Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 1Citation - Scopus: 3Improving the Performance of a Mems-Imu System Based on a False State-Space Model by Using a Fading Factor Adaptive Kalman Filter(Sage Publications Ltd, 2024) Akbas, Eren Mehmet; Cifdaloz, Oguzhan; Ucuncu, MuratIn this study, we introduce a novel algorithm, the low error rate adaptive fading Kalman filter (LERAFKF), designed to predict system states in the presence of uncertainty in both the system matrix and the model. The purpose of developing the LERAFKF is to address challenges arising from measurement difficulties, system parameter uncertainties, and state-space model inaccuracies. Several studies have utilized the Kalman filter (KF) and extended Kalman filter (EKF) algorithms to handle uncertainties in system parameters, corrupted measurements with unknown covariances, and incorrectly defined system modeling. Our work distinguishes itself by proposing a new approach that achieves lower error and deviation rates by combining the current Kalman estimation algorithm and the fading factor adaptive filter. To achieve this goal, we transformed the KF into an adaptive KF by introducing a forgetting factor, and the algorithm was subsequently reconfigured to calculate an optimized forgetting factor. In this study, we conducted simulations and measurements using both linear and nonlinear systems. The linear system represents the motion of an object, and the simulation involved measurements from the inertial navigation system (INS) sensor, specifically the Pololu IMU01b three-axis inertial measurement unit (IMU) sensor. We employed the SDI33 system with 9 degrees of freedom (DoF) mounted on a three-axis rotary table for the nonlinear system. This system simulates a missile as a 4th-order nonlinear system. Our findings demonstrate that the proposed LERAFKF filter outperforms KF and EKF in estimating system states, particularly in measurement-related error scenarios. Mean square error analysis further confirmed that LERAFKF exhibited the lowest error values, showcasing superior performance over KF and EKF in linear and nonlinear systems.Article Enhancing Efficiency of an Old Hydropower Plant Turbine Through a Mutual Runner Design and Component Optimization(Sage Publications Ltd, 2025) Seydim, Sila; Yildirim, Gozde; Ulucak, Oguzhan; Buyuksolak, Fevzi; Ejder, Beril; Kantar, Ece Nil; Celebioglu, KutayThis paper presents a systematic approach to the rehabilitation process of Sar & imath;yar HEPP, a hydroelectric power plant that has been operational for more than 50 years. Units 1 and 2 (U1-U2) were originally designed with a head of 93 m and a turbine power of 48.5 MW, while Units 3 and 4 (U3-U4) were designed with a lower head of 76.5 m but the same turbine power of 48.5 MW. A methodology combining reverse engineering and CFD analysis is developed to identify and evaluate the critical parameters that have an impact on the existing turbine performance. A hybrid design is proposed to replace the existing two different types of turbines, which reduces manufacturing costs and design time. The performance of the new hybrid design is evaluated in detail with CFD analysis. For both existing and hybrid design, steady and unsteady analyses are performed. For all of the situations hill charts are obtained and the comparison of the old and new hybrid design is discussed in detail. The results show that the new design has improved the efficiency of the turbine and the power plant, resulting in a 14.2% efficiency increase in U1-U2 and a 21% system efficiency improvement in U3-U4. This study provides a guide to designers and practitioners for the rehabilitation of hydroelectric power plants.Article Prediction of Noise Generated by Rod-Airfoil Configuration: an Investigation Based on Experiments and Machine Learning(Sage Publications Ltd, 2024) Kocak, Eyup; Ayli, EceThis study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors' knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches.Article Citation - WoS: 10Citation - Scopus: 11Analysis of Heat Transfer Enhancement of Passive Methods in Tubes With Machine Learning(Sage Publications Ltd, 2024) Ayli, Ece; Turkoglu, Hasmet; Yapici, Ekin Ozgirgin; Özgirgin Yapıcı, EkinThis study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients (R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model's ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization.Article Citation - WoS: 4Citation - Scopus: 4Window Length Insensitive Real-Time Emg Hand Gesture Classification Using Entropy Calculated From Globally Parsed Histograms(Sage Publications Ltd, 2023) Alguner, Ayber Eray; Ergezer, HalitElectromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon's entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.Article Citation - WoS: 4Citation - Scopus: 4Investigation of Aerodynamic and Aeroacoustic Behavior of Bio-Inspired Airfoils With Numerical and Experimental Methods(Sage Publications Ltd, 2024) Kocak, Eyup; Aradag, Selin; Guzey, Kaan; Ayli, Ulku EceThis article presents numerical and experimental studies on the aerodynamic and aeroacoustic characteristics of the NACA0012 profile with owl-inspired leading-edge serrations for aeroacoustic control. The leading-edge serrations under investigation are in a sinusoidal profile with two main design parameters of wavelength and amplitude. The noise-suppressing ability of sinusoidal serrations is a function of several parameters such as amplitude, wavelength, inflow speed, angle of attack, which are examined in this study. Amplitude (A) and wavelength (& lambda;) of the serration are varied between 1.25 and 2.5, 20 < & lambda; < 60, respectively. The corresponding Reynolds numbers are between 1 and 3 x 10(5). The angle of attack for each configuration is changed between 4 & DEG; and 16 & DEG;. Forty different configurations are tested. According to the results, owl-inspired leading-edge serrations can be used as aeroacoustic control add-ons in blade designs for wind turbines, aircraft, and fluid machinery. Results show that the narrower and sharper serrations have a better noise reduction effect. Overall sound pressure level (SPL) reduces up to 20% for the configuration with the largest amplitude and smaller wavelength. The results also showed that serration amplitude had a distinct effect on aeroacoustic performance, whereas wavelength is a function of amplitude. At the smaller angle of attack values, AOA < 8 & DEG;, the lift and drag coefficients are almost the same for both clean and wavy profiles. On the other hand, typically