Simulations of the Concentration Fields of Rosette-Type Multiport Buoyant Discharges Using Combined CFD and Multigene Genetic Programming Techniques

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@Article{yan:2021:JMSE,

• author = "Xiaohui Yan and Yan Wang and Abdolmajid Mohammadian and Jianwei Liu",
• title = "Simulations of the Concentration Fields of {Rosette-Type} Multiport Buoyant Discharges Using Combined {CFD} and Multigene Genetic Programming Techniques",
• journal = "Journal of Marine Science and Engineering",
• year = "2021",
• volume = "9",
• number = "11",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2077-1312",
• URL = "https://www.mdpi.com/2077-1312/9/11/1311",
• DOI = "doi:10.3390/jmse9111311",
• abstract = "Rosette-type diffusers are becoming popular nowadays for discharging wastewater effluents. Effluents are known as buoyant jets if they have a lower density than the receiving water, and they are often used for municipal and desalination purposes. These buoyant effluents discharged from rosette-type diffusers are known as rosette-type multiport buoyant discharges. Investigating the mixing properties of these effluents is important for environmental impact assessment and optimal design of the diffusers. Due to the complex mixing and interacting processes, most of the traditional simple methods for studying free single jets become invalid for rosette-type multiport buoyant discharges. Three-dimensional computational fluid dynamics (3D CFD) techniques can satisfactorily model the concentration fields of rosette-type multiport buoyant discharges, but these techniques are typically computationally expensive. In this study, a new technique of simulating rosette-type multiport buoyant discharges using combined 3D CFD and multigene genetic programming (MGGP) techniques is developed. Modeling the concentration fields of rosette-type multiport buoyant discharges using the proposed approach has rarely been reported previously. A validated numerical model is used to carry out extensive simulations, and the generated dataset is used to train and test MGGP-based models. The study demonstrates that the proposed method can provide reasonable predictions and can significantly improve the prediction efficiency.",
• notes = "also known as \cite{jmse9111311}",

}

Genetic Programming entries for Xiaohui Yan Yan Wang Abdolmajid Mohammadian Jianwei Liu

Citations