论文标题
数据驱动的湍流图像分析
Data-driven Analysis of Turbulent Flame Images
论文作者
论文摘要
湍流的预热火焰对于使用燃气轮机发电很重要。对湍流火焰的表征和理解的改善，特别是对于暂时性事件（例如点火和灭绝）而继续进行。在这些事件中，袋或未燃烧的材料岛是湍流火焰的特征。这些特征与释放速率和碳氢化合物的排放直接相关。使用OH Planar激光诱导的荧光图像研究了湍流CH $ _4 $/空气预混合火焰的无燃料材料口袋。卷积神经网络（CNN）用于对包含三个湍流火焰的未燃烧口袋的图像进行分类，其中包括0％，5％和10％CO $ _2 $添加。使用三个卷积层和两个完全连接的层使用辍学和重量衰减构建CNN模型。 CNN模型的精度分别为三火焰的精度分别为91.72％，89.35％和85.80％。
Turbulent premixed flames are important for power generation using gas turbines. Improvements in characterization and understanding of turbulent flames continue particularly for transient events like ignition and extinction. Pockets or islands of unburned material are features of turbulent flames during these events. These features are directly linked to heat release rates and hydrocarbons emissions. Unburned material pockets in turbulent CH$_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%, 5%, and 10% CO$_2$ addition. The CNN model was constructed using three convolutional layers and two fully connected layers using dropout and weight decay. The CNN model achieved accuracies of 91.72%, 89.35% and 85.80% for the three flames, respectively.
