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http://dx.doi.org/10.3744/SNAK.2022.59.2.72

Study on the Reconstruction of Pressure Field in Sloshing Simulation Using Super-Resolution Convolutional Neural Network  

Kim, Hyo Ju (Department of Naval Architecture and Offshore Engineering, Dong-A University)
Yang, Donghun (Department of Intelligent Infrastructure Technology Research, KISTI)
Park, Jung Yoon (Department of Naval Architecture and Offshore Engineering, Dong-A University)
Hwang, Myunggwon (Department of Intelligent Infrastructure Technology Research, KISTI)
Lee, Sang Bong (Department of Naval Architecture and Offshore Engineering, Dong-A University)
Publication Information
Journal of the Society of Naval Architects of Korea / v.59, no.2, 2022 , pp. 72-79 More about this Journal
Abstract
Deep-learning-based Super-Resolution (SR) methods were evaluated to reconstruct pressure fields with a high resolution from low-resolution images taken from a coarse grid simulation. In addition to a canonical SRCNN(super-resolution convolutional neural network) model, two modified models from SRCNN, adding an activation function (ReLU or Sigmoid function) to the output layer, were considered in the present study. High resolution images obtained by three models were more vivid and reliable qualitatively, compared with a conventional super-resolution method of bicubic interpolation. A quantitative comparison of statistical similarity showed that SRCNN model with Sigmoid function achieved best performance with less dependency on original resolution of input images.
Keywords
Super-resolution; Deep learning; Computational Fluid Dynamics(CFD); Sloshing; Statistical similarity;
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1 Xu, X., Sun, D., Pan, J., Zhang, Y., Pfister, H. & Yang, M. H., 2017. Learning to super-resolve blurry face and text images. In Proceedings of the IEEE international conference on computer vision, pp.251-260.
2 Jung, J. H., Yoon, H. S. & Lee, C. Y., 2015. Effect of natural frequency modes on sloshing phenomenon in a rectangular tank. International Journal of Naval Architecture and Ocean Engineering, 7(3), pp.580-594.   DOI
3 Lee, S. I.,Yang, G. M., Lee, J., Lee, J. H., Jeong, Y. J., Lee, J. G. & Choi, W., 2019. Recognition and visualization of crack on concrete wall using deep learning and transfer learning. Journal of the Korean Society of Agricultural Engineers, 61(3), pp.55-65.   DOI
4 Chen, Y.G., Djidjeli, K. & Price, W.G., 2009. Numerical simulation of liquid sloshing phenomena in partially filled con-tainers. Computers & Fluids, 38(4), pp.830-842.   DOI
5 Deng, Z., He, C., Liu, Y. & Kim, K. C., 2019. Super- resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework. Physics of Fluids, 31(12), pp.125111.   DOI
6 Fukami, K., Fukagata, K. & Taira, K., 2018. Super- resolution reconstruction of turbulent flows with machine learning. Journal of Fluid Mechanics, 870, pp.106-120.   DOI
7 Gupta, R. & Jaiman, R., 2021. Hybrid physics-based deep learning methodology for moving interface and fluid- structure interaction. arXiv preprint arXiv:2102.09095.
8 Kingma, D. P. & Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
9 Park, W. S. & Kim, M., 2016. CNN-based in-loop filtering for coding efficiency improvement. In 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp.1-5.
10 Dong, C., Loy, C.C., He, K. & Tang, X., 2014. Learning a deep convolutional network for image super-resolution. European conference on computer vision, pp.184-199.
11 Hui, X., Bai, J., Wang, H. & Zhang, Y., 2020. Fast pressure distribution prediction of airfoils using deep learning. Aerospace Science and Technology, 105, pp.105949.   DOI
12 Kang, D.H. & Lee, Y.B., 2005. Summary report of sloshing model test for rectangular model. No. 001. South Korea: Daewoo Shipbuilding & Marine Engineering Co., Ltd.
13 Shekar, B.H., & Dagnew, G., 2019. Grid search-based hyperparameter tuning and classification of microarray cancer data. Second International Conference on Advanced Computational and Communication Paradigms, pp.1-8.