Volume No. 5 Issue No.: 4 Page No.: 960-971 April-June 2011

 

PREDICTING JET HYDRAULIC PROPERTIES OF IN CIRCULAR BUOYANT JETS FOR SEWAGE EMISSION IN THE STATIC AMBIENT FLOW USING ANN (ARTIFICIAL NEURAL NETWORK)

 

S. A. Dastgheib*1, S. H. Musavi Jahromi2 and S. Samimi3

1. Scientific and Research Unit, Islamic Azad University, (Iran)
2. Shahid Chamran University (SCU), Ahwaz (Iran)
3. University of Tehran (Iran)

 

Received on : February 09, 2011

 

ABSTRACT

 

This paper presents an artificial intelligence approach for predicting jet hydraulic properties of in circular buoyant jets for sewage emission in the static ambient flow. In many cases due to high concentrations of pollutants, or critical toxicity or even very high temperature wastewaters from nuclear reactors, inevitably dilution of wastewater to reach the limit concentration should be done in shortest time. One way to make quick dilution is using submerged jets in rivers or seas that can relatively in short period dilute large amounts of pollution due to its mixture of high turbulence conditions and the destructive effects will rapidly reduce. Jet behaviors are very important in the environmental field and real ambient flow. Characteristics of jet diffusion, mixing length, distribution of concentration and jet core velocity and trajectory of jet are variables that should be considered in the buoyant jets. The flow of jet is heavily dependent on the velocity, as well as on the geometry of diffuser and the physical properties of ambient flow. In this research, drawdown trajectory of jet has been investigated. To achieve goals of this research program 4 input factors has been chosen contains, relative length of jet trajectory, X/dp, which X is length of positive buoyancy and dp is port diameter, jet convergence angel θc, geometry number of jet Di/dp, Densimetric Froude Number Frd..the output is realetive height of jet trajectory Z/dp.192 run of programs has benn taken by Qnet2000 (software for neural network modeling) for upper and lower boundaries. Thus two values for Z direction were predicted. Findings show that the the model can predict upper and lower boundaries with RMSE respectively equal to 0.0401and 0.0382 in training mood, and 0.0435 and 0.0454 in test mood.

 

Keywords : Submerged jet, Trajectory, Sewage emission, Artificial Neural Network (ANN), States, Ambent

 

 

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