Prediction Method of Swirling Flame Lean Blowout Based on Flame Image Morphological Features

Abstract

Swirling flame oscillation, with a local extinguishment-and-reignition phenomenon in advanced low-pollution lean premixed combustion technology, remains a challenge in understanding the underlying physics and predict in technical combustors. Here, a prediction method on swirling flame lean blowout (LBO) is proposed from flame image morphological features. In this method, flame features are first extracted by performing morphological algorithms on flame images. Then, the information of the time series of images is included. By designing the blowout state judgment criterion and the blowout state description method, the typical binary judgment is transformed into a numerical prediction. Finally, a random forest regression model is applied to build a predictive model for the swirling flame LBO. The results show that, with the data set from nine operating conditions, the model can achieve a determination coefficient of 0.9766 and a root mean square error of 3.78 on the 10% test set, which shows a strong generalization ability. This method exhibits potential for practical application in LBO control due to its simplicity and efficiency.

Keywords

• swirling flame
• LBO
• machine learning
• random forest regression model