Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 13Citation - Scopus: 14A Novel Cfd-Ann Approach for Plunger Valve Optimization: Cost-Effective Performance Enhancement(Elsevier Sci Ltd, 2024) Kaak, Abdul Rahman Sabra; Celebiog, Kutay; Bozkus, Zafer; Ulucak, Oguzhan; Ayli, Ece; Çelebioğlu, KutayThis paper introduces a novel computational fluid dynamics-artificial neural network (CFD-ANN) approach that has been devised to enhance the efficiency of plunger valves. The primary emphasis of this research is to achieve an optimal equilibrium between hydraulic flow and geometric configuration. This study is a novel contribution to the field as it explores the flow dynamics of plunger valves using Computational Fluid Dynamics (CFD) and proposes a unique methodology by incorporating Machine Learning (ML) for performance forecasting. An artificial neural network (ANN) architecture was developed using a thorough comprehension of flow physics and the impact of geometric parameters acquired through computational fluid dynamics (CFD). Using optimization, the primary aspects of the Artificial Neural Network (ANN), including the learning algorithm and the number of hidden layers, have been modified. This refinement has resulted in the development of an architecture exhibiting a remarkably high R2 value of 0.987. This architectural design was employed to optimize the plunger valve. By utilizing Artificial Neural Networks (ANN), a comprehensive analysis comprising 1000 distinct configurations was effectively performed, resulting in a significant reduction in time expenditure compared to relying on Computational Fluid Dynamics (CFD). The result was a refined arrangement that achieved maximum head loss, subsequently verified using computational fluid dynamics (CFD) simulations, resulting in a minimal discrepancy of 2.66%. The efficacy of artificial neural networks (ANN) becomes apparent due to their notable cost-efficiency, along with their capacity to produce outcomes that are arduous and expensive to get through conventional optimization research utilizing computational fluid dynamics (CFD).Article Citation - WoS: 1Citation - Scopus: 1Prediction of the Onset of Shear Localization Based on Machine Learning(Cambridge Univ Press, 2023) Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk; Akar, Samet; Ayll, EcePredicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.Article Citation - WoS: 6Citation - Scopus: 7Machine Learning Based Developing Flow Control Technique Over Circular Cylinders(Asme, 2023) Turkoglu, Hasmet; Ayli, Ece; Kocak, EyupThis paper demonstrates the feasibility of blowing and suction for flow control based on the computational fluid dynamics (CFD) simulations at a low Reynolds number flows. The effects of blowing and suction position, and the blowing and suction mass flowrate, and on the flow control are presented in this paper. The optimal conditions for suppressing the wake of the cylinder are investigated by examining the flow separation and the near wake region; analyzing the aerodynamic force (lift and drag) fluctuations using the fast Fourier transform (FFT) to separate the effects of small-scale turbulent structures in the wake region. A method for stochastic analysis using machine learning techniques is proposed. Three different novel machine learning methods were applied to CFD results to predict the variation in drag coefficient due to the vortex shedding. Although, the prediction power of all the methods utilized is in the acceptable accuracy range, the Gaussian process regression (GPR) method is more accurate with an R-2(coefficient of determination) > 0.95. The results indicate that by optimizing the blowing and suction parameters like mass flowrate, slot location, and the slot configuration, up to 20% reduction can be achieved in the drag coefficient.Article Citation - WoS: 7Citation - Scopus: 7A Study on the Μwire-Edm of Ni55.8ti Shape Memory Superalloy: an Experimental Investigation and a Hybrid Ann/Pso Approach for Optimization(Springer Heidelberg, 2023) Seyedzavvar, Mirsadegh; Boga, Cem; Akar, SametThe unique properties of high hardness, toughness, strain hardening, and development of strain-induced martensite of nickel-titanium superalloys made the micro-wire electro discharge machining (mu wire-EDM) process one of the main practical options to cut such alloys in micro-scale. This paper presents the results of a comprehensive study to address the response variables of Ni55.8Ti superalloy in mu wire-EDM process, including the kerf width (KW), material removal rate (MRR), arithmetic mean surface roughness (R-a) and white layer thickness (WLT). To this aim, the effects of pulse on-time (T-on), pulse off-time (T-off), discharge current (I-d) and servo voltage (SV) as input parameters were investigated using the experiments conducted based on Taguchi L-27 orthogonal array. The results were employed in the analysis of variance (ANOVA) to examine the significance of input parameters and their