Chen, Z., Yang, S., Li, X., Li, Y., & Li, L. (2023). Investigation on leakage vortex cavitation and corresponding enstrophy characteristics in a liquid nitrogen inducer.
Cryogenics, 129, 103606.
https://doi.org/10.1016/j.cryogenics.2022.103606
Ding, Y., Shen, G., & Wan, W. (2024). Research on a multi-objective optimization method for transient flow oscillation in multi-stage pressurized pump stations.
Water, 16(12), 1728.
https://doi.org/10.3390/w16121728
Ergur, H. (2022). Multiple performance optimization of a single stage centrifugal pump using an intelligent system approach.
Journal of Applied Fluid Mechanics, 15, 1269-1280.
https://doi.org/10.47176/jafm.15.04.33040
Fu, Y., Gao, B., Ni, D., Zhang, W., & Fu, Y. (2023). Study on the influence of thermodynamic effects on the characteristics of liquid nitrogen cavitating flow around hydrofoils.
Symmetry, 15(10), 1946.
https://doi.org/10.3390/sym15101946
Guo, G., Lu, K., Xu, S., Yuan, J., Bai, T., Yang, K., & He, Z. (2023). Effects of in-nozzle liquid fuel vortex cavitation on characteristics of flow and spray: Numerical research.
International Communications in Heat and Mass Transfer, 148, 107040.
https://doi.org/10.1016/j.icheatmasstransfer.2023.107040
Han, M., Liu, Y., Wu, D., Zhao, X., & Tan, H. (2020). A numerical investigation in characteristics of flow force under cavitation state inside the water hydraulic poppet valves.
International Journal of Heat and Mass Transfer, 111, 1-16.
https://doi.org/10.1016/j.ijheatmasstransfer.2017.03.100
He, J., Liu, X., Li, B., Qiao, S., & Wang, S. (2023). Visual experiment of hydraulic control valve for distribution characteristics of cavitation flow field.
Research and Exploration in Laboratory, 42, 72-77.
https://link.cnki.net/doi/10.19927/j.cnki.syyt.2023.07.015
Homod, R. Z., Mohammed, H. I., & Abderrahmane, A. (2023). Deep clustering of Lagrangian trajectory for multi-task learning to energy saving in intelligent buildings using cooperative multi-agent.
Applied Energy, 351, 121843.
https://doi.org/10.1016/j.apenergy.2023.121843
Hou, Z., Wang, L., Yan, X., Wang, Z., & An, L. (2021). Study on characteristics of the cavitation bubble dynamics of lithium bromide aqueous solution with ultrasonic interaction.
Journal of Building Engineering, 44, 102424.
https://doi.org/10.1016/j.jobe.2021.102424
Hu, C., Wang, Q., Gong, W., & Yan, X. (2022). Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network.
Memetic Computing, 14, 211-223.
https://doi.org/10.1007/s12293-022-00366-9
Jia, X., Zhang, S., & Zhu, Z. (2024). Research on blade tip clearance cavitation and turbulent kinetic energy characteristics of axial flow pump based on the partially-averaged Navier-Stokes model.
Journal of Hydrodynamics, 36, 184-201.
https://doi.org/10.1007/s42241-024-0014-x
Li, G., Ding, X., Wu, Y., Wang, S., Li, D., Yu, W., Wang, X., Zhu, Y., & Guo, Y. (2022). Liquid-vapor two-phase flow in centrifugal pump: Cavitation, mass transfer, and impeller structure optimization.
Vacuum, 201, 111102.
https://doi.org/10.1016/j.vacuum.2022.111102
Li, S., Sheng, H., Zhang, W., & Zhao, Y. (2020). Cavitation characteristics and pressure pulsation response of piston type flow control valve
. Journal of Huazhong University of Science and Technology (Natural Science Edition), 48, 44-48+54.
https://link.cnki.net/doi/10.13245/j.hust.201208
Li, W., Li, S., Hou, J., Lei, Z., Aierken, T., & Wang, J. (2025). Numerical simulation of control valve flow characteristics based on DE-Bayesian modified turbulence model.
