedure and as a result, an upgrade to an older version of the data acquisition software which is based on LabVIEW program has been implemented to support efficiently running tests and analyze the results. Outcomes obtained from the tests data and related experimental and analytical rotor dynamics modeling including key features of the updated software are presented and discussed.

Paper Details

Date Published: 19 April 2017
PDF: 10 pages
Proc. SPIE 10171, Smart Materials and Nondestructive Evaluation for Energy Systems 2017, 101710L (19 April 2017); doi: 10.1117/12.2260115
Show Author Affiliations
Ali Abdul-Aziz, Kent State Univ. (United States)
Daniel Arble, Univ. of Maryland, College Park (United States)
Mark Woike, NASA Glenn Research Ctr. (United States)

Published in SPIE Proceedings Vol. 10171:
Smart Materials and Nondestructive Evaluation for Energy Systems 2017
Norbert G. Meyendorf, Editor(s)

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