http://hdl.handle.net/20.500.12416/263 Makine Mühendisliği Bölümü yayınlarını içerir. Wed, 28 Oct 2026 17:07:32 GMT 2026-10-28T17:07:32Z http://hdl.handle.net/20.500.12416/8467 Fused filament fabrication in CAD education: A closed-loop approach Totuk, Onat Halis; Selvi, Özgün; Akar, Samet Integrating low-cost fused filament fabrication 3D printing as a foundation for learning 3D modelling is explored. This method blends traditional computer aided design (CAD) instruction with additive manufacturing possibilities. Experimental results demonstrate increased comprehension speed and reduced learning time. This hands-on approach empowers students by enabling direct engagement with the modelling process. Analogous to reverse engineering, the strategy instructs engineering students from final product to model creation, closing the gap between theory and practice. Incorporating 3D printing bridges this divide, enhancing understanding, creativity and problem-solving. The study underscores technology's influence on learning strategies, aligning with the surge of 3D printing in education. Results link advanced design technology usage to improved student performance, with 3D-printed materials yielding 45% higher grades and 30% faster task completion. This study advocates curricular advancement for design-focused careers through enhanced technology integration and favourable 3D printing model reception. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/20.500.12416/8467 2023-01-01T00:00:00Z http://hdl.handle.net/20.500.12416/8459 Exploring the potential of artificial intelligence tools in enhancing the performance of an inline pipe turbine Çelebioğlu, Kutay; Aylı, Ece; Çetintürk, Hüseyin; Taşçıoğlu, Yiğit; Aradağ, 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. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/20.500.12416/8459 2024-01-01T00:00:00Z http://hdl.handle.net/20.500.12416/8416 Analysis of heat transfer enhancement of passive methods in tubes with machine learning Özgirgin Yapıcı, Ekin; Aylı, Ece; Türkoğlu, Haşmet 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. Mon, 01 Apr 2024 00:00:00 GMT http://hdl.handle.net/20.500.12416/8416 2024-04-01T00:00:00Z http://hdl.handle.net/20.500.12416/8403 A novel CFD-ANN approach for plunger valve optimization: Cost-effective performance enhancement Kaak, Abdul Rahman Sabra; Çelebioğlu, Kutay; Bozkuş, Zafer; Ulucak, Oğuzhan; Aylı, Ece 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). Mon, 01 Jul 2024 00:00:00 GMT http://hdl.handle.net/20.500.12416/8403 2024-07-01T00:00:00Z