Data-driven Analysis of Turbulent Flame Images
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Turbulent premixed flames are important for power generation using gas turbines. Improvements in characterization and understanding of turbulent flames continue particularly for transient events like ignition and extinction. Pockets or islands of unburned material are features of turbulent flames during these events. These features are directly linked to heat release rates and hydrocarbons emissions. Unburned material pockets in turbulent CH_4/air premixed flames with CO_2 addition were investigated using OH Planar Laser-Induced Fluorescence images. Convolutional Neural Networks (CNN) were used to classify images containing unburned pockets for three turbulent flames with 0 convolutional layers and two fully connected layers using dropout and weight decay. The CNN model achieved accuracies of 91.72 three flames, respectively.
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