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International Journal of Fluid Mechanics Research

Publication de 6  numéros par an

ISSN Imprimer: 2152-5102

ISSN En ligne: 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

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Frictional Pressure Drop for Gas − Non-Newtonian Liquid Flow through 90° and 135° Circular Bend: Prediction Using Empirical Correlation and ANN

Volume 39, Numéro 5, 2012, pp. 416-437
DOI: 10.1615/InterJFluidMechRes.v39.i5.40
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RÉSUMÉ

Experiments have been carried out to determine the two-phase frictional pressure drop across 90° and 135° bend for gas-non-Newtonian liquid flow on the horizontal plane. Empirical correlation has been developed to predict the two-phase friction factor using the physical and dynamic variables of the system. The applicability of Artificial Neural Networks (ANN) methodology have also been reported. The ANN prediction have been reported using Multilayer Perceptrons (MLP) trained with five different algorithms, namely: Backpropagation (BP), Scaled Conjugate gradient (SCG), Delta-Bar-Delta (DBD), Levenberg−Marquardt (LM), Quick-Prop (QP). Four different transfer functions were used in a single hidden layer for all algorithms. The χ-square test confirms that the best network for prediction of frictional pressure drop is when it is trained with Backpropagation algorithm in the hidden and output layer with the transfer function 4 in hidden layer having 13 processing elements for 90° bend. The χ-square test also confirms that the best network for prediction of frictional pressure drop is when it is trained with Levenberg − Marquardt algorithm in the hidden and output layer with the transfer function 1 in hidden layer having 7 processing elements for 135° bend. Both the methods are equally predictive in nature but the empirical correlation is based on the physical and dynamic variables of the system, whereas the ANN prediction is not dependent on the individual relationship between the input variables.

CITÉ PAR
  1. Das Bimal, Ganguly Uma Prasad, Bar Nirjhar, Das Sudip Kumar, Holdup prediction in inverse fluidization using non-Newtonian pseudoplastic liquids: Empirical correlation and ANN modeling, Powder Technology, 273, 2015. Crossref

  2. Bar Nirjhar, Das Sudip Kumar, Applicability of ANN in Adsorptive Removal of Cd(II) from Aqueous Solution, in Handbook of Research on Natural Computing for Optimization Problems, 2016. Crossref

  3. Banerjee Munmun, Bar Nirjhar, Basu Ranjan Kumar, Das Sudip Kumar, Comparative study of adsorptive removal of Cr(VI) ion from aqueous solution in fixed bed column by peanut shell and almond shell using empirical models and ANN, Environmental Science and Pollution Research, 24, 11, 2017. Crossref

  4. Nag Soma, Mondal Abhijit, Bar Nirjhar, Das Sudip Kumar, Biosorption of chromium (VI) from aqueous solutions and ANN modelling, Environmental Science and Pollution Research, 24, 23, 2017. Crossref

  5. Banerjee Munmun, Bar Nirjhar, Basu Ranjan Kumar, Das Sudip Kumar, Removal of Cr(VI) from Its Aqueous Solution Using Green Adsorbent Pistachio Shell: a Fixed Bed Column Study and GA-ANN Modeling, Water Conservation Science and Engineering, 3, 1, 2018. Crossref

  6. Bar Nirjhar, Das Sudip Kumar, Modeling of Gas Holdup and Pressure Drop Using ANN for Gas-Non-Newtonian Liquid Flow in Vertical Pipe, Advanced Materials Research, 917, 2014. Crossref

  7. Nag Soma, Bar Nirjhar, Das Sudip Kumar, Sustainable bioremadiation of Cd(II) in fixed bed column using green adsorbents: Application of Kinetic models and GA-ANN technique, Environmental Technology & Innovation, 13, 2019. Crossref

  8. Ghosh Koushik, Bar Nirjhar, Biswas Asit Baran, Das Sudip Kumar, Removal of methylene blue (aq) using untreated and acid‐treated eucalyptus leaves and GA‐ANN modelling, The Canadian Journal of Chemical Engineering, 97, 11, 2019. Crossref

  9. Bar Nirjhar, Biswas Manindra Nath, Das Sudip Kumar, Flow Regime Prediction Using Artificial Neural Networks for Air-Water Flow Through 1–5 mm Tubes in Horizontal Plane, in Information Systems Design and Intelligent Applications, 339, 2015. Crossref

  10. Bar Nirjhar, Das Sudip Kumar, Biswas Manindra Nath, Prediction of Frictional Pressure Drop Using Artificial Neural Network for Air-water Flow through U-bends, Procedia Technology, 10, 2013. Crossref

  11. Singha Biswajit, Bar Nirjhar, Das Sudip Kumar, The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process, Journal of Molecular Liquids, 211, 2015. Crossref

  12. Bar Nirjhar, Das Sudip Kumar, Prediction of Flow Regime for Air-water Flow in Circular Micro Channels Using ANN, Procedia Technology, 10, 2013. Crossref

  13. Bhattacharya Samanwita, Bar Nirjhar, Rajbansi Baisali, Das Sudip Kumar, Adsorptive Elimination of Cu(II) from Aqueous Solution by Chitosan-nanoSiO2 Nanocomposite—Adsorption Study, MLR, and GA Modeling, Water, Air, & Soil Pollution, 232, 4, 2021. Crossref

  14. Maiti Samit Bikas, Bar Nirjhar, Das Sudip Kumar, Terminal settling velocity of solids in the pseudoplastic non-Newtonian liquid system – Experiment and ANN modeling, Chemical Engineering Journal Advances, 7, 2021. Crossref

  15. Bar Nirjhar, Das Sudip Kumar, Applicability of ANN in Adsorptive Removal of Cd(II) from Aqueous Solution, in Waste Management, 2020. Crossref

  16. Bar Nirjhar, Biswas Asit Baran, Das Sudip Kumar, A Comparative Study of Prediction of Gas Hold up Using ANN, in Computational Intelligence in Communications and Business Analytics, 1579, 2022. Crossref

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