M
Mohsen Hassani
Researcher at Islamic Azad University
Publications - 11
Citations - 644
Mohsen Hassani is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Nanofluid & Thermal conductivity. The author has an hindex of 8, co-authored 11 publications receiving 503 citations. Previous affiliations of Mohsen Hassani include Tehran University of Medical Sciences.
Papers
More filters
Journal ArticleDOI
Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids ☆
TL;DR: In this paper, the effects of solid volume fraction and temperature on thermal conductivity of DWCNT(inner diameter of 3-nm)-ZnO(diameter of 10-30nm)/water-ethylene glycol (60:40) nanofluids have been performed using KD2-Pro thermal analyzer in details.
Journal ArticleDOI
Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data
TL;DR: In this article, an optimal artificial neural network was designed to predict the relative viscosity of multi-walled carbon nanotubes/water nanofluid, which is an imperative parameter for calculating the required pumping power and convective heat transfer.
Journal ArticleDOI
Appraising influence of COOH-MWCNTs on thermal conductivity of antifreeze using curve fitting and neural network
TL;DR: In this paper, an artificial neural network was designed to evaluate the effects of temperature and solid volume fraction on the thermal conductivity of nano-antifreeze, and the results showed that the proposed equation has good accuracy for engineering applications.
Journal ArticleDOI
Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation
TL;DR: In this article, an ANN model has been used to predict thermal conductivity of MgO-water/EG (60:40) nanofluids based on experimental data.
Journal ArticleDOI
Investigation of material removal rate and surface roughness in wire electrical discharge machining process for cementation alloy steel using artificial neural network
TL;DR: In this article, a linear regression model and feedforward backpropagation neural network were established to predict surface roughness and material removal rate for effective machining in wire electro-discharge machining.