Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11526
Title: A novel CFD-ANN approach for plunger valve optimization: Cost-effective performance enhancement
Authors: Kaak, A.R.S.
Çelebioğlu, K.
Bozkuş, Z.
Ulucak, O.
Aylı, E.
Keywords: ANN
CFD
Optimization
Plunger valve
Validation
Cost effectiveness
Efficiency
Learning algorithms
Multilayer neural networks
Network architecture
Artificial neural network approach
Cost effective
Effective performance
Flow configurations
Geometric configurations
Hydraulic flow
Optimisations
Performance enhancements
Plunger valves
Validation
Computational fluid dynamics
Publisher: Elsevier Ltd
Abstract: 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). © 2024 Elsevier Ltd
URI: https://doi.org/10.1016/j.flowmeasinst.2024.102589
https://hdl.handle.net/20.500.11851/11526
ISSN: 0955-5986
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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