分层旋流火焰特征提取与燃烧不稳定预测  

Feature Extraction and Combustion Instabilities Prediction of Stratified Swirling Flame

作　　者：张世红 王欣尧 张弛[1] 韩啸[1] 周宇晨 Zhang Shihong;Wang Xinyao;Zhang Chi;Han Xiao;Zhou Yuchen(Research Institute of Aero-Engine,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学航空发动机研究院,北京100191

出　　处：《燃烧科学与技术》2023年第5期483-490,共8页Journal of Combustion Science and Technology

基　　金：国家自然科学基金资助项目(52106128);国家科技重大专项资助项目(J2019-Ⅲ-0014-0057).

摘　　要：燃烧振荡是火焰释热脉动与压力脉动相耦合形成的不稳定现象,自发光图像是其研究中较易采集的一类数据.本文采用手动提取、无监督和声压级监督神经网络三种特征提取方法,在瞬时图像和时均图像两个数据集上提取了火焰图像的低维特征,并分析了其显著性和物理表征,测试了其声压级预测精度和迁移能力.研究结果表明,本文构建的声压级监督网络能够在复现火焰物理含义的基础上实现强鲁棒性、高精度、强迁移性的特征提取和振荡预测.Combustion instabilities are unstable phenomena arising from the coupling of heat release rate oscillations and pressure fluctuations.The chemiluminescence image is one of the most accessible data types in experimental combustion research.This paper adopted three feature extraction methods:hand-crafted,unsupervised neural network(USN),and sound pressure level supervision neural network(PSN).The features were extracted from two data sets:transient image and time-averaged image.Based on the extracted features,their significance and physical characterization were analyzed,and their sound pressure level prediction accuracy and transfer ability were tested.The results show that the proposed PSN in this paper can achieve feature extraction and instabilities prediction with excellent robustness,accuracy and transfer ability based on the reproduction of the physical mean-ing of flame.

关 键 词：燃烧振荡 数据集 特征提取 振荡预测 

分 类 号：TK11[动力工程及工程热物理—热能工程]