Classification of Microchannel Flame Regimes Based on Convolutional Neural Networks

Abstract

Micro-combustion has shown significant potential to study and characterize the combustion behavior of hydrocarbon fuels. Among several experimental approaches based on this method, the most prominent one employs an externally heated micro-channel. Three distinct combustion regimes are reported for this device namely, weak flames, flames with repetitive extinction and ignition (FREI), and normal flames, which are formed at low, moderate, and high flow rate ranges, respectively. Within each flame regime, noticeable differences exist in both shape and luminosity where transition points can be used to obtain insights into fuel characteristics. In this study, flame images are obtained using a monochrome camera equipped with a 430 nm bandpass filter to capture the chemiluminescence signal emitted by the flame. Sequences of conventional flame photographs are taken during the experiment, which are computationally merged to generate high dynamic range (HDR) images. In a highly diluted fuel/oxidizer mixture, it is observed that FREI disappear and are replaced by a gradual and direct transition between weak and normal flames which makes it hard to identify different combustion regimes. To resolve the issue, a convolutional neural network (CNN) is introduced to classify the flame regime. The accuracy of the model is calculated to be 99.34, 99.66, and 99.83% for “training”, “validation”, and “testing” data-sets, respectively. This level of accuracy is achieved by conducting a grid search to acquire optimized parameters for CNN. Furthermore, a data augmentation technique based on different experimental scenarios is used to generate flame images to increase the size of the data-set.