An artificial neural network model for the prediction of critical submergence for intake in a stratified fluid medium


Kocabas F., Kisi O., Ardiclioglu M.

CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS, vol.26, no.4, pp.367-375, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 4
  • Publication Date: 2009
  • Doi Number: 10.1080/10286600802200130
  • Title of Journal : CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS
  • Page Numbers: pp.367-375

Abstract

Density differences may occur because of temperature differentials, suspended sediments, dissolved salts or other chemicals. Most of the large surface reservoirs are stably stratified throughout most, or all, of the year. One of the means of assisting the management is to allow a selective withdrawal from the reservoir. And while an intake is used for withdrawal (from the lower layer), a maximum discharge is required not allowing the uptake of the upper layer fluids. The value of the intake's vertical distance from the upper layer elevation (submergence) when the upper layer fluids begin to be drawn into the intake is known as 'critical submergence'. In this study, the critical submergence for a circular intake pipe in a stratified body (which has different layer thickness) is investigated. Experiments were conducted on a vertically flowing downward intake pipe in a still-water reservoir. Artificial neural network (ANN) models and formulas, which are found by the theoretical analysis of critical spherical sink surface (CSSS), are used for the analysis of experimental results. The CSSS has the same centre and discharge as the intake. The ANN model and CSSS results are compared with the experimental results.