A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Abstract

Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO). The present work explores a Cross-wavelet transform (XWT) aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.