WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article 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, YigitIn 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.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 Citation - WoS: 2Citation - Scopus: 1Exploring 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, SelinIn 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.