Browsing by Author "Ayli, Ece"

Now showing 1 - 20 of 28

Citation - WoS: 6
Citation - Scopus: 7
Analysis of Heat Transfer Enhancement of Passive Methods in Tubes With Machine Learning
(Sage Publications Ltd, 2024) Ayli, Ece; Turkoglu, Hasmet; Yapici, Ekin Ozgirgin; 31329; 265836; 12941; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This 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.
Citation - WoS: 5
Citation - Scopus: 5
Ann and Anfis Performance Prediction Models for Francis Type Turbines
(Turkish Soc thermal Sciences Technology, 2020) Aylı, Ülkü Ece; Ayli, Ece; Ulucak, Oguzhan; 265836; Makine Mühendisliği; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Turbines can be operated under partial loading conditions due to the seasonal precipitation fluctuations and due to the needed electrical demand over time. According to this partial working need, designers generate hill chart diagrams to observe the system behavior under different flow rates and head values. In order to generate a hill chart, several numerical or experimental studies have been performed at different guide vane openings and head values which are very time consuming and expensive. In this study, the efficiency prediction of Francis turbines has been performed with ANN and ANFIS methods under different operating conditions and compared with simulation results. The obtained results indicate that it is possible to obtain a hill chart using ANFIS method instead of a costly experimental or numerical tests. ANN and ANFIS parameters which effect the output, have been optimized with trying 100 different cases. 75% of the numerical data set is used for training and 25 % is used for validation as testing data. To asses and compare the performance of multiple ANN and ANFIS models several statistical indicators have been used. Insight to the performance evaluation, it is seen that ANFIS can predict the efficiency distribution with higher accuracy than the ANN model. The developed ANFIS model predicts the efficiency with 1.41% mean average percentage error and 0.999 R-2 value. To the best of the author's knowledge, this is the first study in the literature that ANN and ANFIS are used in order to predict the efficiency distribution of the turbines at different loading conditions.
Citation - WoS: 3
Citation - Scopus: 2
Artificial Neural Networks for Predicted Bending Properties of Additively Manufactured Pyramidal Lattice Truss Core Sandwich Structures
(Elsevier, 2025) Karagozlu, Cem Onat; Ayli, Ece; Tanabi, Hamed; Sabuncuoglu, Baris; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
An Artificial Neural Network (ANN) model is developed to predict the mechanical behavior of pyramidal lattice truss core sandwich structures under bending load. The development process aims to optimize material use, enhance structural efficiency, and reduce analysis time for the developed ANN model. Key phases include specimen fabrication via additive manufacturing, experimental testing in four-point bending, and validation of the finite element model (FEM). Experimental tests on five specimens validated FEM simulations with a 4.5 % error rate. The ANN, trained on FEM data, accurately predicts reaction forces and stress components (sigma,, sigma 2, tau,2). Comparison of training algorithms (LM, Levenberg-Marquardt, BR, Bayesian Regularization, SCG, Scaled Conjugate Gradient) highlights LM's superior performance in convergence and MSE reduction (max. MSE value: 2.287), while BRexcels in generalization and robustness. Scaled Conjugate Gradient's performance was lower than the others. The ANN demonstrates high accuracy within the training range but shows limitations in extrapolation. Overall, this ANN model offers engineers a rapid and precise tool for predicting the mechanical behavior of these sandwich structures, reducing reliance on time-consuming FEM simulations and facilitating efficient design optimization across various engineering applications.
Citation - WoS: 14
Citation - Scopus: 20
Cavitation in Hydraulic Turbines
(Edizioni Ets, 2019) Ayli, Ece; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Hydroenergy is one of the richest and most useful renewable energy sources in the world. Hydropower is a vital source as it is the clean energy source, sustainable and last but not least it is also cost-effective. One of the most important parameters that affect the performance of the hydraulic machines is the cavitation phenomenon, which is defined as the formation of the vapor bubbles in the liquid through any hydraulic turbine. In this paper, hydraulic machines, cavitation, types of cavitation are briefly described. After theoretical studies, analytical and numerical researches about cavitation in hydraulic machinery are discussed extensively. With those studies which are summarized in this paper covers a lot of ground about cavitation on the other hand further studies are needed about cavitation in hydro turbines. Numerical methods provide sufficient predictions for cavitation. However, numerical results should be verified by experimental measurements and detection methods to decide what intensity and which shape of cavitation is hazardous and vital, where the local pressure is lower than the vapor pressure and at which static pressure cavities start to grow and collapse.
