Browsing by Author "Aksoy, Muharrem Hilmi"

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Experimental Investigation of Flow Past Circular Cylinders with Dimpled and Protruded Surface Modifications Using PIV
(Elsevier Sci Ltd, 2026) Kurtulmus, Nazim; Ispir, Murat; Aksoy, Muharrem Hilmi; Goktepeli, Ilker
This study examines the impact of surface modifications, including dimpled/protruded formations, on the flow characteristics of circular cylinders in free-stream flow, aiming to enhance passive flow control. Experiments have been conducted in an open-water channel using a Particle Image Velocimetry (PIV) system to obtain detailed velocity field data and turbulent statistics. Circular cylinders with in-line dimple/protrusion arrangements have been fabricated via 3D printing and their performance was evaluated at Reynolds numbers of Re = 4000 and Re = 6000. Furthermore, the angle of dimple/protrusion configured in an in-line arrangement around the circumference of the cylinder varied between beta = 15 degrees and beta = 60 degrees. The results indicate that the formation of dimples/protrusions on the cylinder surface is an efficient tool that substantially alters wake characteristics, including reduced backflow intensity and delayed vortex interactions. Compared to that of the bare cylinder, configurations with beta = 45 degrees exhibited the most notable improvements in wake recovery and reduction in crossstream velocity fluctuations. Furthermore, normalized Reynolds shear stress distributions revealed a marked decrease in magnitude and a reduced affected region for modified cylinders, contributing to potential drag reduction and lowering the forces influencing the body.
Flow Patterns Around a Sphere in Terms of Wall Proximity
(Sakarya University, 2025) Goktepelı, Ilker; Aksoy, Muharrem Hilmi; Ispir, Murat
Flow over a sphere exhibits three-dimensional phenomena even at lower values of Reynolds number (Re). Nonetheless, three-dimensional flow structures may also be affected by boundary layers on the rough walls. For this reason, the effects of wall proximity are very significant for the present case. Regarding these issues, flow characteristics of a sphere have been examined for several gap ratios from G* = 0.01 to G* = 2 at Re = 250. The influence of jet flow between the sphere and the wall has increased by decreasing the gap ratio. Although symmetrical flow patterns have been observed for G* = 1 and G* = 2, this situation is not valid for G* ≤ 0.5 in the present study. It is clearly observed in the wake regions for G* ≤ 0.25 and the positive cross-stream velocity components become more dominant, especially for G* = 0.1 and G* = 0.25, respectively. The positive spanwise vorticity component becomes more dominant; however, the negative one is more dominant for G* ≤ 0.1 in terms of gap ratios. For the case of G* ≤ 0.05, the drag coefficients are less than CD = 0.47 and these values are so close to each other. For G* = 0.1, it is around CD = 0.5 for the present problem. By increasing the gap ratios, drag coefficient values also indicated an increasing trend. Moreover, CD = 0.579 has been attained for the case of G* = 0.25 and it is approximately CD = 0.68 for G* = 0.5 as observed. On the other hand, the values of G* = 1 and G* = 2 approached the values of the uniform flow conditions and the effect of the wall proximity by the boundary layer of the bottom surface disappeared.
Machine Learning Based Flow Simulator: Flow Around an Airfoil with Vortex Generators
(Elsevier, 2026) Aksoy, Muharrem Hilmi; Ispir, Murat; Malazi, Mahdi Tabatabaei; Okbaz, Abdulkerim
Controlling the flow structure around an airfoil is crucial for increasing lift and reducing drag. Delaying flow separation improves aerodynamic performance, especially in aircraft and wind turbines. In recent years, artificial intelligence and machine learning methods have emerged as fast and cost-effective alternatives to traditional approaches in fluid mechanics. In this study, we aimed to control the flow around the NACA (National Advisory Committee for Aeronautics) 4412 airfoil using vortex generators (VGs) and to develop a machine-learning-based flow simulator that predicts velocity components based on angle of attack, VG yaw angle, and spatial coordinates. Experimental measurements were conducted in an open-surface, closed-loop water channel at a Reynolds number of Re = 1.0 x 104 using a two-dimensional Particle Image Velocimetry (PIV) system. A total of 60,500 data points were collected per velocity component from 20 experimental cases within the range of alpha = 0 degrees-20 degrees and beta = 15 degrees-30 degrees. A Multilayer Perceptron (MLP) model implemented using TensorFlow was trained to predict the ensemble-averaged (u) and (v) velocity components. We analyzed the effects of hidden layer neuron count and mini-batch size, achieving the highest accuracy with 41 neurons and a batch size of 4, yielding R2 values of 0.978 for (u) and 0.950 for (v). The error distributions were symmetric and closely approximated a Gaussian distribution. Experimental results showed that VGs delayed early-stage flow separation at low alpha but became less effective at higher alpha. The MLP model successfully reconstructed major flow features, providing a reliable data-driven alternative to CFD-based methods. Future work will extend the model to various airfoils, VG designs, Reynolds numbers, and unsteady flows using time-resolved PIV data.