# 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. s +
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Although these findings on the swirling flame and blowout phenomena revealed the correlation between the swirling flame morphology and the LBO, some present research studies on the LBO detection methods lack the full use of these understandings. The existing detection methods of flame state and prediction methods of LBO mostly come from the use of statistical methods to analyze and process optical, acoustic, electrical, and thermal signals [19]. Although these methods have a fast response and wide application, their judgment of the blowout state has obvious uncertainty and lag. Therefore, it is often necessary to set a considerable degree of margin to avoid false judgment. Other methods for predicting LBO rarely balance prediction efficiency and accuracy. In addition, the previous researchers' use of swirling flame optical signals in the prediction of LBO mostly came from the flame chemiluminescence intensity, and there was no consideration for the specific flame morphology. For example, [PERSON] et al. used the time-domain characteristics and statistics of local OH chemiluminescence signals [3; 20; 21; 22]. [PERSON] et al. [23] and [PERSON] et al. [24] both used chemiluminescence color and its spectral distribution as prediction indicators of LBO. [PERSON] et al. [25] turned to use the characteristic phenomenon of OH chemiluminescence signal to quantify the occurrence of the stability index (SI) as a predictive indicator of LBO. [PERSON] et al. [26] conducted a comprehensive statistical analysis of the near-LBO combustion phenomenon, investigated the cumulative distribution function, autocorrelation coefficient, and probability density function of the OH\({}^{*}\) chemiluminescence signal, and finally proposed a normalized root mean square of the OH\({}^{*}\) chemiluminescence signal as an indicator of LBO. One possible way to link the two aspects, the understanding of the relevance between the swirling flame dynamic and LBO, and the swirling flame LBO prediction, is to propose a novel method that applies the swirling flame dynamics to predict swirling flame LBO. With the rapid development of machine learning and its regression ability for non-linear problems, the application of machine learning methods in combustion science is also increasing [27]. Though deep-learning-based techniques, including the convolutional neural networks [28; 29; 30] and auto-encoders [31; 32; 33], can automatically extract discriminant features from raw data through multi-layer nonlinear transformation, the interpretability of the features they extract has long been questioned.