A novel spatial–temporal prediction method for the effects of fish movement on flow field based on hybrid deep neural network

References

• Cai, G., Zhang, B., Liu, L., Weng, H., Wang, W., & He, B. (2022). Fast vacuum plume prediction using a convolutional neural networks-based direct simulation Monte Carlo method. Aerospace Science and Technology, 129, 107852. https://doi.org/10.1016/j.ast.2022.107852
   Web of Science ®Google Scholar
• Chen, S. Y., Fei, Y. J., Chen, Y. C., Chi, K. J., & Yang, J. T. (2016). The swimming patterns and energy-saving mechanism revealed from three fish in a school. Ocean Engineering, 122, 22–31. https://doi.org/10.1016/j.oceaneng.2016.06.018
   Web of Science ®Google Scholar
• Curatolo, M., & Teresi, L. (2015). The virtual aquarium: Simulations of fish swimming. The Proceedings of the 2015 COMSOL Conference.
   Google Scholar
• De Bie, J., Manes, C., & Kemp, P. S. (2020). Collective behaviour of fish in the presence and absence of flow. Animal Behaviour, 167, 151–159. https://doi.org/10.1016/j.anbehav.2020.07.003
   Web of Science ®Google Scholar
• Eivazi, H., Veisi, H., Naderi, M. H., & Esfahanian, V. (2020). Deep neural networks for nonlinear model order reduction of unsteady flows. Physics of Fluids, 32(10), 105104. https://doi.org/10.1063/5.0020526
   Web of Science ®Google Scholar
• Fukagata, K., Fukami, K., & Taira, K. (2019). Super-resolution reconstruction of turbulent flows with machine learning. Journal of Fluid Mechanics, 870, 106–120. https://doi.org/10.1017/jfm.2019.238
   Web of Science ®Google Scholar
• Guo, X., Li, W., & Iorio, F. (2016). Convolutional neural networks for steady flow approximation. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 481–490).
   Google Scholar
• Han, R., Wang, Y., Zhang, Y., & Chen, G. (2019). A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network. Physics of Fluids, 31(12), 127101. https://doi.org/10.1063/1.5127247
   Web of Science ®Google Scholar
• Hanson, A., Pnvr, K., Krishnagopal, S., & Davis, L. (2018). Bidirectional convolutional LSTM for the detection of violence in videos. Proceedings of the european conference on computer vision (ECCV) workshops (pp. 285–295).
   Google Scholar
• Hao, Y., Xie, X., Zhao, P., Wang, X., Ding, J., Xie, R., & Liu, H. (2023). Forecasting three-dimensional unsteady multi-phase flow fields in the coal-supercritical water fluidized bed reactor via graph neural networks. Energy, 282, 128880. https://doi.org/10.1016/j.energy.2023.128880
   Web of Science ®Google Scholar
• Khalid, M. S. U., Akhtar, I., Imtiaz, H., Dong, H., & Wu, B. (2018). On the hydrodynamics and nonlinear interaction between fish in tandem configuration. Ocean Engineering, 157, 108–120. https://doi.org/10.1016/j.oceaneng.2018.03.049
   Web of Science ®Google Scholar
• Kong, L., Wei, W., & Yan, Q. (2018). Application of flow field decomposition and reconstruction in studying and modeling the characteristics of a cartridge valve. Engineering Applications of Computational Fluid Mechanics, 12(1), 385–396. https://doi.org/10.1080/19942060.2018.1438925
   Web of Science ®Google Scholar
• Li, Y., Pan, Z., & Xia, J. (2022a). Computational analysis on propulsive characteristic of flexible foil undergoing travelling wave motion in ground effect. Ocean Engineering, 253, 111300. https://doi.org/10.1016/j.oceaneng.2022.111300
   Web of Science ®Google Scholar
• Li, Z., Xia, D., Cao, J., Chen, W., & Wang, X. (2022b). Hydrodynamics study of dolphin's self-yaw motion realized by spanwise flexibility of caudal fin. Journal of Ocean Engineering and Science, 7(3), 213–224. https://doi.org/10.1016/j.joes.2021.07.011
   Web of Science ®Google Scholar
• Li, Z., Xia, D., Zhou, X., Cao, J., Chen, W., & Wang, X. (2022c). The hydrodynamics of self-rolling locomotion driven by the flexible pectoral fins of 3-D bionic dolphin. Journal of Ocean Engineering and Science, 7(1), 29–40. https://doi.org/10.1016/j.joes.2021.04.006
   Web of Science ®Google Scholar
• Li, C., Yuan, P., Liu, Y., Tan, J., Si, X., Wang, S., & Cao, Y. (2023). Fast flow field prediction of hydrofoils based on deep learning. Ocean Engineering, 281, 114743. https://doi.org/10.1016/j.oceaneng.2023.114743
   Web of Science ®Google Scholar
• Liao, J. C., Beal, D. N., Lauder, G. V., & Triantafyllou, M. S. (2003). Fish exploiting vortices decrease muscle activity. Science, 302(5650), 1566–1569. https://doi.org/10.1126/science.1088295
   PubMed Web of Science ®Google Scholar
• Liu, Z., Han, R., Zhang, M., Zhang, Y., Zhou, H., Wang, G., & Chen, G. (2022). An enhanced hybrid deep neural network reduced-order model for transonic buffet flow prediction. Aerospace Science and Technology, 126, 107636. https://doi.org/10.1016/j.ast.2022.107636
   Web of Science ®Google Scholar
• Liu, H., Lin, J., Wang, D., Huang, J., Jiang, H., Zhang, D., Peng, Q., & Yang, J. (2023a). Experimental study of the behavioral response of fish to changes in hydrodynamic indicators in a near-natural environment. Ecological Indicators, 154, 110813. https://doi.org/10.1016/j.ecolind.2023.110813
   Web of Science ®Google Scholar
• Liu, H., Zhou, Y., Ren, X., Liu, S., Liu, H., & Li, M. (2023b). Numerical modeling and application of the effects of fish movement on flow field in recirculating aquaculture system. Ocean Engineering, 285, 115432. https://doi.org/10.1016/j.oceaneng.2023.115432
   Web of Science ®Google Scholar
• Meng, F., Wang, J., Chen, Z., Qiao, F., & Yang, D. (2023). Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method. Journal of Environmental Management, 345, 118817. https://doi.org/10.1016/j.jenvman.2023.118817
   PubMed Web of Science ®Google Scholar
• Palmer, M., & Ruhi, A. (2019). Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration. Science, 365(6459), eaaw2087. https://doi.org/10.1126/science.aaw2087
   PubMed Web of Science ®Google Scholar
• Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241).
