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Hamed Safikhani

Researcher at Arak University

Publications -  60
Citations -  1274

Hamed Safikhani is an academic researcher from Arak University. The author has contributed to research in topics: Pressure drop & Multi-objective optimization. The author has an hindex of 16, co-authored 54 publications receiving 932 citations. Previous affiliations of Hamed Safikhani include Islamic Azad University & University of Tehran.

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Numerical simulation of flow field in three types of standard cyclone separators

TL;DR: In this paper, numerical simulation of the fluid flow and particle dynamics is presented by CFD techniques to characterize the performance of the three types of standard cyclones, namely, 1D3D, 2D2D and 1D2d.
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Modeling and Pareto based multi-objective optimization of wavy fin-and-elliptical tube heat exchangers using CFD and NSGA-II algorithm

TL;DR: In this paper, a multi-objective optimization of wavy fin-and-elliptical tube heat exchangers has been performed by using Computational Fluid Dynamics (CFD), Artificial Neural Network (ANN) of Group Method of Data Handling (GMDH) type, and Non-Dominated Sorting Genetic Algorithm II (NSGA-II).
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Modeling and multi-objective optimization of cyclone separators using CFD and genetic algorithms

TL;DR: It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of cyclones can be discovered by Pareto based multi-objective optimization of the obtained polynomial meta-models.
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Numerical study of flow field in new cyclone separators

TL;DR: In this paper, a cyclone with the separation space consisting of an outer cylinder and a vortex limiter was designed to improve cyclone performance by increasing the vortex length, and the velocity fluctuations were simulated using the Discrete Random Walk (DRW).
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The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps

TL;DR: In this article, a multi-objective optimization of centrifugal pumps is performed in three steps using polynomial neural networks and particle swarm optimization method (MOPSO) for Pareto-based optimization of the pumps considering two conflicting objectives, η and NPSHr.