Clean Energy Generation in Residential Green Buildings
(inst Engineering Tech-iet, 2019) Aylı, Ülkü Ece; Yapici, Ekin Ozgirgin; Ayli, Ece; Makine Mühendisliği; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Due to the recent investigations, buildings consume a considerable amount of the electricity, drinking water, global final energy use and as a result are responsible for one third of the global carbon emissions. Therefore, building sector has a key role to reach global energy targets. In this sight, this study draws attention to the sustainable energy performances of green buildings (GBs) and aims towards the GBs concept which includes renewable sources in the construction and lifetime utilization. The remainder of the chapter is subjected as follows: Section 2.1 gives a brief information about residential GBs, and in Section 2.2, certification systems for sustainability ratings of residential GBs are given. This is followed by case studies related to the certification systems in Section 2.3 part. In Section 2.4, GBs incentives are summarized. Section 2.5 provides information about energy demand modelling for residential GBs, and in Section 2.6, clean energy generation systems in residential GBs are described in detail. Finally, outlook for the works that is performed up to now and the outlook for the future is given.
Citation - WoS: 13
Citation - Scopus: 14
A Comparative Study of Multiple Regression and Machine Learning Techniques for Prediction of Nanofluid Heat Transfer
(Asme, 2022) Ayli, Ece; Turkoglu, Hasmet; Kocak, Eyup; 283455; 265836; 12941; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
The aim of this article is to introduce and discuss prediction power of the multiple regression technique, artificial neural network (ANN), and adaptive neuro-fuzzy interface system (ANFIS) methods for predicting the forced convection heat transfer characteristics of a turbulent nanofluid flow in a pipe. Water and Al2O3 mixture is used as the nanofluid. Utilizing fluent software, numerical computations were performed with volume fraction ranging between 0.3% and 5%, particle diameter ranging between 20 and 140 nm, and Reynolds number ranging between 7000 and 21,000. Based on the computationally obtained results, a correlation is developed for the Nusselt number using the multiple regression method. Also, based on the computational fluid dynamics results, different ANN architectures with different number of neurons in the hidden layers and several training algorithms (Levenberg-Marquardt, Bayesian regularization, scaled conjugate gradient) are tested to find the best ANN architecture. In addition, ANFIS is also used to predict the Nusselt number. In the ANFIS, number of clusters, exponential factor, and membership function (MF) type are optimized. The results obtained from multiple regression correlation, ANN, and ANFIS were compared. According to the obtained results, ANFIS is a powerful tool with a R-2 of 0.9987 for predictions.
Citation - WoS: 1
Citation - Scopus: 2
A Comprehensive Review of Cyclone Separator Technology
(Wiley, 2024) Ayli, Ece; Kocak, Eyup; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This review article examines the working principles, optimal dimensions, effects of key parameters, and the results of experimental/numerical studies on cyclone separators. Investigations have been conducted on the effects of parameters such as vortex finder diameter, conical part diameter, cyclone separator diameter, cylinder height, inlet height, inlet width, vortex finder length, and cyclone total length on efficiency, performance, and pressure drop. Furthermore, the article explores current modifications and efforts to improve efficiency. These modifications include adding water nozzles, inserting ribs, employing double-stage cyclones, incorporating additional inlets, using finned cylinder bodies, adding extra top inlets, introducing liquid jets, employing helical roof inlets, adding laminarizers, incorporating internal spiral vanes, and employing slotted vortex finders. While serving as a guide to optimize the design and performance of cyclone separators, this article emphasizes new and innovative approaches to enhance their industrial applicability. By compiling studies conducted from conceptual birth to the present, the aim of this article is to serve as a guidebook.
