Thermal conductivity of non-Newtonian nanofluids: Experimental data and modeling using neural network
By Mohammad Hojjat, S. Etemad, R. Bagheri, Jules Thibault. 2011
214 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/8d94b9e806de6cfbbe8566f0280e8b7caae3e750
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Davood Toghraie
- Masoud Afrand
- Arash Karimipour
- M. Hemmat Esfe
- Mohammad Reza Safaei
- Marjan Goodarzi
- Mohammad Kazem Akbari
- Somchai Wongwises
In Mechanical Engineering
- Davood Toghraie
- Arash Karimipour
- Masoud Afrand
- M. Hemmat Esfe
- Mohammad Reza Safaei
- Marjan Goodarzi
- Mohammad Kazem Akbari
- Somchai Wongwises
Citations in the corpus
- Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH)2–EG using experimental data (2015, methodology): “[17] have investigated the impact of nanoparticle concentration and the type of nanoparticles on nanofluid thermal conductivity using the neural network of thermal”
- Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network (2014, methodology): “[19] investigated the thermal conductivity of non-Newtonian nanofluids using artificial neural network and compared the data with experimental data.”
- Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods (2015, background): “[27] conducted an experimental study on thermal conductivity of three different nanofluids, alumina, titania, and CuO.”
- Evaluation of thermal conductivity of COOH-functionalized MWCNTs/water via temperature and solid volume fraction by using experimental data and ANN methods (2015, background): “Many researchers have studied an application of ANN in the modeling thermal conductivity in different nanofluids and thermophysical properties [13, 19, 20].”
- Natural convection of sodium alginate (SA) non-Newtonian nanofluid flow between two vertical flat plates by analytical and numerical methods (2014, methodology): “[11] prepared three different types of nanofluids by dispersing Al2O3, TiO2 and CuO nanoparticles in a 0.”
- Latest developments on the viscosity of nanofluids (2012, unknown): “having different contents including heat transfer enhancement [5–7], thermal conductivity measurement [8–10], thermal conductivity enhancement [11–21], effective thermal conductivity [22– 28], thermal conductivity of suspensions [29–31], thermal properties enhancement [32], thermal transport [33], t”