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,
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author = "Xiaohui Yan and Yan Wang and
Abdolmajid Mohammadian and Jianwei Liu",
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title = "Simulations of the Concentration Fields of
{Rosette-Type} Multiport Buoyant Discharges Using
Combined {CFD} and Multigene Genetic Programming
Techniques",
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journal = "Journal of Marine Science and Engineering",
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year = "2021",
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volume = "9",
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number = "11",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2077-1312",
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URL = "https://www.mdpi.com/2077-1312/9/11/1311",
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DOI = "doi:10.3390/jmse9111311",
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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.",
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notes = "also known as \cite{jmse9111311}",
- }
Genetic Programming entries for
Xiaohui Yan
Yan Wang
Abdolmajid Mohammadian
Jianwei Liu
Citations