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Öğe 3-D Numerical Simulation of a Real Dam Reservoir: Thermal Stratified Flow(Springer-Verlag Singapore Pte Ltd, 2016) Unes, Fatih; Demirci, Mustafa; Varcin, Hakan[Abstract Not Available]Öğe DAM RESERVOIR LEVEL MODELING BY NEURAL NETWORK APPROACH: A CASE STUDY(Acad Sciences Czech Republic, Inst Computer Science, 2010) Unes, FatihPrediction of reservoir level fluctuation is important in the operation, design, and security of dams. In this paper, Artificial Neural Networks (ANN) is used for modeling. In such modeling approaches, it is possible to determine darn reservoir level and water balance (budget) by taking the monthly average precipitation and needed parameters into consideration. The basic data are available for over 29 years at the Tahtakopru Dam in the southeast Mediterranean region of Turkey. As a sub-approach of ANN, a multi layer perceptron (MLP) is used. Bayesian regularization back-propagation training algorithm is employed for optimization of the network. MLP results are compared with the results of conventional multiple linear regression (MLR) and autoregressive (AR) models. The comparison shows that the ANN model provides better performance than the mentioned models in reservoir level estimation.Öğe Experimental investigation of cross-shore sandbar volumes(Springer, 2014) Demirci, Mustafa; Akoz, M. Sami; Unes, FatihSandbars are critical to the cross-shore movement of sediment. Prediction of cross-shore sandbar volumes requires knowledge about the functional relationship of sediment transport rate conditions with waves, currents, base slope, sediment property and water depth. In this study, experiments on cross- shore sediment transport were carried out in a laboratory wave channel for initial base slopes of 1/8, 1/10 and 1/15. Using regular waves with different deep-water wave steepness generated by a pedal-type wave generator, bar volumes caused by cross-shore sediment transport are investigated for beach materials with the medium diameter of d(50) = 0.25, 0.32, 0.45, 0.62 and 0.80 mm. A non-dimensional equation for sandbar volume was obtained by using linear and non-linear regression methods through the experimental data and was compared with previously developed equations in the literature. The results have shown that the experimental data fitted well to the proposed equation with respect to the previously developed equations.Öğe Investigation of density flow in dam reservoirs using a three-dimensional mathematical model including Coriolis effect(Pergamon-Elsevier Science Ltd, 2008) Unes, FatihThis paper aims to simulate and discuss the propagation of density current and divergence flow in a dam reservoir. The density plunging flow is modeled in three-dimensions through a dam reservoir with diverging and sloping bottom channels, and the plunging phenomenon has been reproduced in the present model. Nonlinear and unsteady continuity, momentum, energy and turbulence model equations are formulated in the Cartesian coordinates both in a sloping and in a diverging channel. For the turbulence viscosity, a k-epsilon turbulence model including buoyancy effects is used to reproduce the main flow characteristics. To investigate the Coriolis force effect oil the density flow in a dam reservoir, Coriolis force parameter is also included in the governing equations. The equations of the model are solved based oil the initial and boundary conditions of the dam reservoir flow for a range of bottom slopes and divergence angles. In this paper the main interest is the formation of separated flows, such as wall-jet and free-jet flows. The model successfully simulates the formation of attached flow, wall jets, and free jets in a negatively buoyant environment. The simulation results obtained from this study are compared with previous experimental work, and the mathematical model studies data oil density current generated by the plunging of cold water in ambient warm water in a diverging channel, and is found to be of the same magnitude as the experimental measurements and followed the expected basic trend. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Investigation of Plunging Depth and Density Currents in Egrekkaya Dam Reservoir(Turkish Chamber Civil Engineers, 2012) Unes, Fatih; Varcin, HakanFresh water sources are dwindling and becoming contaminated throughout the world due to environmental problems and fast growing population. Therefore, definition of reservoirs, reservoir flows, efficient use and proper modeling of fresh water sources gain great importance. In the real dam reservoirs, the density differences between inflow river water and ambient darn reservoir water create stratified flows. The density differences can be due to the discrepancies in temperatures, concentration of dissolved or suspended substances or a combination of both. In this study, a mathematical model is used for modeling reservoir flow and determining the plunging point and depth. The model consists of nonlinear and unsteady continuity, momentum, energy and k-epsilon turbulence model equations. The equations are constructed on actual dimensions, shape, boundary and initial conditions of the Egrekkaya dam. The model successfully simulates the formation of density currents and plunging flow. The results are found to be in accordance with the actual measured values. The results of this study are important with regard to sedimentation studies, water quality modeling and management and habitat assessment in reservoirs.