interactions with the output variables. An optimization approach was adopted based on a hybrid neural network/particle swarm optimization (ANN/PSO) technique. The ANN was employed to achieve the models representing the correlation between the input parameters and output variables of the mu wire-EDM process. The weight and bias factor matrices were obtained by ANN in MATLAB and together with the feed forward/backpropagation model and developed functions based on PSO methodology were used to optimize the input parameters to achieve the minimum quantities of KW, R-a and WLT and the maximum value of MRR, individually and in an accumulative approach. The results represented a maximum accumulative error of nearly 8% that indicated the precision of the developed model and the reliability of the optimization approach. At the optimized level of input parameters obtained through the accumulative optimization approach, the KW, R-a, and WLT remained nearly intact as compared with the levels of responses obtained in the individual optimization approach, while there was a sacrifice in the machining efficiency and reduction in the MRR in the mu wire-EDM process of Nitinol superalloy.Article Citation - WoS: 5Citation - Scopus: 6The Effects of Using Corrugated Booster Reflectors To Improve the Performance of a Novel Solar Collector To Apply in Cooling Pv Cells-Navigating Performance Using Ann(Springer, 2021) Oztop, Hakan F.; Alnefaie, Khalid A.; Ahmadian, Ali; Baleanu, Dumitru; Abu-Hamdeh, Nidal H.In this study, turbulent flow and heat transfer of water in a solar collector equipped with corrugated booster reflectors to apply in cooling photovoltaic cells (PV) are investigated. 3D simulation is done using by the control volume method and SIMPLEC algorithm. The optimization was carried out by comparison of different parameters to reach the optimal situation with the maximum efficiencies of energy and exergy. It is established that in the case of corrugated booster reflectors, the temperature of outlet fluid and efficiencies of energy and exergy are more. In general, while the trend of variation of exergy efficacy with impressive parameters is increasing, using the mixers precipitate the efficacy increment. Furthermore, for the case that the trend of variation of exergy efficacy with altering these parameters is reducing, the reducing trend gets slow. Finally, it is realized that using corrugated booster reflectors have a significant effect on collector efficacy, and the highest exergy efficacy was obtained for the 50 degrees corrugations. At Re = 6800, the maximum Nusselt number achieves and it is about 6.03. Finally, using an artificial neural network, the output parameters were navigated with acceptable accuracy. For 76.6% of the data points, the margin of deviation (MOD) was < 1%, and for the rest, the maximum MOD was 2%.Article Citation - WoS: 5Citation - Scopus: 7Hydrodynamic Analysis of a Heat Exchanger With Crosscut Twisted Tapes and Filled With Thermal Oil-Based Swcnt Nanofluid: Applying Ann for Prediction of Objective Parameters(Springer, 2021) Almitani, Khalid H.; Gari, Abdullatif A.; Alimoradi, Ashkan; Ahmadian, Ali; Baleanu, Dumitru; Abu-Hamdeh, Nidal H.Present study investigates hydrodynamic analysis of heat exchanger with crosscut twisted tapes and filled with thermal oil-based SWCNT nanofluid (NF). SIMPLE algorithm and FVM method are used. The heat transfer fluid enters the test section at T-in = 300 K in different flow velocities, which are related with Reynolds numbers 5000, 10,000, 15,000 and 20,000. For ensuring that the input flow to test section is always fully developed, the input part to length 2L is considered. It is also intended to ensure that the flow does not return to test section of the exit section of length L. Also, the test section has the constant temperature of T-s = 400 K. Different geometrical parameters of twisted tapes in heat exchanger are studied. The optimization is carried out due to fulfill the highest performance evaluation criterion (PEC index). Based on results, usage of twisted tapes has a sharp impact on thermal and hydraulic characteristics of heat exchanger and leading to swirling motion, which improve the heat transfer coefficient and augment the pressure drop (Delta P). Besides, usage of simple model is more efficient than crosscut model. Also, it is understood that the PEC index values always are more than 1.11, which means that employment of these turbulators is effective and positive with thermal-hydraulic viewpoint. The simple model (Case K and N = 8) is introduced as the most optimum model in this paper, and its PEC values for system filled with NF in phi = 0.8% at Re = 5000, 10,000, 15,000 and 20,000 are 1.37, 1.59, 1.78 and 1.93, respectively. The application of machine learning methods showed that the output parameters in the simulation of heat exchangers are well predicted. The accuracy of the developed neural network was such that the maximum error was below 1%.