   Google Scholar
• Rui, E. Z., Chen, Z. W., Ni, Y. Q., Yuan, L., & Zeng, G. Z. (2023). Reconstruction of 3D flow field around a building model in wind tunnel: A novel physics-informed neural network framework adopting dynamic prioritization self-adaptive loss balance strategy. Engineering Applications of Computational Fluid Mechanics, 17(1), 2238849. https://doi.org/10.1080/19942060.2023.2238849
   Web of Science ®Google Scholar
• Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM Network: A machine learning approach for precipitation nowcasting. Proceedings of the 28th International Conference on Neural Information Processing Systems – Volume 1 (pp. 802–810).
   Google Scholar
• Silva, A. T., Bærum, K. M., Hedger, R. D., Baktoft, H., Fjeldstad, H. P., Gjelland, KØ, Økland, F., & Forseth, T. (2020). The effects of hydrodynamics on the three-dimensional downstream migratory movement of Atlantic salmon. Science of The Total Environment, 705, 135773. https://doi.org/10.1016/j.scitotenv.2019.135773
   PubMed Web of Science ®Google Scholar
• Tang, M. F., Xu, T. J., Dong, G. H., Zhao, Y. P., & Guo, W. J. (2017). Numerical simulation of the effects of fish behavior on flow dynamics around net cage. Applied Ocean Research, 64, 258–280. https://doi.org/10.1016/j.apor.2017.03.006
   Web of Science ®Google Scholar
• Videler, J. J., & Hess, F. (1984). Fast continuous swimming of two pelagic predators, Saithe (Pollachius virens) and Mackerel (Scomber scombrus): A kinematic analysis. Journal of Experimental Biology, 109(1), 209–228. https://doi.org/10.1242/jeb.109.1.209
   Google Scholar
• Wang, J., Qie, Z., Li, G., Ran, Y., & Wu, X. (2023). An efficient fish migration modeling method integrating the random forest and Eulerian–Lagrangian–agent method for vertical slot fishways. Ecological Engineering, 195, 107067. https://doi.org/10.1016/j.ecoleng.2023.107067
   Web of Science ®Google Scholar
• Wang, L., & Wu, C. (2010). An adaptive version of ghost-cell immersed boundary method for incompressible flows with complex stationary and moving boundaries. Science China Physics, Mechanics and Astronomy, 53(5), 923–932. https://doi.org/10.1007/s11433-010-0185-z
   Web of Science ®Google Scholar
• Wei, C., Hu, Q., Zhang, T., & Zeng, Y. (2022). Passive hydrodynamic interactions in minimal fish schools. Ocean Engineering, 247, 110574. https://doi.org/10.1016/j.oceaneng.2022.110574
   Web of Science ®Google Scholar
• Wohl, E., Lane, S. N., & Wilcox, A. C. (2015). The science and practice of river restoration. Water Resources Research, 51(8), 5974–5997. https://doi.org/10.1002/2014WR016874
   Web of Science ®Google Scholar
• Xie, X., Wang, X., Zhao, P., Hao, Y., Xie, R., & Liu, H. (2023). Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning. Energy, 263, 125907. https://doi.org/10.1016/j.energy.2022.125907
   Web of Science ®Google Scholar
• Zhao, Z., & Shi, Q. (2023). Hydrodynamic interactions coordinate the swimming of two self-propelled fish-like swimmers. Ocean Engineering, 284, 115263. https://doi.org/10.1016/j.oceaneng.2023.115263
   Web of Science ®Google Scholar
• Zhong, H., Wei, Z., Man, Y., Pan, S., Zhang, J., Niu, B., Yu, X., Ouyang, Y., & Xiong, Q. (2023). Prediction of instantaneous yield of bio-oil in fluidized biomass pyrolysis using long short-term memory network based on computational fluid dynamics data. Journal of Cleaner Production, 391, 136192. https://doi.org/10.1016/j.jclepro.2023.136192
   Web of Science ®Google Scholar
• Zhou, W., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar