Open Channel Bend Simulation

1- Gholami A., Bonakdari H., Zaji A. H., Ajeel Fenjan S., Akhatri A. A., (2017) New radial basis function network method based on decision trees to predict flow variables in a curved channel. Neural Computing and Applications, DOI: 10.1007/s00521-017-2875-1.

 

2- Gholami A., Bonakdari H., Zaji A. H., Ajeel Fenjan S., Akhatri A. A., (2016), Developing finite volume method (FVM) in numerical simulation of flow pattern in 60° open channel bend, Journal of Applied Research in Water and Wastewater. Vol. 5, pp. 193-200.

 

3- Gholami A., Bonakdari H., Zaji A. H., Michelson D. G., Akhatri A. A., (2016), Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90◦ bend, Applied Soft Computing, Vol. 48, pp. 563–583.

 

4- A., Bonakdari H., Zaji A. H., Ajeel Fenjan S., Akhatri A. A., (2016), Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in a 90° open channel bends. Engineering Applications of Computational Fluid Mechanics, Vol. 10, No. 1, pp. 194-209. DOI: 10.1080/19942060.2015.1128358.

 

5- Gholami A., Bonakdari H., Zaji A. H., Akhtari A.A., (2015), Simulation of open channel bend characteristics using computational fluid dynamics and artificial neural network, Engineering Application of Computational Fluid Mechanics Journal, DOI:10.1080/19942060.2015.1033808.

 

6- Gholami A., Bonakdari H., Zaji A. H., Akhtari A. A., Khodashenas S. R., (2015), Predicting the velocity field in a 90 open channel bend using a gene expression programming model, Flow Measurement and Instrumentation, Vol. 46, Part A, pp. 189-192, DOI: 10.1016/j.flowmeasinst.2015.10.006.