S
Seyed Hassan Hashemabadi
Researcher at Iran University of Science and Technology
Publications - 121
Citations - 3676
Seyed Hassan Hashemabadi is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Heat transfer & Pressure drop. The author has an hindex of 26, co-authored 115 publications receiving 2885 citations. Previous affiliations of Seyed Hassan Hashemabadi include Amirkabir University of Technology & Islamic Azad University.
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CFD simulation of viscosity modifier effect on cutting transport by oil based drilling fluid in wellbore
TL;DR: In this paper, the effects of viscosity on particle transport capacity of fluid were studied by considering various particle parameters such as density, diameter, shape, and initial particle concentration and wellbore inclination through using the algebraic slip mixture (ASM) model.
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Numerical Analysis of Drops Coalescence and Breakage Effects on De-Oiling Hydrocyclone Performance
TL;DR: In this article, the effects of breakage and coalescence on de-oiling hydrocyclone performance utilizing Computational Fluid Dynamics (CFD) were investigated, and a comparison between the standard design and the conical inlet chamber design was drawn in terms of separation efficiency for low entrance oil concentration.
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Direct numerical simulation of mass transfer from Taylor bubble flow through a circular capillary
TL;DR: In this article, mass transfer during a Taylor bubble flow regime has been investigated by a volume of fluid (VOF) based numerical method and the validity of Taylor bubble hydrodynamics simulation has been checked by comparing the liquid film thickness and the relative bubble velocity obtained from computational fluid dynamics simulations with reported empirical correlations and experimental results.
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Application of artificial neural networks and support vector regression modeling in prediction of magnetorheological fluid rheometery
TL;DR: In this article, the effects of temperature and magnetic field strength on rheological properties have been modeled by Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods and the results showed that SVR is the most reliable model in predicting shear stress as well as dynamic yield stress (at low shear rate values).
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Local convective heat transfer coefficient and friction factor of CuO/water nanofluid in a microchannel heat sink
TL;DR: Forced convective heat transfer in a microchannel heat sink (MCHS) using CuO/water nanofluids with 0.1 and 0.2vol% as coolant was investigated in this article.