Journal of Building Engineering, 99, 111473.
https://doi.org/10.1016/j.jobe.2024.111473
Liu, J., Guan, X., & Yang, N. (2023). Bubble-induced turbulence in CFD simulation of bubble columns. Part I: Coupling of SIT and BIT.
Chemical Engineering Science, 270, 118528.
https://doi.org/10.1016/j.ces.2023.118528
Liu, R., Wang, M., Li, X., Liu, Y., Pei, C., & Gong, J. (2024). Effects of scaling criteria on modelling of multi-phase flow in the packed bed using coarse grain CFD-DEM.
Chemical Engineering Science, 296, 120244.
https://doi.org/10.1016/j.ces.2024.120244
Mashhadi, A., Sohankar, A., & Moradmand, M.M. (2024). Three-dimensional wake transition of rectangular cylinders and temporal prediction of flow patterns based on a machine learning algorithm.
Physics of Fluids, 36, 094138.
https://doi.org/10.1063/5.0225180
Mohsenabadi, S. E., Nistor, I., & Mohammadian, A. (2023). CFD modelling of initial stages of dam-break flow.
Canadian Journal of Civil Engineering, 50(10), 838-852.
https://doi.org/10.1139/cjce-2021-0493
Nadimi-Shahraki, M, H., Zamani, H., Varzaneh, Z., & Mirjalili, S. (2023). A Systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations.
Archives of Computational Methods in Engineering, 30, 4113-4159.
https://doi.org/10.1007/s11831-023-09928-7
Naik, D., & Naik, N. (2024). The changing landscape of machine learning: A comparative analysis of centralized machine learning, distributed machine learning and federated machine learning.
Advances in Computational Intelligence Systems, 1453, 18-28.
https://doi.org/10.1007/978-3-031-47508-5_2
Ou, G., Li, W., Xiao, D., Zheng, Z., Dou, H., & Wang, C. (2015). Numerical investigation on cavitation in pressure relief valve for coal liquefaction.
IOP Conference Series: Materials Science and Engineering, 72, 042039.
https://iopscience.iop.org/article/10.1088/1757-899X/72/4/042039
Ou, G., Wang, C., & Jin, H. (2024). Erosion characteristics and flashing flow of high-differential-pressure control valves: A numerical study using an erosion-coupled dynamic mesh.
Journal of Applied Fluid Mechanics, 17, 559-570.
https://doi.org/10.47176/jafm.17.3.2226
Park, S, H., Phan, T, H., Nguyen, V., Duy, T., Nguyen, Q., & Park, W. (2024). Numerical simulation of wall shear stress and boundary layer flow from jetting cavitation bubble on unheated and heated surfaces.
International Journal of Heat and Mass Transfer, 222, 125189.
https://doi.org/10.1016/j.ijheatmasstransfer.2024.125189
Park, S., Kim, J., Jeong, H, Y., Kim, T., & Yoo, J. (2023). C2RL: Convolutional-contrastive learning for reinforcement learning based on self-pretraining for strong augmentation.
Sensors, 23(10), 4946.
https://doi.org/10.3390/s23104946
Rabelo, S. N., Paulino, T. F., & Soares, C. P. M. (2023). Mass flow characteristics of CO2 operating in a transcritical cycle flowing through a needle expansion valve in a direct-expansion solar assisted heat pump.
Journal of Building Engineering, 67, 105963.
https://doi.org/10.1016/j.jobe.2023.105963
Ruetten, E., Leister, N., Karbstein, H, P., & Hakansson, A. (2023). Possibilities and limits of modeling cavitation in high-pressure homogenizers - a validation study.
Chemical Engineering Science, 283, 119405.
https://doi.org/10.1016/j.ces.2023.119405
Shijo, J. S., & Behera, N. (2023). Pressure drop prediction in fluidized dense phase pneumatic conveying using machine learning algorithms.
