<p>Micro-combustion has shown&nbsp;significant potential to study and characterize the combustion&nbsp;behavior&nbsp;of hydrocarbon fuels. Among several experimental approaches based on&nbsp;this method, the most prominent one employs an externally heated micro-channel. Three distinct combustion regimes reported for this device namely, weak flames, flames with repetitive&nbsp;extinction&nbsp;and ignition (FREI), and normal flames, which are formed at low, moderate, and high&nbsp;flow&nbsp;rate ranges,&nbsp;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. &nbsp;&nbsp;In a highly&nbsp;diluted&nbsp;fuel/oxidizer mixture, it is observed that FREI&nbsp;disappear&nbsp;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&nbsp;neural&nbsp;network (CNN) is introduced to classify&nbsp;the flame regime. The accuracy of the model is calculated to be 99.5, 99.32, and 99.5% for the “training”, “developing”, and “testing” dataset, 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&nbsp;scenarios is used to generate flame images to increase the size of the dataset.</p>

Classification of Microchannel Flame Regimes Based on Convolutional Neural Networks

Paper Proceedings: https://asmedigitalcollection.asme.org/POWER/POWER2021/volume/85109

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