Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH)2–EG using experimental data

By M. Hemmat Esfe, Masoud Afrand, Somchai Wongwises, A. Naderi, Amin Asadi, S. Rostami, Mohammad Kazem Akbari. 2015

135 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/b44277aa40022e432bda9a88fc92533ad8e3ae71

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. Masoud Afrand
2. Hamed Eshgarf
3. Davood Toghraie
4. Arash Karimipour
5. Mohammad Kazem Akbari
6. Liu Yang
7. M. Hemmat Esfe
8. Hafiz Muhammad Ali

In Materials Chemistry

1. Masoud Afrand
2. Hamed Eshgarf

Citations in the corpus

• Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid (2016, unknown): “[29] Mg(OH)2/ EG knf kbf 1⁄4 0:995 þ 4:28 uþ 0:134 uT :00011T 0:00908 sinð0:981 þ 0:0201T þ 3:02 10(2)u(3) þ 0:000691T(2) þ 2:22 10(7)Tu(4) þ 9:78 10(3)Tu(2) 2:75 10(3) u 49:8 uT 3:89 10(5)u(2) 8:28 10(8) u(4) 7:6 10(5)Tu(3)Þ”
• An experimental study on rheological behavior of non-Newtonian hybrid nano-coolant for application in cooling and heating systems (2016, background): “These suspensions, called nanofluids, have been widely investigated in the recent decade [12–19].”