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International Journal of Fluid Mechanics Research

Publicado 6 números por año

ISSN Imprimir: 2152-5102

ISSN En Línea: 2152-5110

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.1 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0002 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.33 SJR: 0.256 SNIP: 0.49 CiteScore™:: 2.4 H-Index: 23

Indexed in

ESTIMATION OF PRESSURE DROP FOR NON-NEWTONIAN LIQUID FLOW THROUGH BENDS USING ADAPTIVE NON-PARAMETRIC MODEL

Volumen 47, Edición 1, 2020, pp. 59-69
DOI: 10.1615/InterJFluidMechRes.2019021943
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SINOPSIS

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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CITADO POR
  1. Xie Le, He Guangwen, Yu Bin, Yan Shaowei, Evaluation of the mixing quality of high-viscosity yield stress fluids in a tubular reactor, International Journal of Chemical Reactor Engineering, 19, 6, 2021. Crossref

  2. Banik Anirban, Majumder Mrinmoy, Biswal Sushant Kumar, Bandyopadhyay Tarun Kanti, Polynomial neural network-based group method of data handling algorithm coupled with modified particle swarm optimization to predict permeate flux (%) of rectangular sheet-shaped membrane, Chemical Papers, 76, 1, 2022. Crossref

  3. Banik Anirban, Bandyopadhyay Tarun Kanti, Biswal Sushant Kumar, Panchenko Vladimir, Garhwal Sunil, Comparative Performance Assessment of Multi Linear Regression and Artificial Neural Network for Prediction of Permeate Flux of Disc Shaped Membrane, in Intelligent Computing & Optimization, 569, 2023. Crossref

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