Muammer Ozgoren

Estimation of global solar radiation using ANN over Turkey

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000-2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient (R) value was obtained to be about 0.9936 for the testing data set.

Vortex Structure on a Delta Wing at High Angle of Attack

The structure of the leading-edge vortex from a delta wing at high angle of attack is addressed using a scanning laserversion of high-image-density particleimagevelocimetry.Emphasisis ontheglobal patterns of instantaneous vorticity.Thesepatternsarerelated to distributions ofaveraged and e uctuating velocity and vorticity. At low angle ofattack,intheabsenceofvortex breakdown,itispossibleto detecta totalofe vedistinctlayersofvorticity;theyall exhibit small-scale concentrations of azimuthal vorticity. Immediately downstream of thetrailing edgeof the wing, larger-scale vorticity concentrations appear in the outermost vorticity layers. At sufe ciently high angle of attack, vortex breakdown evolves from the innermost two vorticity layers. For all of these classes of vortical structures, the values of dimensionless wavelength and circulation are assessed. Moreover, the onset of vortex breakdown is interpreted in terms of both instantaneous and averaged patterns of velocity and vorticity. These considerations lead to a direct comparison of vorticity-based and stagnation criteria for breakdown. In turn, these criteria are linked to the onset of regions of e uctuating vorticity in the initial region of breakdown.

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.

Numerical investigation of flow structures around a sphere

A numerical investigation of flow around a sphere is performed and compared with previous studies. Here, a second-order accurate, finite volume method is used in order to predict the instantaneous and time-averaged flow characteristics using large eddy simulation (LES) on the multi-block grid system. Namely, the objectives of this article are: (i) the presentation of flow structures in the wake region downstream of the sphere with a wide variety of flow properties such as the distribution of velocity vectors, patterns of streamlines, Reynolds stress correlations, root mean square of velocity components and other time-averaged flow data in order to reveal the vortical flow structures in detail and (ii) to demonstrate the abilities of computational methods in simulation of vortical flow data. Finally, it has been concluded that there are good agreements between the experimental results and numerical predictions.

Prediction of Hourly Solar Radiation in Six Provinces in Turkey by Artificial Neural Networks

AbstractThe purpose of this study is to apply the method of artificial neural networks (ANNs) to predict the hourly solar radiation of six selected provinces in Turkey. Six neurons—receiving input signals of latitude, longitude, altitude, day of the year, hour of the day, and mean hourly atmospheric air temperature—were used in the input layer of the network. One neuron producing a corresponding output signal of hourly solar radiation was utilized in the output layer of the network. Two different models have been analyzed in the ANNs for training and testing. The results obtained from both models were compared by using different neurons, mean squared error (MSE), coefficient of determination (R2), and mean absolute error (MAE). According to the results, the MSE value of training data in Model II was better than Model I.

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.