Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor

In this study, the frequency and amplitude of combustion instability were predicted using an artificial neural network (ANN). Experimental data, obtained from a CH4-fueled partially premixed combustor were used to train the ANN model. The instability frequency and amplitude as well as the axial flame distance and injection velocity were measured under various equivalence ratios and flow rates. The experiments indicated that the equivalence ratio, axial flame distance, and injection velocity varied the frequency and amplitude of combustion instability. These three factors were set as candidates of input parameters for the instability prediction model. ANNs for predicting instability frequency and amplitude were trained by dividing them into three categories: ANNs with a single input parameter, two input parameters, and three input parameters. As a result, the ANNs with a single input parameter and two input parameters did not predict both the instability frequency and amplitude simultaneously. However, the ANN trained using three input parameters predicted both the instability frequency and amplitude accurately. The high prediction accuracy can be also confirmed by the correlation coefficients (0.9971 for instability frequency and 0.9204 for instability amplitude). Therefore, the three parameters were crucial for determining the instability frequency and amplitude. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In this study, an artificial neural network was used to predict the frequency and amplitude of combustion instability, with experimental results showing that equivalence ratio, axial flame distance, and injection velocity play crucial roles in determining the instability. The ANN trained with three input parameters accurately predicted both the instability frequency and amplitude, with high correlation coefficients confirming the prediction accuracy.