M
Mousa Rejvani
Researcher at Semnan University
Publications - 16
Citations - 1293
Mousa Rejvani is an academic researcher from Semnan University. The author has contributed to research in topics: Nanofluid & Viscosity. The author has an hindex of 12, co-authored 15 publications receiving 1022 citations. Previous affiliations of Mousa Rejvani include Islamic Azad University & Imam Hossein University.
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An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management
TL;DR: In this article, the authors deal with the measurement of thermal conductivity of SWCNTs-MgO/EG hybrid nanofluids and the modeling of experimental data using artificial neural network (ANN).
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Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data
Mohammad Hemmat Esfe,Mohammad Reza Hassani Ahangar,Mousa Rejvani,Davood Toghraie,Mohammad Hadi Hajmohammad +4 more
TL;DR: In this article, a neural network with one hidden layer and four neurons has been used to estimate the viscosity of the aqueous nanofluid of TiO2 using experimental data.
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Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating
TL;DR: In this paper, an artificial neural network (MLP) was used to predict the viscosity in terms of temperature, solid volume fraction and shear stress, which showed more accurate results in comparison with correlation results.
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Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications
TL;DR: In this article, a new correlation was proposed to predict experimental TCR (thermal conductivity ratio) based on the solid volume fraction and the temperature, and an ANN was designed for TCR data modeling and forecasting.
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Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data
TL;DR: In this article, the effects of temperature and volume fraction on thermal conductivity of SWCNT-Al2O3/EG hybrid nanofluid are investigated, and an experimental correlation and a neural network are presented and for thermal conductivities of the nanoprocessor in terms of volume fraction and temperature.