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International Journal of Fluid Mechanics Research
ESCI SJR: 0.206 SNIP: 0.446 CiteScore™: 0.9

ISSN Druckformat: 2152-5102
ISSN Online: 2152-5110

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International Journal of Fluid Mechanics Research

DOI: 10.1615/InterJFluidMechRes.2019021943
pages 59-69


Suman Debnath
Department of Mathematics, NIT Agartala, Jirania, Agartala, Tripura West, 799046, India
Anirban Banik
Department of Civil Engineering, NIT Agartala, Jirania, Agartala, Tripura West, 799046, India
Tarun Kanti Bandyopadhyay
Department of Chemical Engineering, NIT Agartala, Jirania, Agartala, Tripura West, 799046, India
Mrinmoy Majumder
Department of Civil Engineering, NIT Agartala, Jirania, Agartala, Tripura West, 799046, India
Apu Kumar Saha
Department of Mathematics, NIT Agartala, Jirania, Agartala, Tripura West, 799046, India


Studies of non-Newtonian pseudo-plastic liquid flow through bends are important as it is used in many chemical process industries like petroleum and refinery, pharmaceutical, rubber, paper pulp, and food industries, as a piping component for fluid flow transfer and heat transfer equipment in boiler, heat exchanger, distillation column, and air-crafts. In the concerned study, non-Newtonian pseudo-plastic SCMC solution (sodium salt of carboxy methyl cellulose solution) liquid flow through different types of angle of 0.0127 m diameter pipe bends has been investigated experimentally to optimize the frictional pressure drop across the bends in laminar and water flow in turbulent condition. The Group Method of Data Handling (GMDH) with multilayered neural network is used to predict and minimize the pressure drop. Pressure drop is minimized at the optimal concentration of the fluid and the bend angle. The GMDH model is validated against the validation techniques like Nash−Sutcliffe efficiency (NSE), percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), etc. It has been found that software-predicted data can be used for the trouble shooting in industry and in equipment design.


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