Journal of Applied Fluid Mechanics, 16, 1951-1961.
https://doi.org/10.47176/jafm.16.10.1869
Trilling, J., Schumacher, A., & Zhou, M. (2024). Reinforcement learning based agents for improving layouts of automotive crash structures.
Applied Intelligence, 54, 1751-1769.
https://doi.org/10.1007/s10489-024-05276-6
Wadood, A., Khan, B. S., & Albalawi, H. (2024). Design of the novel fractional order hybrid whale optimizer for thermal wind power generation systems with integration of chaos infused wind power.
Fractal and Fractional, 8(7), 379.
https://doi.org/10.3390/fractalfract8070379
Wang, Q., Wang, J., Zhuang, H., Liu, J., Jiang, B., Song, Y., & Zhang, K. (2024a). Acoustic mechanism and noise reduction optimization of globe valve in air conditioning system.
Journal of Building Engineering, 89, 109239.
https://doi.org/10.1016/j.jobe.2024.109239
Wang, X., Sun, Q., Wang, T., Ma, J., & Lv, W. (2024b). Study on cavitation noise monitoring and characteristics based on venturi bench test.
Journal of Theoretical and Computational Acoustics, 110819.
https://doi.org/10.1142/S2591728524500099
Wang, X., Wang, Y., Liu H., Xiao, Y., Jiang, L., & Li, M. (2023). A numerical investigation on energy characteristics of centrifugal pump for cavitation flow using entropy production theory.
International Journal of Heat and Mass Transfer, 201, 123591.
https://doi.org/10.1016/j.ijheatmasstransfer.2022.123591
Xia, Q., Xu, K., Li, M., Hu, K., Song, L., Song, Z., & Sun, N. (2024). Review of attention mechanisms in reinforcement learning.
Journal of Frontiers of Computer Science & Technology, 18, 1457-1475.
http://fcst.ceaj.org/CN/10.3778/j.issn.1673-9418.2312006
Xie, Y., Yuan, H., Li, T., Chen, E., & Ju, B. (2020). Research on improvement measures of fire protection capacity for UHVDC converter station.
High Voltage Apparatus, 56, 241-254.
https://doi.org/10.13296/j.1001-1609.hva.2020.01.036
Xin, X., Zhang, Z., Zhou, Y., Liu, Y., Wang, D., & Nan, S. (2024). A comprehensive review of predictive control strategies in heating, ventilation, and air-conditioning (HVAC): Model-free VS model.
Journal of Building Engineering, 94, 110013.
https://doi.org/10.1016/j.jobe.2024.110013
Yin, Q., Yu, T., Shen, S., Yang, J., Zhao, M., Ni, W., Huang, K., Liang, B., & Wang, L. (2024). Distributed deep reinforcement learning: A survey and a multi-player multi-agent learning toolbox.
Machine Intelligence Research, 21, 411-430.
https://doi.org/10.1007/s11633-023-1454-4
Zeng, N., Song, D., Li, H., Cheng, Y., & You, Y. (2021). Improved whale optimization algorithm and turbine disk structure optimization.
Journal of Mechanical Engineering, 57, 254-265.
https://api.semanticscholar.org/CorpusID:245941288
Zhang, G., Wu, X., Wu, Z., Zhang, H., Kim, H., & Lin, Z. (2024). 3-D numerical study of cavitation evolution through a butterfly valve model at different regulating conditions
. Journal of Applied Fluid Mechanics, 18, 332-347.
https://doi.org/10.47176/iafm.18.2.2629
Zhang, Y., Liu, Y., Liu, S., Gong, Z., & Wu, L. (2023). Transient analysis of a noise suppression method with aerating techniques in capillary tubes.
Thermal Science, 27, 4637-4649.
https://doi.org/10.2298/TSCI221127093Z
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023). Transfer learning in deep reinforcement learning: A survey.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 13344-13362.
https://ieeexplore.ieee.org/document/10172347