Citation - WoS: 2
Citation - Scopus: 2
Critical Decision Making for Rehabilitation of Hydroelectric Power Plants
(Taylor & Francis inc, 2023) Westerman, Jerry; Celebioglu, Kutay; Ayli, Ece; Ulucak, Oguzhan; Aradag, Selin; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Due to their diminishing performance, reliability, and maintenance requirements, there has been a rise in the demand for the restoration and renovation of old hydroelectric power facilities in recent decades. Prior to initiating a rehabilitation program, it is crucial to establish a comprehensive understanding of the power plant's current state. Failure to do so may result in unnecessary expenses with minimal or no improvements. This article presents a systematic rehabilitation methodology specifically tailored for Francis turbines, encompassing a methodological approach for condition assessment, performance testing, and evaluation of rehabilitation potential using site measurements and CFD analysis, and a comprehensive decision-making process. To evaluate the off-design performance of the turbines, a series of simulations are conducted for 40 different flow rate and head combinations, generating a hill chart for comprehensive evaluation. Various parameters that significantly impact the critical decision-making process are thoroughly investigated. The validity of the reverse engineering-based CFD methodology is verified, demonstrating a minor difference of 0.41% and 0.40% in efficiency and power, respectively, between the RE runner and actual runner CFD results. The optimal efficiency point is determined at a flow rate of 35.035 m(3)/s, achieving an efficiency of 94.07%, while the design point exhibits an efficiency of 93.27% with a flow rate of 38.6 m(3)/s. Cavitation is observed in the turbine runner, occupying 27% of the blade suction area at 110% loading. The developed rehabilitation methodology equips decision-makers with essential information to prioritize key issues and determine whether a full-scale or component-based rehabilitation program is necessary. By following this systematic approach, hydroelectric power plants can efficiently address the challenges associated with aging Francis turbines and optimize their rehabilitation efforts.
Citation - WoS: 15
Citation - Scopus: 15
Developing and Implementation of an Optimization Technique for Solar Chimney Power Plant With Machine Learning
(Asme, 2021) Kocak, Eyup; Bayer, Ozgur; Beldek, Ulas; Yapic, Ekin Ozgirgin; Ayli, Ece; Ulucak, Oguzhan; 59950; 31329; 265836; 06.06. Makine Mühendisliği; 06.08. Mekatronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Green energy has seen a huge surge of interest recently due to various environmental and financial reasons. To extract the most out of a renewable system and to go greener, new approaches are evolving. In this paper, the capability of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System in geometrical optimization of a solar chimney power plant (SCPP) to enhance generated power is investigated to reduce the time cost and errors when optimization is performed with numerical or experimental methods. It is seen that both properly constructed artificial neural networks (ANN) and adaptive-network-based fuzzy inference system (ANFIS) optimized geometries give higher performance than the numerical results. Also, to validate the accuracy of the ANN and ANFIS predictions, the obtained results are compared with the numerical results. Both soft computing methods over predict the power output values with MRE values of 12.36% and 7.25% for ANN and ANFIS, respectively. It is seen that by utilizing ANN and ANFIS algorithms, more power can be extracted from the SCPP system compared to conventional computational fluid dynamics (CFD) optimized geometry with trying a lot more geometries in a notably less time when it is compared with the numerical technique. It is worth mentioning that the optimization method that is developed can be implemented to all engineering problems that need geometric optimization to maximize or minimize the objective function.
Citation - WoS: 2
Citation - Scopus: 1
Exploring the Potential of Artificial Intelligence Tools in Enhancing the Performance of an Inline Pipe Turbine
(Sage Publications Ltd, 2024) Celebioglu, Kutay; Ayli, Ece; Cetinturk, Huseyin; Tascioglu, Yigit; Aradag, Selin; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
In this study, investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe. The objective of the study is to determine the feasibility of implementing a turbine within a pipe and enhance its performance values within the operating range. The turbine within the pipe occupies significantly less space in hydroelectric power plants since a spiral casing is not used to distribute the flow to stationary vanes. Consequently, production and assembly costs can be reduced. Hence, there is a broad scope for application, particularly in small and medium-scale hydroelectric power plants. According to the results, the efficiency value increases on average by approximately 1.5% compared to conventional design, and it operates with higher efficiencies over a wider flow rate range. In the second part of the study, machine learning was employed for the efficiency prediction of an inline-type turbine. An appropriate Artificial Neural Network (ANN) architecture was initially obtained, with the Bayesian Regularization training algorithm proving to be the best approach for this type of problem. When the suitable ANN architecture was utilized, the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194. An R2 value of 0.99631 was achieved with the appropriate ANN architecture.
