Abstract: In recent years a great deal of research effort on ship hydrodynamics have been devoted to practical navigation problems in moving planing vessels safety in rivers and confined water. The important point of any analysis is the investigation of ship hydrodynamics in shallow water conditions. The purpose of this study is to investigate the resistance of a planing vessel model 4667-1 by using CFD software based on finite volume method to solve the RANS equations in different speed and depths including deep and shallow water conditions. Also the wave pattern and flow field around the vessel is investigated. For validating the method, at first the resistance results in deep water are compared with the experimental data and show good agreements. Simulations are performed in transient mode, using Volume of Fluid (VOF) and k -ε schemes to model the free surface turbulent flow. The results have shown that by decreasing the depth, the shallow water resistance of a planing vessel will be increase

• Publication Date: 29-Dec-2015
• DOI: 10.15224/978-1-63248-083-5-62
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Abstract: The use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of hydrology and water resources problems. In this research, an ANN was developed and used to model the snowmelt runoff, in a catchment located in a semiarid climate in Turkey. The multilayer perceptron (MLP) neural network was chosen for use in the current study. The one year data (2009) obtained from the stations, located in Erzurum Kırkgöze (Çipak) basin, are integrated into daily average time series of temperature (T), solar radiation (R), snow-covered area (S), snow water equivalent (SWE), runoff coefficient for snow (Cs). The results indicate that the artificial neural network method is suitable to predict the river discharges by using some variables and parameters of snowmelt for the Kırkgöze Basin.

• Publication Date: 29-Dec-2015
• DOI: 10.15224/978-1-63248-083-5-63
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