Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
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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.