WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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CFD and DEM Analysis of Cyclone Separator Performance: Implications of Cylinder-to Ratios for Sustainable Engineering
(Springer Heidelberg, 2025) Ayli, Ece; Kocak, Eyup
This research addresses a common industrial challenge: efficiently separating particles from gas using cyclone separators, a critical component for various applications in sustainable engineering. While several studies have focused on airflow within these separators, this research introduces a novel approach by combining two advanced simulation methods (CFD and DEM) to analyze how different cone heights in a cyclone separator impact its performance. This combined methodology enables the examination of particle movement within the separator, a critical aspect often overlooked in previous studies. By visualizing particle dynamics and analyzing them with DEM, the research underscores the importance of considering particle behavior for obtaining accurate results. Overall, this study enhances our understanding of cyclone separators through state-of-the-art simulations and empirical testing. By elucidating the complex airflow and the influence of geometric design on performance, practical recommendations are provided for the development of more efficient cyclone separators. These improvements can lead to enhanced particle separation and reduced energy consumption, offering significant benefits across multiple industries. The findings reveal that as the conical height-to-total height ratio (h/hc) increases, indicating a more pointed cone, there is a substantial increase in efficiency alongside a minimal and tolerable rise in pressure drop. For instance, at a velocity of 25 m/s, increasing the h/hc ratio from 0.33 to 3 results in a 0.7% reduction in pressure drop and a 14% efficiency increase, contributing to more sustainable operational practices.
Clean Energy Generation in Residential Green Buildings
(inst Engineering Tech-iet, 2019) Aylı, Ülkü Ece; Yapici, Ekin Ozgirgin; Ayli, Ece; Makine Mühendisliği
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.
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
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: 4
Citation - Scopus: 4
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
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.
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
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.
Citation - WoS: 6
Citation - Scopus: 6
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
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: 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
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: 3
Citation - Scopus: 6
A Comprehensive Review of Cyclone Separator Technology
(Wiley, 2024) Ayli, Ece; Kocak, Eyup
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.
Prediction of Noise Generated by Rod-Airfoil Configuration: an Investigation Based on Experiments and Machine Learning
(Sage Publications Ltd, 2024) Kocak, Eyup; Ayli, Ece
This 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.
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
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.

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