基于深度学习的湍流火焰三维羟基浓度场的时间超分辨率成像  

Temporal super-resolution imaging of 3D OH concentration field in turbulent flame based on deep learning

作　　者：钟越 蔡敏男 徐文江 杨帆 ZHONG Yue;CAI Minnan;XU Wenjiang;YANG Fan(School of Aerospace Engineering,Xiamen University,Xiamen Fujian 361005,China;Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision,Xiamen University,Xiamen Fujian 361005,China)

机构地区：[1]厦门大学航空航天学院,福建厦门361005 [2]厦门大学厦门市大数据智能分析与决策重点实验室,福建厦门361005

出　　处：《航空动力学报》2024年第12期297-304,共8页Journal of Aerospace Power

基　　金：国家自然科学基金面上项目(62173282);国家自然科学基金青年基金(52006184);厦门市自然科学基金面上项目(3502Z20227180);科技部科技创新2030—“新一代人工智能”重大项目(2021ZD0112600)。

摘　　要：针对火焰三维羟基浓度场的高速测量难度大、成本昂贵的问题,提出一种基于深度学习的帧重建模型Cycle-3D-CNN,用于连续时间的湍流火焰三维羟基浓度场数据。使用基于循环一致性的三维卷积神经网络(3D-CNN),以数值驱动的方式实现了更高的时间分辨率。在实验分析中使用该模型分别实现了三维羟基浓度场时间序列的2倍和3倍时间分辨率提升,验证了其良好的重建性能。在两种实验结果中,峰值信噪比(PSNR)均值分别达到了33.57 dB和30.37 dB,结构相似性(SSIM)指数分别达到了0.899和0.813,均优于传统的帧重建方法。In response to the difficulty and high cost of high-speed measurement of flame hydroxyl concentration field,a Cycle-3D-CNN model based on deep learning was proposed for temporal reconstruction of three-dimensional(3D)hydroxyl concentration fields in turbulent flames.It achieved a higher temporal resolution by utilizing a data-driven approach with a 3D convolutional neural network(3DCNN)based on cycle consistency.In the experimental analysis,the model was used to achieve a two-fold and three-fold increase in temporal resolution of the 3D hydroxyl concentration field time series,respectively.In both experimental results,the mean peak signal-to-noise ratio(PSNR)reached 33.57 dB and 30.37 dB,respectively,the structural similarity(SSIM)indices reached 0.899 and 0.813,respectively,outperforming traditional frame reconstruction method.

关 键 词：湍流燃烧 羟基自由基 时间超分辨率 循环一致性模型 深度学习 

分 类 号：V231[航空宇航科学与技术—航空宇航推进理论与工程] TK12[动力工程及工程热物理—工程热物理]