Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data

By M. Hemmat Esfe, Mousa Rejvani, R. Karimpour, A. A. Abbasian Arani. Journal of Thermal Analysis and Calorimetry, 2017

141 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b80c66fe71c3b61fd1b0277471c908c9ebba3a22

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

1. M. Alhuyi Nazari
2. Mohammad Hossein Ahmadi
3. Roghayeh Ghasempour
4. Omid Mahian
5. Masoud Afrand
6. M. Hemmat Esfe

In Mechanical Engineering

1. M. Alhuyi Nazari
2. Mohammad Hossein Ahmadi
3. Roghayeh Ghasempour
4. Omid Mahian
5. M. Ramezanizadeh

Citations in the corpus

• A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach (2018, methodology): “models utilized temperature and concentration of solid phase as influential variables [37, 39, 40].”