WoS İndeksli Yayınlar Koleksiyonu

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

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Author: search.filters.author.Ayli, EcePublisher: search.filters.publisher.Elsevier Sci Ltd

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  • Article
    Citation - WoS: 13
    Citation - Scopus: 14
    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; Çelebioğlu, Kutay
    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).
  • Article
    Citation - WoS: 52
    Citation - Scopus: 58
    Numerical Investigation on the Performance of a Small Scale Solar Chimney Power Plant for Different Geometrical Parameters
    (Elsevier Sci Ltd, 2020) Yapici, Ekin Ozgirgin; Ayli, Ece; Nsaif, Osama
    In recent decades, demand for energy has been significantly increased, and considering environmental impacts and the degrading nature of fossil fuels, clean and emission-free renewable energy production has attracted a great deal of attention. One of the most promising renewable energy sources is solar energy due to low cost and low harmful emissions, and from the 1980s, one of the most beneficial applications of solar energy is the utilization of solar chimney power plants (SCPP). A SCPP is a simple and reliable system that consists of three main components; a solar collector, a chimney (tower) and a turbine to utilize electrical energy. Recently, by the advancement in computer technology, the use of CFD methodology for studying SCPP has become an extensive, robust and powerful technique. In light of the above, in this study, numerical simulations of a SCPP through three-dimensional axisymmetric modeling is performed. A numerical model is created using CFD software, and the results are verified with an experimental study from the literature. After ensuring good agreement with the experiments, chimney's and collector's geometric parameters effects and different configurations effects on SCPP performance, simultaneously and additively is investigated. The study introduces an insight to the performance enhancement methods and finding the best configuration of a SCPP model, which will be the basis of a detailed prototyping process. Based on the numerical results, the best configuration of the SCPP has been found as the diverging chimney which enhances the generated power. The results of the study showed that the chimney height and collector radius increase has a positive effect on the power output and efficiency of the system, but when construction and material costs are also considered, each has an optimal value. The maximum impact on the performance is found to be by the chimney tower radius and the collector height and inclination are found to have optimum values considering performance. According to the obtained results, the best performance for the SCPP was obtained with 3.5 m chimney height, 30 cm tower diameter, 400 cm of collector diameter with 6 cm height and zero inclination angle. By the correct selection of the dominant performance parameter which can be done by correctly interpreting the results of this study, "the best" design of a SCPP real scale prototype considering maximum power requirement can be done. (C) 2020 Elsevier Ltd. All rights reserved.