B. Yildiz
Please Note
9 records found
1
Compound weirs have been used as adjustable structures to divert flow, for example, through river branches at the river bifurcations. For this purpose, a wide variety of weir configurations can be used including asymmetric configurations that have not been studied in the literature yet. A proper one-dimensional representation of flow over these structures is needed as the effect they have on the river are generally added as subgrid energy losses to the river hydrodynamic models. In this study, an experimental study was conducted to estimate the correct representation of compound weirs at varying weir configurations and flow conditions. In the experimental campaign, eight weir configurations were used with six discharge values. Upstream flow depths at each case were recorded and their relationship with the flow rate and weir configuration was analyzed. A 1D model was proposed to estimate flow rates when the upstream flow depths are known. The proposed correction to the well-known Kindsvater and Carter approach was applied to modify the discharge coefficient when nonuniform geometries are used that cause horizontal flow contraction. To estimate and validate the proposed correction, additional numerical simulations using computational fluid dynamics (CFD) were conducted to estimate the detailed flow field upstream of the nonuniform weirs. Surface particle image velocimetry (SPIV) measurements were also conducted to validate the CFD model. The corrected 1D model predicted the flow rates at 48 cases covering uniform to highly nonuniform weir geometries with a maximum of 9.7% and a mean of 2.45% deviation from the measurements. Additional tests on the performance of the proposed model validated its effectiveness in various nonuniform geometries at low flows. However, when substantial changes are made to the geometry, such as the removal of buttresses, the model may require calibration to maintain its accuracy.
The shape of a pendant drop is studied by employing free energy minimization. This free energy includes the gravitational potential energy and the interfacial surface energy. We employed the Lagrange multipliers method to minimize free energy while maintaining drop volume as constant. The differential equation for the shape of any pendant drop was established as a function of one dimensionless parameter only. This novel dimensionless parameter is defined as the shape factor. Around the origin of the chosen coordinate axis, an analytical solution to the differential equation was found. For a general solution, a numerical approach was followed to estimate drop shape. Furthermore, we calculated the detached volume from the bulk pendant drop. Comparison of the results with the experimental findings shows good agreements. A new Axisymmetric Drop Shape Analysis method is suggested, which can help users estimate any unknown of the problem if one geometrical data of the drop is known.
This study aims to search for optimum design parameters for a slurry pipeline problem and optimum operation parameters for a multi-reservoir scheduling problem by using Bi-Attempted Base Optimization Algorithm (ABaOA), which has been recently developed as a numerical bidirectional search algorithm. The slurry pipeline problem is a constrained non-linear cost minimization problem with constraints on facility capacities. It has two separate cost terms that behave differently with changes in decision variables. The problem includes several decision variables in addition to the fact that the objective function is highly non-linear. On the other hand, the multi-reservoir problem is a well-known problem in Hydraulics that aims to maximize benefit by optimizing the releases of each reservoir. The problem has a known global optimum, which is used to test the abilities of the ABaOA. The ABaOA is developed from Base Optimization Algorithm (BaOA) by transforming its operators with the aim to diversify the search paths to reach the global optimum. Its applications in hydrosystems show that it converges to the optimum solutions in reasonable times. The results from the first application are compared to the ones obtained from Genetic Algorithms (GA) application. It is observed that ABaOA outperformed GA in terms of speed of convergence and finding a better alternative solution. The ABaOA reaches the global optimum in the second application. In addition, alternatives with better benefit functions, including some penalties have been determined.