Numerical investigation into fuel–air mixing characteristics and cold flow field of single hydrogen-rich Micromix nozzle

Chen, Xuanren; Wang, Hui; Wang, Chao; Wang, Xiangyu; Wang, Ning; Liu, Xiang

doi:10.1016/j.fuel.2022.126181

Cited by 14 publications

(1 citation statement)

References 30 publications

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“…The loss function of the neural network in this framework uses the MSE + C GL-Mean function, which was previously considered optimal. The test samples are from the BOS results of the micro mixing nozzle under cold conditions (Chen et al 2023). The data preprocessing is similar to the previous one, where 2000 displacement fields were decomposed into X and Y directions, with 70% of these results used…”

Section: Reconstruction Effect On a Practical Application Nozzlementioning

confidence: 99%

Super-resolution reconstruction of background-oriented Schlieren displacement field based on the convolution neural network with the gradient loss function

Wang,

Wang

et al. 2024

Meas. Sci. Technol.

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In order to refine the displacement field of the background oriented schlieren method, a super-resolution method based on deep learning has been newly proposed in the study, and were compared with the bicubic interpolation. The gradient loss functions were firstly introduced into the hybrid downsampled skip-connection/multi-scale (DSC/MS) model to improve the reconstruction effect. The reconstruction effects of the new loss functions were compared with that of the traditional MSE loss function. The results showed that the Laplace operator with average pooling had better performance than the origin loss function in all the indexes including PSNR, MSE, MSE of the gradient and the max MSE. In these four indexes, MSE of the gradient and the max MSE performed especially better than the others, where the MSE of the gradient reduced from 3.90E-05 to 3.30E-05 and the max MSE reduced from 0.392 to 0.360.

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Section: Reconstruction Effect On a Practical Application Nozzlementioning

confidence: 99%