12th European Conference on Turbomachinery Fluid dynamics & Thermodynamics

Paper ID:

ETC2017-083

Main Topic:

Vibrations

https://doi.org/10.29008/ETC2017-083

Authors

Samuel Norton - Department of Mechanical Engineering, Aristotle University of Thessaloniki
Taylor Ramsay - Department of Mechanical Engineering, Aristotle University of Thessaloniki
Kostas Karatzas - Department of Mechanical Engineering, Aristotle University of Thessaloniki
Jens Fridh - Department of Energy Technology, KTH Royal Institute of Technology
Paul Petrie-Repar - Department of Energy Technology, KTH Royal Institute of Technology

Abstract

A method for modelling turbomachine blade vibration events is proposed, based on computational intelligence algorithms. The method utilises steady thermodynamic data and blade tip-timing data to identify high amplitude vibration events and to draw underlying relationships between steady-thermodynamic input channels and resultant blade motion characteristics. Several computational studies probe specific process aspects in order to improve model prediction accuracy and several methods of data-feature reduction are established to further enhance vibration predictions. Overall, the study shows promise of what prediction capabilities can be achieved with seemingly limited instrumentation. Drawbacks in matters of tip-timing interpretation, quality/quantity of data and process limitations are discussed. Consequential future objectives are outlined to envisage onward predictive accuracy.

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