Lean Blowout Sensing and Processing via Optical Interferometry and Wavelet Analysis of Dynamic Pressure Data
This paper introduces a novel approach to monitor combustion instabilities in turbomachinery. This innovation is motivated by the need to better analyze flame health in a continuous flow machine, and more precisely in its hot core expressed by machine OEMs and end-users. This improved monitoring system will support an optimization of the machine performance, leading to a safer, cleaner, more flexible and more cost-efficient operation for the end-user. There have been several numerical studies that simulate the nonlinear dynamics of flame instabilities limit in simple combustors, they predict the pressure oscillations frequency is very low 5-25 Hz. Nowadays, in gas turbine monitoring, dynamic pressure measurement sensors are based on electrical working principle technologies such as piezoelectric, piezo-resistive and capacitive measurement. While exposed to high temperatures, due to pyro-electric effects and noise spectral density of charge amplifiers, piezo sensors signal to noise ratio (SNR) decreases sharply at low frequencies (about a factor 10 every 100°). Optical sensing systems provide much better stability, in the low frequency range when exposed to high temperature and, moreover, are intrinsically insensitive to vibration or EMI perturbation. This feature allows analyzing more accurately low frequency flame dynamics.
Within the frame of a joint study between Meggitt, Combustion Bay One e.U. and FH Joanneum GmbH, the behavior of an optical measurement system was tested under different combustion instability conditions. By acting on system parameters (equivalent ratio, air speed) several tests have been conducted where combustion has been driven to different instability conditions such as premixed to diffusion flame transition, lean blow out, flashback.
Data have been recorded and analysed from both the optical and the piezo sensors installed within the combustion chamber.
The objective of the test campaign is twofold: compare optical to piezo system performances under flame combustion instabilities and, taking advantage of improved low frequency SNR of pressure data coming from optical sensors, to develop and validate an algorithmic strategy to detect/prognose phenomena such as Lean Blow Out and Flashback.
The paper initially describes the principia of the used optical sensing technology, including some relevant hardware and software aspects of the measurement chain. After that, the experimental set-up and tests conditions are presented.
About piezo to optical data comparison, SNR analysis of test data confirms the expectations of a lower noise at low frequency and the improved visibility of flame instabilities provided by optical sensor. Based on this, in order to analyze the transient behavior at low frequencies associated to flame instabilities two algorithmic strategies have been developed: wavelet analysis and chaos dimension analysis. Based on these methodologies several Lean Blow Out indicators have been implemented and tested. Details of their behavior in response to the variation of the equivalence ratio close to the flame blow out are presented and discussed. Three of the proposed indicators, under the investigated test conditions, proved to possess the capability to detect the blow out event. Depending on how the approach to the blow out event occurs, some indicators also shown prognostics capability. Results show some indicators start reacting as the flame approaches to extinction or as the equivalence ratio reduces. These indicators can be considered flame health meters.
The test campaign results shown in the paper allow to conclude that optical sensing technologies provide advantages over piezoelectric sensing technologies such as: insensitivity to pyro-electric effects and inherent insensitivity to external perturbations, i.e. electromagnetic interferences and radio frequency interferences, vibrations.
Particularly the improved accuracy at low frequencies of the sensor coupled with new signal processing techniques, as the ones presented, may support a flame health monitoring capability and the ability to sense blowout precursors. The replacement of piezo-electric with fiber optic sensors will therefore be desirable for many combustion monitoring applications, especially the ones requiring monitoring of low frequencies. This can, provide significant payoffs in engine reliability and operability, in enabling optimal performance over extended time periods as an engine ages, in reducing maintenance costs, and in increasing engine life.
How to Cite
Combustion Monitoring, Lean Blow Out, Optical Sensing, Fabry Perot Interferometry, Wavelet Analysis, Pressure sensors
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