Compared techniques for the critical submergence of an intake in water flow

Air-entraining vortex at intake is an important problem encountered in hydraulic engineering. Intake submergence depth could result in formation of the air-entraining free surface vortices. Unless the dangerous air entrainment is eliminated, air entraining causes mechanical damage, vibration in pipelines and loss of pump performance. The value of the intake's submergence when the vortex starts entraining air is known as ''critical submergence''. In this study, the critical submergence for a circular intake pipe in still-water and open channel flow for permeable and impermeable bottom was investigated. Experimental results were used to compare with critical spherical sink surface (CSSS), radial basis function based neural network (RBNN) and general linear model (GLM). The CSSS has the same center and discharge as the intake with the critical submergence. The GLM underlies most of the statistical analyses that are used in applied research. And the RBNN is one of the most used network models. The ranking of prediction on critical submergence is obtained as RBNN, GLM and CSSS, respectively.

[1]  Fikret Kocabaş,et al.  Estimation of critical submergence for an intake in a stratified fluid media by neuro-fuzzy approach , 2006 .

[2]  Nevzat Yildirim,et al.  Prediction of critical submergence for an intake pipe , 2002 .

[3]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[4]  Fikret Kocabas,et al.  A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom , 2008 .

[5]  O. Kisi.  The potential of different ANN techniques in evapotranspiration modelling , 2008 .

[6]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[7]  Nevzat Yildirim,et al.  Flow-Boundary Effects on Critical Submergence of Intake Pipe , 2000 .

[8]  Nevzat Yildirim,et al.  Critical Submergence for Intakes in Open Channel Flow , 1995 .

[9]  Soon Heung Chang,et al.  Radial basis function networks applied to DNBR calculation in digital core protection systems , 2003 .

[10]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[11]  A. Jacob Odgaard,et al.  Free‐Surface Air Core Vortex , 1986 .

[12]  Lyle H. Ungar,et al.  Using radial basis functions to approximate a function and its error bounds , 1992, IEEE Trans. Neural Networks.

[13]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[15]  Nevzat Yildirim,et al.  Critical Submergence for Intakes in Still-Water Reservoir , 1998 .

[16]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[17]  Fikret Kocabaş,et al.  An artificial neural network model for the prediction of critical submergence for intake in a stratified fluid medium , 2009 .

[18]  D. F. Denny,et al.  An Experimental Study of Air-Entraining Vortices in Pump Sumps: , 1956 .

[19]  Nevzat Yildirim.  Critical submergence for a rectangular intake , 2004 .

[20]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..