An Innovative Showcase of Similarity Methods for Accelerated Turbine Design Processes and Cost-Effective Solutions
(Taylor & Francis Ltd, 2025) Kantar, Ece Nil; Ayli, Ece; Celebioglu, Kutay; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This study aims to design a containerized Francis-type turbine for installation on drinking water pipelines equipped with pressure-reducing equipment, enabling energy recovery from untapped hydraulic resources. The turbine, designed to operate unmanned and housed within a container, represents an innovative approach to harnessing residual energy in drinking water pipelines. The research methodology leverages similarity laws derived from a previously developed high-efficiency turbine facility as a foundation for the preliminary design. This approach diverges from conventional turbine design methods, offering significant time and cost efficiencies. It should be noted that similarity laws were used only for the preliminary dimensioning of the scale turbine. Following this initial design, design optimizations were carried out based on CFD, focusing on components such as the runner, to enhance performance and achieve the required power output without cavitation at the specified flow rate and head. The results demonstrate that the application of similarity laws expedites the design process while maintaining high efficiency, effectively addressing the unique constraints of the operational environment. Additionally, the study provides a comprehensive analysis of the advantages and limitations of employing similarity in turbine design. In conclusion, this research not only exemplifies a novel turbine design methodology that ensures operational similarity but also serves as a practical guide for reducing costs and design timelines in small hydropower applications.This now clearly states that similarity was used for the preliminary dimensioning, followed by optimization based on CFD.
Kanat Profili-Silindir Konfigürasyonunun Aerodinamik ve Aeroakustik Performansının Sayısal Analizi
(2021) Türkoğlu, Haşmet; Ayli, Ece; Koçak, Eyup; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Fanlar, rüzgâr ve su türbinleri gibi birçok akım makinesinde ve uçak gövdesi bileşenlerinde akışın fiziğinin ve akustik performansının anlaşılmasında, kanat profilisilindir konfigürasyonlarının akış performansından yararlanılmaktadır. Silindirin arkasında meydana gelen kayma tabakası ayrılmaları ve Von Karman girdapları, kanat girişinde parçalanmakta ve birçok küçük yapı meydana getirmektedir. Ortaya çıkan akış-katı yüzey etkileşimine bağlı olarak gürültü ve titreşim meydana gelmektedir. Akım makinelerinde geniş bant gürültüsünün en önemli sebebi, türbülanslı akış ve stator kanat giriş ucu etkileşimidir. Bundan dolayı akım makineleri gürültüsünün analizi için, kanat profili-silindir konfigürasyonu modellemesi yapılır. Bu çalışmada, kanat profili dairesel silindirin iz bölgesine yerleştirilerek sayısal simülasyonlar yapılmıştır. Simülasyonlar için Large Eddy Simulation (LES) metodu kullanılmıştır. Sayısal sonuçlar literatürdeki deneysel çalışmalar ile karşılaştırılarak sonuçlar doğrulandıktan sonra, farklı çaplardaki silindirler için simülasyonlar yapılarak, silindir çapının girdap oluşum bölgesi, akış birleşme noktası, akış ayrılma noktası, basınç dağılımı ve ses basınç seviyesi üzerindeki etkileri incelenmiştir. Elde edilen sonuçlar, Strouhal sayısındaki artış ile ses basınç seviyelerinin yükseldiğini göstermiştir.
Citation - WoS: 6
Citation - Scopus: 6
Machine Learning Based Developing Flow Control Technique Over Circular Cylinders
(Asme, 2023) Turkoglu, Hasmet; Ayli, Ece; Kocak, Eyup; 265836; 283455; 12941; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This 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.
