Cargando…
An optimal feed-forward artificial neural network model and a new empirical correlation for prediction of the relative viscosity of Al(2)O(3)-engine oil nanofluid
This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al(2)O(3)/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity ([Formula: see text] ) is evaluated at volume fractions (...
Autores principales: | Hemmat Esfe, Mohammad, Toghraie, Davood |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382827/ https://www.ncbi.nlm.nih.gov/pubmed/34426630 http://dx.doi.org/10.1038/s41598-021-96594-z |
Ejemplares similares
-
Optimal viscosity modelling of 10W40 oil-based MWCNT (40%)-TiO(2) (60%) nanofluid using Response Surface Methodology (RSM)
por: Hemmat Esfe, Mohammad, et al.
Publicado: (2022) -
A well-trained artificial neural network for predicting the rheological behavior of MWCNT–Al(2)O(3) (30–70%)/oil SAE40 hybrid nanofluid
por: Esfe, Mohammad Hemmat, et al.
Publicado: (2021) -
Investigating the rheological behavior of a hybrid nanofluid (HNF) to present to the industry
por: Hemmat Esfe, Mohammad, et al.
Publicado: (2022) -
Experimental Study of Rheological Behavior of MWCNT-Al(2)O(3)/SAE50 Hybrid Nanofluid to Provide the Best Nano-lubrication Conditions
por: Hemmat Esfe, Mohammad, et al.
Publicado: (2022) -
Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids
por: Zhao, Ningbo, et al.
Publicado: (2017)