| Citation: | ZHONG Yue, CAI Minnan, XU Wenjiang, et al. Temporal super-resolution imaging of 3D OH concentration field in turbulent flame based on deep learning[J]. Journal of Aerospace Power, 2024, 39（12）：20230071 doi: 10.13224/j.cnki.jasp.20230071
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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 (3D-CNN) 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.
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