Machine Learning-Based Efficiency Prediction of Francis Type Hydraulic Turbines Through Comprehensive Performance Testing
(Sage Publications Ltd, 2025) Besni, Ferdi; Buyuksolak, Fevzi; Ayli, Ece; Celebioglu, Kutay; Aradag, Selin; Tascioglu, Yigit; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
In this study, the rehabilitation works carried out for the KEPEZ HPP, which has been in operation for over 50 years in Antalya, Turkey, is discussed. Within this scope, the existing turbine components are optimized using the CFD method, and a design that provides higher performance at the required flow rate and head is obtained. Analyses are performed using numerical methods to examine the behavior of the new turbine at different flow rates and heads, and a hill chart is created. In the second stage, model tests are carried out at the TOBB ETU HYDRO Water Turbine Design and Test Center in accordance with IEC60193 standards. Different ML methods are examined for their ability to predict turbine performance, following the development of the hydrid CFD-Experimental methodology. According to the authors knowledge, there is no study in the literature that combines experimental, numerical, and ML methods for turbines, and ML methods have not been applied before for Francis-type turbine performance prediction. The outcomes of the study contribute to the advancement of turbine design and optimization processes, offering valuable insights for the successful implementation of rehabilitation projects in the hydropower sector.
Citation - WoS: 2
Citation - Scopus: 2
Machine Learning-Driven Approach for Reducing Tool Wear in Die-Sinking Electrical Discharge Machining
(Springer Heidelberg, 2025) Cogun, Can; Ayli, Ece; 06.08. Mekatronik Mühendisliği; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This study examines the use of machine learning (ML) techniques to optimize the basic machining parameters and protrusion dimensions that affect tool shape degeneration in die-sinking electric discharge machining (EDM). The primary objective is to decrease errors and enhance prediction and optimization effectiveness. This study introduces a completely novel tool geometry model aimed at minimizing tool shape degeneration, which, to our knowledge, has not been previously documented in the literature. Additionally, this research represents the first instance of employing ML techniques to generate data for addressing this specific type of problem, further advancing the field of die-sinking EDM. The pivotal machining parameters include discharge current, pulse time and machining depth. Three ML approaches are implemented in this investigation: Artificial Neural Network (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). In comparison with experimental outcomes, the ANN technique exhibited superior predictive ability with an coefficient of determination (R2) of 0.99985 and an Mean Relative Error (MRE) of 0.854%. Four distinct EDM machining scenarios are presented and machining parameters and protrusion dimensions are optimized using the ANN technique to decrease tool shape degeneration. Optimizing the machining parameters and diagonal dimensions of the protrusion substantially reduced tool shape degeneration. This research demonstrates the effectiveness of ANN in optimizing machining parameters and improving tool performance in die-sinking EDM. A significant reduction in total wear area of 66.7% was achieved with a considerably lower time cost through the optimized ANN network. While the study demonstrates promising results, its reliance on specific datasets for training may limit the generalizability of the model to broader machining scenarios.
Citation - WoS: 5
Citation - Scopus: 4
Mitigating Cavitation Effects on Francis Turbine Performance: a Two-Phase Flow Analysis
(Pergamon-elsevier Science Ltd, 2025) Altintas, Burak; Ayli, Ece; Celebioglu, Kutay; Aradag, Selin; Tascioglu, Yigit; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
Due to their ability to operate over a wide range of flow rates and generate high power, Francis turbines are the most widely used of hydroturbine type. Hydraulic turbines, are designed for specific flow and head conditions tailored to site conditions. However, Francis turbines can also be operated outside of design conditions due to varying flow and head values. Operation outside of design conditions can lead to cavitation. In this study, singlephase steady-state an alyses were conducted initially to examine cavitation in detail, followed by two-phase transient analyses. The results obtained from these analyses were compared to determine the cavitation characteristics of the designed turbine. The steady-state simulation results indicate the occurrence of cavitation, including traveling bubble and draft tube cavitation, under overload operating conditions. However, these cavitation characteristics are not observed in the two-phase transient simulation results under the same operating conditions. Additionally, the turbine efficiency is predicted to be higher in the transient simulation results. This is attributed to the frozen rotor interface used in the steady-state simulations, which over predicts flow irregularities. The reduced flow irregularities in the transient results have resulted in lower cavitation and losses, leading to higher predicted turbine efficiency.
