Yazar "Erduran, Kutsi S." seçeneğine göre listele

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    3D model for prediction of flow profiles around bridges
    (TAYLOR & FRANCIS LTD, 2010) Kocaman, Selahattin; Seckin, Galip; Erduran, Kutsi S.
    Computational fluid dynamics models have become well established as tools for simulating free surface flow over a wide range of structures. This study is an assessment and comparison of the performance of a commercially available three-dimensional numerical software, which solves the Reynolds-averaged Navier-Stokes equations, to predict the free surface profiles from up-to downstream of four different bridge types with and without piers in a compound channel. The model results were compared with the available experimental data. Comparisons indicate that the model provides a reasonably good description of free surface profiles under both gradually and rapidly varied flow conditions in the bridge vicinity, respectively.
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    3D NUMERICAL MODELLING OF FLOW AROUND SKEWED BRIDGE CROSSING
    (HONG KONG POLYTECHNIC UNIV, DEPT CIVIL & STRUCTURAL ENG, 2012) Erduran, Kutsi S.; Seckin, Galip; Kocaman, Selahattin; Atabay, Serter
    This study investigates the performance of commercially available three-Dimensional (3D) numerical software, FLOW-3D, on the prediction of the water surface profiles using a series of experimental data obtained in a two stage channel with skewed bridge crossing. The experiments were carried out for four different types of bridge models with two different skew angles of phi = 30 degrees and phi = 45 degrees. FLOW-3D, which solves the Reynolds-averaged Navier - Stokes equations, was applied to experimental data for the prediction of water surface profiles along the compound channel from upstream to downstream. The comparison of free surface profiles of 3D model showed good agreement with the experimental data. Notably, the measured and computed afflux values are found to be almost identical.
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    An Experimental Investigation of Tsunami Bore Impact on Coastal Structures
    (Mdpi, 2024) Erduran, Kutsi S.; Akansu, Yahya E.; Unal, Ugur; Adekoya, Olusola O.
    This experimental study focused on the measurement and analysis of the impact force caused by a tsunami bore on a coastal structure. The bore wave was produced by a dam break mechanism. The water depth in the reservoir and the location of the coastal structures were varied to simulate different impact scenarios. The time history of the force resulting from the impact of the bore wave on the coastal structure was measured. The propagation of the bore wave along the flume was recorded and the video recordings were converted into digital data using an image-processing technique in order to determine the flow depth variations with time. The hydrostatic forces and the corresponding depth and time-averaged hydrodynamic forces as well as the maximum hydrodynamic forces were acquired for each scenario. The ratio of hydrodynamic to hydrostatic forces were obtained, and it was observed that the calculated averaged ratio was within the recommended design ratio. The results indicate that an increase in the reservoir level caused an increase in the magnitude and intensity of the impact forces, however, the relationship was non-linear. Moreover, it was found that the location of the structure did not play a significant role on the intensity of the impact forces.
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    Bridge afflux estimation using artificial intelligence systems
    (ICE PUBL, 2011) Seckin, Galip; Cobaner, Murat; Ozmen-Cagatay, Hatice; Atabay, Serter; Erduran, Kutsi S.
    Most of the methods developed for the prediction of bridge afflux are generally based on either energy or momentum equations. Recent studies have shown that the energy method, which is one of the four bridge subroutines within the commonly used program HEC-RAS for computing water surface profiles along rivers, produced more accurate results than three other methods (momentum, WSPRO and Yarnell's methods) when applied to bridge afflux data obtained from experiments conducted in a two-stage channel. This work developed three artificial intelligence models (the radial basis neural network, the multi-layer perceptron and the adaptive neuro-fuzzy inference system) as alternatives to the energy method. Multiple linear and multiple non-linear regression models were also used in the analysis. The results showed that the performance of the adaptive neuro fuzzy inference system in predicting bridge afflux was superior to the other models.
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    Experimental and numerical investigation of partial dam-break waves
    (Pergamon-Elsevier Science Ltd, 2024) Erduran, Kutsi S.; Unal, Ugur; Dokuz, Ahmet S. .
    In this study, the partial dam-break wave was experimentally and numerically investigated with regard to the wave speed and pattern for dry and wet downstream bed conditions at two different reservoir water levels. Experimental studies were carried out in a horizontal rectangular cross-section flume with dimensions of 6.0 x 1.2 x 0.5 m. The experiments were recorded and the video recordings were converted into digital data with the help of image processing technique. Hence, both 1 and 2 dimensional propagations of the flood shock wave and the water surface variations with time at the pre-determined points on the reservoir were able to be monitored. The exact gate opening time was acquired by the Arduino system. Flow-3D software and a 2D numerical model were used for the numerical studies. Comparisons were made between the numerical and experimental studies for 1D -2D partial dam-break wave propagations and the pointwise measurement. The satisfactory agreements in terms of the shock and rarefaction wave speed and pattern were observed. The results also indicated that the image processing technique was a promising technique to obtain the experimental outputs.
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    Prediction of backwater level of bridge constriction using an artificial neural network
    (ICE PUBLISHING, 2013) Atabay, Serter; Abdalla, Jamal A.; Erduran, Kutsi S.; Mortula, Maruf; Seckin, Galip
    Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (n(mc)) and of the floodplain (n(fp)), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of n(mc), n(fp), b and Q on the backwater level due to a bridge constriction without piers. It is concluded that n(mc) and Q have a more profound effect on the backwater level than does n(fp), while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.