Documents disponibles écrits par cet auteur

Prediction of pressure drop using artificial neural network for gas non - newtonian liquid flow through piping components / Nirjhar Bar in Industrial & engineering chemistry research, Vol. 49 N° 19 (Octobre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 19 (Octobre 2010) . - pp. 9423–9429 Titre : Prediction of pressure drop using artificial neural network for gas non - newtonian liquid flow through piping components Type de document : texte imprimé Auteurs : Nirjhar Bar, Auteur ; Manindra Nath Biswas, Auteur ; Sudip Kumar Das, Auteur Année de publication : 2010 Article en page(s) : pp. 9423–9429 Langues : Anglais (eng) Mots-clés : Gas  Non  Newtonian  liquid  flow Résumé : The ANN approach proved its worth when rigorous fluid mechanics treatment based on the solution of first principle equations is not tractable. Evaluation and prediction of the frictional pressure drop across different piping components such as orifices, gate and globe valves, elbows, and horizontal pipe in 0.0127 m diameter for gas non-Newtonian liquid flow is manifested in this paper. In this paper, we have used the power-law-model (Oswald−de Waele model) liquids only. The experimental data used for the prediction is taken from our earlier work, Bandyopadhyay (Bandyopadhyay, T. K. Studies on non-Newtonian and gas-non-Newtonian liquid flow through horizontal tube and piping components. Ph.D Thesis, University of Calcutta, Kolkata, India, 2002) and the subsequent publications (Banerjee T. K.; Das, S. K. Gas-non-Newtonian liquid flow through globe and gate valves. Chem. Eng. Commun. 1998, 167, 133−146. Samanta A. K.; Banerjee, T. K.; Das, S. K. Pressure loses in orifices for the flow of gas-non-Newtonian liquids. Can. J. Chem. Eng. 1999, 77, 579−583. Bandyopadhyay, T. K.; Banerjee, T. K.; Das, S. K. Gas-non-Newtonian liquid flow through elbows. Chem. Eng. Commun. 2000, 82, 21−33). The proposed approach toward the prediction is done using a multilayer perceptron (MLP with one hidden layer and four different transfer functions), which is trained with backpropagation algorithm. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1007739 [article] Prediction of pressure drop using artificial neural network for gas non - newtonian liquid flow through piping components [texte imprimé] / Nirjhar Bar, Auteur ; Manindra Nath Biswas, Auteur ; Sudip Kumar Das, Auteur . - 2010 . - pp. 9423–9429. Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 19 (Octobre 2010) . - pp. 9423–9429 Mots-clés : Gas  Non  Newtonian  liquid  flow Résumé : The ANN approach proved its worth when rigorous fluid mechanics treatment based on the solution of first principle equations is not tractable. Evaluation and prediction of the frictional pressure drop across different piping components such as orifices, gate and globe valves, elbows, and horizontal pipe in 0.0127 m diameter for gas non-Newtonian liquid flow is manifested in this paper. In this paper, we have used the power-law-model (Oswald−de Waele model) liquids only. The experimental data used for the prediction is taken from our earlier work, Bandyopadhyay (Bandyopadhyay, T. K. Studies on non-Newtonian and gas-non-Newtonian liquid flow through horizontal tube and piping components. Ph.D Thesis, University of Calcutta, Kolkata, India, 2002) and the subsequent publications (Banerjee T. K.; Das, S. K. Gas-non-Newtonian liquid flow through globe and gate valves. Chem. Eng. Commun. 1998, 167, 133−146. Samanta A. K.; Banerjee, T. K.; Das, S. K. Pressure loses in orifices for the flow of gas-non-Newtonian liquids. Can. J. Chem. Eng. 1999, 77, 579−583. Bandyopadhyay, T. K.; Banerjee, T. K.; Das, S. K. Gas-non-Newtonian liquid flow through elbows. Chem. Eng. Commun. 2000, 82, 21−33). The proposed approach toward the prediction is done using a multilayer perceptron (MLP with one hidden layer and four different transfer functions), which is trained with backpropagation algorithm. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1007739