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Mehmet Bilgili,Firat Ekinci,Tugce Demirdelen 한국풍공학회 2021 Wind and Structures, An International Journal (WAS Vol.32 No.2
The aim of the current study is to compare the performance of large 2 MW and 3 MW wind turbines operating on existing onshore wind farms using Blade Element Momentum (BEM) theory and Angular Momentum (AM) theory and illustrate the performance characteristic curves of the turbines as a function of wind speed (U∞). To achieve this, the measurement data obtained from two different Wind Energy Power Plants (WEPPs) located in the Hatay region of Turkey was used. Two different horizontal-axis wind turbines with capacities of 2 MW and 3 MW were selected for evaluation and comparison. The hub-height wind speed (UD), turbine power output (P), atmospheric air temperature (Tatm) and turbine rotational speed (Ω) data were used in the evaluation of the turbine performance characteristics. Curves of turbine power output (P), axial flow induction factor (a), turbine rotational speed (Ω), turbine power coefficient (CP), blade tip speed ratio (λ), thrust force coefficient (CT) and thrust force (T) as a function of U∞ were obtained for the 2 MW and 3 MW wind turbines and these characteristic curves were compared. Results revealed that, for the same wind speed conditions, the higher-capacity wind turbine (3 MW) was operating at higher turbine power coefficient rates, while rotating at lower rotational speed ratios than the lower-capacity wind turbine (2 MW).
Erdem Bilgili,Ali Muhittin Albora,I. Cem Göknar,Osman Nuri Uçan 한국전자통신연구원 2005 ETRI Journal Vol.27 No.3
In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.
Serindere, Gozde,Bilgili, Ersen,Yesil, Cagri,Ozveren, Neslihan Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.-
Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.
Analysis of aerodynamic characteristics of 2 MW horizontal axis large wind turbine
Ilhan, Akin,Bilgili, Mehmet,Sahin, Besir Techno-Press 2018 Wind and Structures, An International Journal (WAS Vol.27 No.3
In this study, aerodynamic characteristics of a horizontal axis wind turbine (HAWT) were evaluated and discussed in terms of measured data in existing onshore wind farm. Five wind turbines (T1, T2, T3, T4 and T5) were selected, and hub-height wind speed, $U_D$, wind turbine power output, P and turbine rotational speed, ${\Omega}$ data measured from these turbines were used for evaluation. In order to obtain characteristics of axial flow induction factor, a, power coefficient, $C_p$, thrust force coefficient, $C_T$, thrust force, T and tangential flow induction factor, a', Blade Element Momentum (BEM) theory was used. According to the results obtained, during a year, probability density of turbines at a rotational speed of 16.1 rpm was determined as approximately 45%. Optimum tip speed ratio was calculated to be 7.12 for most efficient wind turbine. Maximum $C_p$ was found to be 30% corresponding to this tip speed ratio.
Serindere Gozde,Bilgili Ersen,Yesil Cagri,Ozveren Neslihan 대한영상치의학회 2022 Imaging Science in Dentistry Vol.52 No.2
Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the “gold standard” for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.
Analysis of aerodynamic characteristics of 2 MW horizontal axis large wind turbine
Akin Ilhan,Mehmet Bilgili,Besir Sahin 한국풍공학회 2018 Wind and Structures, An International Journal (WAS Vol.27 No.3
In this study, aerodynamic characteristics of a horizontal axis wind turbine (HAWT) were evaluated and discussed in terms of measured data in existing onshore wind farm. Five wind turbines (T1, T2, T3, T4 and T5) were selected, and hub-height wind speed, UD, wind turbine power output, P and turbine rotational speed, Ω data measured from these turbines were used for evaluation. In order to obtain characteristics of axial flow induction factor, a, power coefficient, Cp, thrust force coefficient, CT, thrust force, T and tangential flow induction factor, a', Blade Element Momentum (BEM) theory was used. According to the results obtained, during a year, probability density of turbines at a rotational speed of 16.1 rpm was determined as approximately 45%. Optimum tip speed ratio was calculated to be 7.12 for most efficient wind turbine. Maximum Cp was found to be 30% corresponding to this tip speed ratio.
DETERMINATION OF THE REMAINING METHANE POTENTIAL OF LANDFILLED WASTES
( Adem Baştürk ),( Ahmet Demir ),( M. Siuan Bilgili ),( Bestamin Özkaya ) 한국폐기물자원순환학회 2002 APLAS Vol.2002 No.1
In this study, a comparison of methane (CH₄) generation rates for two test cells, one operated with (enhanced) and one without leachate recirculation at Odayeri Sanitary Landfill was performed by biological methane potential (BMP) test. Initial methane potential was approximately 34,5㎥ CH₄/wet ton. The remaining methane potential for the control cell (H1) and the enhanced cell (H2) is determined to be 32,6 ㎥ CH₄/wet ton and 31,1 ㎥CH₄/wet ton of refuse after 8 months of operation. Thus, H2 test cell shows more decomposition relative to the H1 test cell.