Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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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: 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: 4Citation - Scopus: 4Critical Decision Making for Rehabilitation of Hydroelectric Power Plants(Taylor & Francis inc, 2023) Westerman, Jerry; Celebioglu, Kutay; Ayli, Ece; Ulucak, Oguzhan; Aradag, SelinDue 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.