Encapsulation of air vessel design in a neural network

• 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
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2005.11.010

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.