for angle of attack values more than 12 & DEG; (after stall), when the angle of attack is increased, serration adversely affects aerodynamic performance.Article Citation - WoS: 2Citation - Scopus: 3Control Structure Design With Constraints for a Slung Load Quadrotor System(Sage Publications Ltd, 2024) Leblebicioglu, Kemal; Ergezer, HalitWe propose a control structure for a quadrotor carrying a slung load with swing-angle constraints. This quadrotor is supposed to pass through the waypoints at specified speeds. First, a cascaded PID autopilot is designed, which adaptively gives attention to position and speed requirements as a function of their errors. Its parameters are found from an optimization problem solved using the PSO algorithm. Second, this controller's performance is improved by adding the Complementary Controller employing an ANN. 5. Training data for the ANN is created by solving optimal control problems. The ANN is activated when the swing angle constraint is about to be violated. It is trained using optimal control values corresponding to the cases where the swing angle falls in a particular band about the upper swing angle constraint. Simulations are performed in a MATLAB environment. Finally, some of the simulation results are validated on a physical system.Article Citation - WoS: 24Citation - Scopus: 28Behavior of Glulam Timber Beam Strengthened With Carbon Fiber Reinforced Polymer Strip for Flexural Loading(Sage Publications Ltd, 2021) Isleyen, ummu K.; Ghoroubi, Rahim; Mercimek, Omer; Anil, Ozgur; Erdem, Recep TugrulIn the last 20 years, the use of wooden structures and their dimensions have gradually increased. The wood application has increased in different structures such as multistory buildings, sports, industrial facilities, road and railway bridges, power transmission lines, and towers. The widespread use and size of wood structures have increased the research on developing special types of wood products supported by composite materials. Laminated wood elements are the leading composite wood materials. Laminated wooden beams allow making much larger openings than standard solid wood structural elements. The development of the sizes and usage areas of wooden structures has increased the capacity of glulam structural elements and reveals the need to improve their performance. Carbon fiber reinforced polymers (CFRPs) are the most suitable options for increasing the bearing capacity values of glulam beams and improving general load-displacement behaviors. In this study, the use of CFRP strips in different layouts to increase glulam wooden beams and the application of CFRP fan-type anchors in the CFRP strip endpoints are the studied variables. Anchored and non-anchored glulam wooden beams reinforced with CFRP strips with different layouts were tested using a three-point bending test. The ultimate load capacity, initial stiffness, displacement ductility ratio, energy dissipation capacity, failure mechanisms, and general load-displacement behavior of wooden beam test specimens were obtained and interpreted as a result of the experiments.Article Citation - WoS: 20Citation - Scopus: 20Modeling of Mixed Convection in an Enclosure Using Multiple Regression, Artificial Neural Network, and Adaptive Neuro-Fuzzy Interface System Models(Sage Publications Ltd, 2020) Ayli, EceIn this study, the heat transfer characteristics of laminar combined forced convection through a horizontal duct are obtained with the help of the numerical methods. The effect of the geometrical parameters of the cavity and Reynolds number on the heat transfer is investigated. New heat transfer correlation for hydrodynamically fully developed, laminar combined forced convection through a horizontal duct is proposed with an average error of 6.98% and R-2 of 0.8625. The obtained correlation results are compared with the artificial neural network and adaptive neuro-fuzzy interface system models. Due to the obtained results, good agreement is identified between the numerical results and predicted adaptive neuro-fuzzy interface system results. In conclusion, it is seen that adaptive neuro-fuzzy interface system can predict the Nusselt number distribution with a higher accuracy than the developed correlation and the artificial neural network model. The developed adaptive neuro-fuzzy interface system model predicts the Nusselt number with 1.07% mean average percentage error and 0.9983 R-2 value. The effect of the different training algorithms and their ability to predict Nusselt number distribution are examined. According to the results, the Bayesian regulation algorithm gives the best approach with a 2.235% error. According to the examination that is performed in this study, the adaptive neuro-fuzzy interface system is a powerful, robust tool that can be used with confidence for predicting the thermal performance.Article Citation - WoS: 32Citation - Scopus: 36Effect of Powder Metallurgy Cu-B4c Electrodes on Workpiece Surface Characteristics and Machining Performance of Electric Discharge Machining(Sage Publications Ltd, 2016) Cogun, Ferah; Akturk, Nizami; Cogun, Can; Esen, Ziya; Genc, AsimThe main aim of this study is to produce new powder metallurgy (PM) Cu-B4C composite electrode (PM/(Cu-B4C)) capable of alloying the recast workpiece surface layer during electric discharge machining process with boron and other hard intermetallic phases, which eventually yield high hardness and abrasive wear resistance. The surface characteristics of the workpiece machined with a PM/(Cu-B4C) electrode consisted of 20 wt% B4C powders were compared with those of solid electrolytic copper (E/Cu) and powder metallurgy pure copper (PM/Cu) electrodes. The workpiece surface hardness, surface abrasive wear resistance, depth of the alloyed surface layer and composition of alloyed layers were used as key parameters in the comparison. The workpiece materials, which were machined with PM/(Cu-B4C) electrodes, exhibited significantly higher hardness and abrasive wear resistance than those of machined with the E/Cu and PM/Cu. The main reason was the presence of hard intermetallic phases, such as FeB, B4C (formed due to the boron in the electrode) and Fe3C in the surface layer. The improvement of the surface hardness achieved for steel workpiece when using PM/(Cu-B4C) electrodes was significantly higher than that reported in the literature. Moreover, the machining performance outputs (workpiece material removal rate, electrode wear rate and workpiece average surface roughness (Ra)) of the electrodes were also considered in this study.