M
Mojtaba Mirzaee
Researcher at Islamic Azad University
Publications - 8
Citations - 335
Mojtaba Mirzaee is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Heat exchanger & Drilling fluid. The author has an hindex of 6, co-authored 8 publications receiving 221 citations. Previous affiliations of Mojtaba Mirzaee include Payame Noor University & Energy Institute.
Papers
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Journal ArticleDOI
Factorial experimental design for the thermal performance of a double pipe heat exchanger using Al2O3-TiO2 hybrid nanofluid
Heydar Maddah,Reza Aghayari,Mojtaba Mirzaee,Mohammad Hossein Ahmadi,Milad Sadeghzadeh,Ali J. Chamkha,Ali J. Chamkha +6 more
TL;DR: In this article, a double pipe heat exchanger with loaded Al2O3-TiO2 hybrid nanofluid in turbulent flow regimes is studied and evaluated through exergy analysis.
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Hydrogen and syngas production by catalytic biomass gasification
TL;DR: In this article, the authors investigated the catalytic activity of two different kinds of metal catalysts (Ni/CeO2/Al2O3) with various catalyst loadings at various residence time (20, 40, and 60min) and gasification temperature (750, 825, and 900 ǫ c).
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Thermodynamic analyses of different scenarios in a CCHP system with micro turbine – Absorption chiller, and heat exchanger
Mojtaba Mirzaee,Reza Zare,Milad Sadeghzadeh,Heydar Maddah,Mohammad Hossein Ahmadi,Emin Açıkkalp,Lingen Chen +6 more
TL;DR: In this paper, a cogeneration system that includes a gas turbine, absorption chillers, boilers, and heat exchangers is modeled in EES software, and the system is studied in multiple scenarios.
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Investigating Created Properties of Nanoparticles Based Drilling Mud
TL;DR: In this article, the effect of adding Al2O3 and TiO2 nanoparticles into the drilling mud was investigated, and it was shown that they can increase gel strength, reduce capillary suction time and decrease formation damage.
Stuck Drill Pipe Prediction with Networks Neural in Maroon Field
TL;DR: In this paper, the authors used feed forward neural networks and back propagation network training to predict pipe sticking related to pressure difference, well narrowness (Mobile and Chili circulation), weak hydraulic of drilling mud, weak geology of drilling sand, non-regulated drilling line, with geological effects were estimated during drilling.