Mousa Rejvani

Biomedical Engineering · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 4 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Biomedical Engineering — 99 citations into it; #3,231 of 10,734 by trust here, #2,287 by citations
• Mechanical Engineering — 73 citations into it; #3,555 of 8,300 by trust here, #2,446 by citations
• Renewable Energy, Sustainability and the Environment — 28 citations into it
• Computational Mechanics — 11 citations into it

Whom Mousa Rejvani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. M. Hemmat Esfe
2. Seyfolah Saedodin

Who trusts Mousa Rejvani overall

1. M. Hemmat Esfe
2. Mohammad Hossein Ahmadi

Papers from Mousa Rejvani's perspective

Ordered by how strongly the researchers Mousa Rejvani trusts endorse each paper. Same corpus, a different order for every researcher.

1. Effect of volume concentration and temperature on viscosity and surface tension of graphene–water nanofluid for heat transfer applications (Journal of Thermal Analysis and Calorimetry)
2. A review on hybrid nanofluids: Recent research, development and applications
3. Turbulent forced convection heat transfer and thermophysical properties of Mgo–water nanofluid with consideration of different nanoparticles diameter, an empirical study (Journal of Thermal Analysis and Calorimetry)
4. Study of thermal conductivity of nanofluids for the application of heat transfer fluids
5. Experimental studies on the convective heat transfer performance and thermophysical properties of MgO–water nanofluid under turbulent flow
6. Thermal conductivity and particle agglomeration in alumina nanofluids: experiment and theory. (Physical review. E, Statistical, nonlinear, and soft matter physics)
7. New temperature dependent thermal conductivity data for water-based nanofluids
8. A critical review on convective heat transfer correlations of nanofluids
9. Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids
10. Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids

Their papers

• An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management (2017) — 253 citations
• Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data (2017) — 141 citations
• Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications (2018) — 176 citations
• Rheological behavior characteristics of ZrO 2 -MWCNT/10w40 hybrid nano-lubricant affected by temperature, concentration, and shear rate: An experimental study and a neural network simulating (2017) — 194 citations
• Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data (2016) — 212 citations