Öğe Investigation of seasonal thermal flow in a real dam reservoir using 3-D numerical modeling(Veda, Slovak Acad Sciences, 2015) Unes, Fatih; Varcin, HakanInvestigations indicate that correct estimation of seasonal thermal stratification in a dam reservoir is very important for the dam reservoir water quality modeling and water management problems. The main aim of this study is to develop a hydrodynamics model of an actual dam reservoir in three dimensions for simulating a real dam reservoir flows for different seasons. The model is developed using nonlinear and unsteady continuity, momentum, energy and k-epsilon turbulence model equations. In order to include the Coriolis force effect on the flow in a dam reservoir, Coriolis force parameter is also added the model equations. Those equations are constructed using actual dimensions, shape, boundary and initial conditions of the dam and reservoir. Temperature profiles and flow visualizations are used to evaluate flow conditions in the reservoir. Reservoir flow's process and parameters are determined all over the reservoir. The mathematical model developed is capable of simulating the flow and thermal characteristics of the reservoir system for seasonal heat exchanges. Model simulations results obtained are compared with field measurements obtained from gauging stations for flows in different seasons. The results show a good agreement with the field measurements.Öğe Plunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Technique(Springer, 2015) Unes, Fatih; Joksimovic, Darko; Kisi, OzgurThe cold river water inflow often plunges below the ambient dam reservoir water and becomes density underflow through the reservoir. The hydrodynamics of density currents and plunging are difficult to study in the natural environment and laboratory condition due to small-scale, entrainment and turbulent flows. Numerical modeling of plunging flow and defining of the plunging depth can provide valuable insights for the dam reservoir sedimentation and water quality problem. In this study, an adaptive neuro-fuzzy (NF) approach is proposed to estimate plunging flow depth in dam reservoir. The results of the NF model are compared with two-dimensional hydrodynamic model, artificial neural network (ANN), and multi linear regression (MLR) model results. The two-dimensional model is adapted to simulate density plunging flow simulation through a reservoir with sloping bottom. The model is developed using nonlinear and unsteady continuity, momentum, energy and k-epsilon turbulence model equations in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results. Mean square errors (MSE), mean absolute errors (MAE) and determination coefficient (R-2) statistics are used as comparing criteria for the evaluation of the models' performances. The NF model approach for the data yields the small MSE (1.18 cm), MAE (0.86 cm), and high determination coefficient (0.95-0.98). Based on the comparisons, it was found that the NF computing technique performs better than the other models in plunging flow depth estimation for the particular data sets used in this study.Öğe Prediction of cross-shore sandbar volumes using neural network approach(Springer Japan Kk, 2015) Demirci, Mustafa; Unes, Fatih; Akoz, M. SamiCorrect estimation of bar volumes, wave height, wave period and median sediment diameter is crucial for the designing of coastal structures and water quality problem. In this study, bar volumes caused by cross-shore sediment transport were investigated using a physical model and obtained 64 experimental data considering the wave steepness (H (0)/L (0)) and period (T), the bed slope (m) and the sediment diameter (d (50)). Artificial neural network (ANN) and multi-linear regression (MLR) are used for predicting the bar volumes. A multi layer perceptron is used as the ANN structure. The results show that the ANN model estimates are much closer to the experimental data than the MLR model estimates.Öğe Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach(Wiley, 2010) Unes, FatihExperimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi-linear regression (MLR), and two-dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two-dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k-epsilon turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.Öğe Prediction of Millers Ferry Dam Reservoir Level in USA Using Artificial Neural Network(Budapest Univ Technology Economics, 2015) Unes, Fatih; Demirci, Mustafa; Kisi, OzgurReservoir level modeling is important for the operation of dam reservoir, design of hydraulic structures, determining pollution in reservoir and the safety of dam. In this study, daily reservoir levels for Millers Ferry Dam on the Alabama River in USA were predicted using artificial neural networks (ANN). Bayesian regularization backpropagation training algorithm is employed for optimization of the network. The results of the optimal ANN models were compared with conventional auto-regressive models (AR), auto-regressive moving average (ARMA), multi-linear regression (MLR) models. The models are compared with each other according to the three criteria, namely, mean square errors, mean absolute relative error and correlation coefficient. The comparison results show that the ANN models perform better than the conventional models.