Citation - WoS: 20
Citation - Scopus: 20
Modeling of Mixed Convection in an Enclosure Using Multiple Regression, Artificial Neural Network, and Adaptive Neuro-Fuzzy Interface System Models
(Sage Publications Ltd, 2020) Ayli, Ece; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
In 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.
Citation - WoS: 9
Citation - Scopus: 8
A 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; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
This 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).
Numerical Investigation of Rod-Airfoil Configuration Aeroacoustic Characteristics Using Ffowcs-Williams Equations
(2021) Koçak, Eyup; Turkoglu, Hasmet; Ayli, Ece; 265836; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
The rod-airfoil configuration is a fundamental study to understand sound generation processes and the acoustic phenomena in the application of turbines, fans, and airfoils. In the present research, the noise that is originated by the rod-airfoil configuration is examined using numerical methods which are Large Eddy Simulation (LES), and Reynolds Averaged Navier Stokes (RANS) models, coupled with an FFOWCS-WILLIAMSHAWKINGS (FW-H) technique. For the RANS method, k-ω SST and Spalart Allmaras (S-A) turbulence models are utilized in order to investigate the capability of different models for the analysis of the aeroacoustic flow field. The ANSYS FLUENT solver is chosen to carry out the numerical simulations. The examined rod and chord diameter Reynolds numbers are 48000 and 480000, respectively and the Mach number is 0.2. Results are obtained for both in the near field and acoustic far-field. The obtained numerical results are verified with an experimental study from the literature, and the results of both approaches are compared with each other and the experiment. Comparisons are performed for mean velocity profiles in the rod and airfoil wakes, pressure spectra and power spectral density. The results obtained show that LES is preferable for this problem as it is capable of capturing the flow separation, reattachments, vortex street, and various length scales of turbulence. Although both RANS and LES methods provide a consistent flow field with experimental methods, the RANS approach overestimates the vortex shedding frequency and Strouhal number. The RANS model predicts the flow field well; however, it overestimates the noise spectra. The LES model predicts satisfactory acoustic spectra.
Citation - WoS: 2
Numerical Investigation of Rod-Airfoil Configuration Aeroacoustic Characteristics Using Ffowcs-Williams Equations
(Yildiz Technical Univ, 2021) Kocak, Eyup; Turkoglu, Hasmet; Ayli, Ece; 12941; 06.06. Makine Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
The rod-airfoil configuration is a fundamental study to understand sound generation processes and the acoustic phenomena in the application of turbines, fans, and airfoils. In the present research, the noise that is originated by the rod-airfoil configuration is examined using numerical methods which are Large Eddy Simulation (LES), and Reynolds Averaged Navier Stokes (RANS) models, coupled with an FFOWCS-WILLIAMS-HAWKINGS (FW-H) technique. For the RANS method, k-omega SST and Spalart Allmaras (S-A) turbulence models are utilized in order to investigate the capability of different models for the analysis of the aeroacoustic flow field. The ANSYS FLUENT solver is chosen to carry out the numerical simulations. The examined rod and chord diameter Reynolds numbers are 48000 and 480000, respectively and the Mach number is 0.2. Results are obtained for both in the near field and acoustic far-field. The obtained numerical results are verified with an experimental study from the literature, and the results of both approaches are compared with each other and the experiment. Comparisons are performed for mean velocity profiles in the rod and airfoil wakes, pressure spectra and power spectral density. The results obtained show that LES is preferable for this problem as it is capable of capturing the flow separation, reattachments, vortex street, and various length scales of turbulence. Although both RANS and LES methods provide a consistent flow field with experimental methods, the RANS approach overestimates the vortex shedding frequency and Strouhal number. The RANS model predicts the flow field well; however, it overestimates the noise spectra. The LES model predicts satisfactory acoustic spectra.