This book is based on my M. Tech work on prediction of flow patterns of gas-liquid flow in microchannel using probabilistic neural network (PNN). It also contains a brief description on PNN and steps to develop a PNN in MATLAB R2008a. The analytical models present in literature, to predict the gas-liquid flow patterns employs different physics involved. So, for each flow pattern there are separate models being developed. This present work is aim to develop a single PNN model for predicting the flow patterns. The advantage of using a PNN is the ability to predict without any detail knowledge and understanding of the physics involved.
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Mr. Seim Timung, Master of Technology: Studied Petroleum Refinery Engineering at Inian Institute of Technology Guwahati, India, under the supervision of Dr. Tapas K Mandal. Presently working as Research Scholar at IIT Guwahati, in the field of microfluidic technology under the supervision of Dr. Tapas K Mandal and Dr. Dipankar Bandyopadhyay
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is based on my M. Tech work on prediction of flow patterns of gas-liquid flow in microchannel using probabilistic neural network (PNN). It also contains a brief description on PNN and steps to develop a PNN in MATLAB R2008a. The analytical models present in literature, to predict the gas-liquid flow patterns employs different physics involved. So, for each flow pattern there are separate models being developed. This present work is aim to develop a single PNN model for predicting the flow patterns. The advantage of using a PNN is the ability to predict without any detail knowledge and understanding of the physics involved. 76 pp. Englisch. Codice articolo 9783659397745
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Timung SeimMr. Seim Timung, Master of Technology: Studied Petroleum Refinery Engineering at Inian Institute of Technology Guwahati, India, under the supervision of Dr. Tapas K Mandal. Presently working as Research Scholar at IIT Guwa. Codice articolo 5153589
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is based on my M. Tech work on prediction of flow patterns of gas-liquid flow in microchannel using probabilistic neural network (PNN). It also contains a brief description on PNN and steps to develop a PNN in MATLAB R2008a. The analytical models present in literature, to predict the gas-liquid flow patterns employs different physics involved. So, for each flow pattern there are separate models being developed. This present work is aim to develop a single PNN model for predicting the flow patterns. The advantage of using a PNN is the ability to predict without any detail knowledge and understanding of the physics involved. Codice articolo 9783659397745
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is based on my M. Tech work on prediction of flow patterns of gas-liquid flow in microchannel using probabilistic neural network (PNN). It also contains a brief description on PNN and steps to develop a PNN in MATLAB R2008a. The analytical models present in literature, to predict the gas-liquid flow patterns employs different physics involved. So, for each flow pattern there are separate models being developed. This present work is aim to develop a single PNN model for predicting the flow patterns. The advantage of using a PNN is the ability to predict without any detail knowledge and understanding of the physics involved.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Codice articolo 9783659397745
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Taschenbuch. Condizione: Neu. Identification of flow pattern in microchannel using ANN | Seim Timung (u. a.) | Taschenbuch | 76 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659397745 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Codice articolo 105933502
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