Towards a condition monitoring scheme for combustion instability detection and fuel blends performance classification in gas turbine engines using pattern recognition and advanced machine learning
Abstract »The investigation and improvement in fuel performance and combustion is necessary in order to minimize emissions and operation costs in various engineering applications e.g. aerospace. Among these factors, nevertheless, ensuring safe operation is a priority: undesired phenomena, such as thermoacoustic instabilities, can have detrimental effects on jet engines, gas turbines and combustors, in general, due to excessive vibrations. It is for this reason that monitoring and design schemes should be able to identify the potential of occurrence of such events. This is a difficult task due to the complexity of the nature of these events. This paper is a preliminary investigation into the performance and characterization of various fuel blends and the examination of the vibration levels expected for different combustion states of a gas turbine engine. We tackle the issue from the perspective of modifying the input to the system (i.e. the fuel composition) in order to investigate nonlinear behavior of the gas turbine engine through the development of a multi-class classification algorithm. Features from a vibration channel for each of the fuel blends were extracted for both classification modelling and cluster analysis.
Biography: Ioannis is a PhD student at the University of Sheffield and a member of the Dynamics Research Group at the same institution. He holds a Master of Engineering degree in Mechanical Engineering from the University of Bath. His research, which is funded by the University of Sheffield's Mechanical Engineering department, is concerned with the development of advanced data-driven condition monitoring methods using pattern recognition and machine learning.
Affiliation: University of Sheffield Dynamics Research Group, Department of Mechanical Engineering S1 3JD Sheffield United KingdomKhandelwal, BhupendraKhandelwal, Bhupendra email@example.com
Affiliation: University of Sheffield Thermofluids Research Group, Department of Mechanical Engineering S1 3JD Sheffield United KingdomAntoniadou, IfigeneiaAntoniadou, Ifigeneia firstname.lastname@example.org
Affiliation: University of Sheffield Dynamics Research Group, Department of Mechanical Engineering S1 3JD Sheffield United Kingdom*Contact