Muharrem Hilmi Aksoy

An Assessment of Wind Energy Potential to Meet Electricity Demand and Economic Feasibility in Konya, Turkey

In this study, wind energy potential of Selcuk University campus region was investigated by means of wind data, which were obtained locally from a special observation station. The wind data were collected during the year 2005 at three different heights and were statistically analyzed with the help of MS office, Alwin, Wasp, and Witerm software. It is obtained that yearly averaged wind velocities of the region in 2005 at 10, 20, and 40 m of the mass height were found to be 5.36, 5.40, and 6.05 m/s, respectively. The power density of the region at 40 m was in the range of 260–308 W/m2 with a medium level. For consideration of the calculation, wind speed frequency histogram, Rayleigh and Weibull distributions, wind direction, and temperature data were also used. The obtained results were used for evaluating electricity production of a 6 MW capacity wind farm composed of 1.0, 1.5, and 2.0 MW wind turbines. In addition, the planned wind farm was economically evaluated by means of the basic payback, the net present value, and the internal rate of return methods. The minimum basic payback period was found to be 6.44 years. The results were discussed and interpreted to encourage the investors for establishing wind farm in the region.

Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.