Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids
By G. Longo, C. Zilio, Elena Ceseracciu, Monica Reggiani. 2012
141 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/8c8f8eb16e9d8248b9546b0e07fa2365fa5d6e76
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
- Masoud Afrand
- Davood Toghraie
- Arash Karimipour
- M. Hemmat Esfe
- Mohammad Reza Safaei
- Mohammad Kazem Akbari
- Somchai Wongwises
- Omid Mahian
In Electrical and Electronic Engineering
- Davood Toghraie
- Arash Karimipour
- Masoud Afrand
- Mohammad Kazem Akbari
- M. Hemmat Esfe
- Mohammad Reza Safaei
- Somchai Wongwises
- Amin Asadi
In Renewable Energy, Sustainability and the Environment
- Masoud Afrand
- Davood Toghraie
- Arash Karimipour
- M. Hemmat Esfe
- Somchai Wongwises
- Mohammad Kazem Akbari
- Wei‐Mon Yan
- Omid Mahian
Citations in the corpus
- Experimental study on thermal conductivity of DWCNT-ZnO/water-EG nanofluids ☆ (2015, background): “[37] proposed two artificial neural network models for estimating the thermal conductivity of Al2O3/water and TiO2/water nanofluids by the temperature, volume fraction, diameter of nanoparticle and particle thermal conductivity as the input variables.”
- Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation☆ (2015, methodology): “[15] used three-input and four-input artificial neural networks to predict the thermal conductivity ratio of oxide-water nanofluid.”
- 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): “[21], using a neural network and considering various input conditions, predicted the oxide–water nanofluids' thermal conductivity.”
- Evaluation of thermal conductivity of COOH-functionalized MWCNTs/water via temperature and solid volume fraction by using experimental data and ANN methods (2015, unknown): “1 and 2) [13, 23, 24].”
- Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network (2014, methodology): “[20] modeled thermal conductivity of TiO2/water and Al2O3/water nanofluids (with different cluster average size) using a 3-input and a 4-input artificial neural network model.”