In this study, we estimated global horizontal irradiance (GHI) and photovoltaic (PV) power potential in Türkiye using fan Adaptive Neuro-Fuzzy Inference System (ANFIS) with inputs from Meteonorm 8.0. We considered latitude and longitude for GHI estimation and added tilt and azimuth angles for PV power potential. Training and testing data yielded Root Mean Square Errors (RMSE) of 10.429 kWh/m²/year and 35.395 kWh/m²/year for GHI, and 3.984 kWh/kWp/year and 39.624 kWh/kWp/year for PV power potential, respectively. The coefficient of determination ( $${R}^{2}$$ R 2 ) was 0.995 for GHI training data, and $${R}^{2}$$ R 2 = 0.946 for GHI testing data, and $${R}^{2}$$ R 2 = 0.993 for PV power potential training data, and $${R}^{2}$$ R 2 = 0.951 for PV power potential testing data. Results showed that the southeast of Türkiye had the highest GHI, around 1900 kWh/m²/year, while the north had the lowest, around 1400 kWh/m²/year. The highest electricity production occurred in the south, approximately 1900 kWh/kWp/year, while the north yielded the least, about 1450 kWh/kWp/year. Tilt angle increase and azimuth angles of -90° or + 90° led to decreased electricity production. This study provides precise GHI and PV power potential estimates for Türkiye, using ANFIS with diverse meteorological inputs.

In the present study, active and passive flow control methods have been implemented to investigate their effects on the wake flow structures of a circular cylinder. Grooves having circular, rectangular, and triangular cross sections have been applied to the cylinders exposed to the rotation rates, α, from 0 to 0.79. The experiments have been conducted by particle image velocimetry at a Reynolds number of Re = 5 × 103. The contour graphics of time-averaged results have been presented. Moreover, the variations in velocity profiles have also been depicted. The experimental results revealed significant variations for flow patterns, wake structures, and turbulence parameters due to the effects of both groove geometries and rotational motion. In the stationary cases, for turbulence intensity, the circular grooved cylinder exhibited a 15% increase, while the triangular grooved cylinder showed a slightly higher increase of around 20% compared to that of the bare cylinder (BC). Conversely, in non-stationary cases, the rectangular grooved cylinder displayed the most prominent reduction in turbulence intensity, decreasing by approximately 10% compared to that of the BC. The groove type has considerably affected the flow structures of the wake regions, especially for the lower rotation rates.

In this study, artificial neural networks (ANNs) have been implemented to recover missing data from the particle image velocimetry (PIV), providing quantitative measurements of velocity fields. Due to laser reflection or lower intensity of particles in the interrogation area, the reconstruction of erroneous velocity vectors is required. Therefore, the distribution of time-averaged and normalized flow characteristics around a circular cylinder has been demonstrated as streamwise and cross-stream velocities at Re = 8000. These velocity components have been given for different regions at x/D = 0.5, x/D = 1.25, x/D = 2, and y/D = 0. These stations have been chosen to estimate missing data for near-wake, mid-wake, far-wake, and symmetry regions. The missing data ratios (A*) for 0.5 ≤ x/D ≤ 2 are A* = 3.5%, 7%, and 10%. In addition, these values are A* = 4%, 8%, and 12% for y/D = 0, while A* = 7.5% for the shaded region. The increment of area positively affects the estimation results for near-wake and mid-wake regions. Moreover, the errors tend to decrease by moving away from the body. At y/D = 0, increasing the area negatively influences the prediction of the results. The mean velocity profiles of predicted and experimental data have also been compared. The missing data have been predicted with a maximum percentage error of 3.63% for horizontal stations. As a result, the ANN model has been recommended to reconstruct PIV data.