Encapsulation of air vessel design in a neural network

The trial and error process of calculating the characteristics of an air vessel suitable to protect a rising main against the effects of hydraulic transients has proved to be cumbersome for the design engineer. The own experience and sets of charts, which can be found in the literature, can provide...

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Published in:	Applied mathematical modelling Vol. 30; no. 5; pp. 395 - 405
Main Authors:	Izquierdo, J., López, P.A., López, G., Martínez, F.J., Pérez, R.
Format:	Journal Article
Language:	English
Published:	New York, NY Elsevier Inc 01-05-2006 Elsevier Science
Subjects:	Applied sciences Compressible flows; shock and detonation phenomena Exact sciences and technology Flows in ducts, channels, nozzles, and conduits Fluid dynamics Function approximation Fundamental areas of phenomenology (including applications) Hydraulic transients Mechanical engineering. Machine design Neural networks Physics Pipings, valves, fittings Shock-wave interactions and shock effects Steel design Water hammer protection techniques Hydraulic transients Function approximation Neural networks Water hammer protection techniques Encapsulation Transient response Water hammer Hydroelasticity Protection system Piping Neural network Modeling
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Abstract	The trial and error process of calculating the characteristics of an air vessel suitable to protect a rising main against the effects of hydraulic transients has proved to be cumbersome for the design engineer. The own experience and sets of charts, which can be found in the literature, can provide some help. The aim of this paper is to present a neural network allowing instantaneous and direct calculation of air and vessel volumes from the system parameters. This neural network has been implemented in the hydraulic transient simulation package DYAGATS.
AbstractList	The trial and error process of calculating the characteristics of an air vessel suitable to protect a rising main against the effects of hydraulic transients has proved to be cumbersome for the design engineer. The own experience and sets of charts, which can be found in the literature, can provide some help. The aim of this paper is to present a neural network allowing instantaneous and direct calculation of air and vessel volumes from the system parameters. This neural network has been implemented in the hydraulic transient simulation package DYAGATS.
Author	Martínez, F.J. Izquierdo, J. López, G. López, P.A. Pérez, R.
Author_xml	– sequence: 1 givenname: J. surname: Izquierdo fullname: Izquierdo, J. email: jizquier@gmmf.upv.es – sequence: 2 givenname: P.A. surname: López fullname: López, P.A. – sequence: 3 givenname: G. surname: López fullname: López, G. – sequence: 4 givenname: F.J. surname: Martínez fullname: Martínez, F.J. – sequence: 5 givenname: R. surname: Pérez fullname: Pérez, R.
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Keywords	Hydraulic transients Function approximation Neural networks Water hammer protection techniques Encapsulation Transient response Water hammer Hydroelasticity Protection system Piping Neural network Modeling
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SubjectTerms	Applied sciences Compressible flows; shock and detonation phenomena Exact sciences and technology Flows in ducts, channels, nozzles, and conduits Fluid dynamics Function approximation Fundamental areas of phenomenology (including applications) Hydraulic transients Mechanical engineering. Machine design Neural networks Physics Pipings, valves, fittings Shock-wave interactions and shock effects Steel design Water hammer protection techniques
Title	Encapsulation of air vessel design in a neural network
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