[HTML][HTML] Machine-learning methods for computational science and engineering

M Frank, D Drikakis, V Charissis - Computation, 2020 - mdpi.com
The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

Recent developments on viscosity and thermal conductivity of nanofluids

L Yang, J Xu, K Du, X Zhang - Powder technology, 2017 - Elsevier
The physical properties and especially viscosity and thermal conductivity are essential
parameters for evaluating the heat transfer and flowing drag coefficients when designing a …

Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: Experimental data and modeling using artificial neural network and correlation

MH Esfe, S Wongwises, A Naderi, A Asadi… - … communications in heat …, 2015 - Elsevier
In the present paper, the thermal conductivity of hybrid nanofluids is experimentally
investigated. The studied nanofluid was produced using a two-step method by dispersing …

Experimental study on thermal conductivity of water-based Fe3O4 nanofluid: development of a new correlation and modeled by artificial neural network

M Afrand, D Toghraie, N Sina - … Communications in Heat and Mass Transfer, 2016 - Elsevier
In this paper, the thermal conductivity of Fe 3 O 4 magnetic nanofluids has been investigated
experimentally. The nanofluid samples were prepared using a two-step method by …

Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN)

E Heidari, MA Sobati, S Movahedirad - Chemometrics and intelligent …, 2016 - Elsevier
Viscosity is a significant physical property of nanofluids in practical heat transfer
applications. No general model is capable for accurate prediction of nanofluid viscosity in a …

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

M Vafaei, M Afrand, N Sina, R Kalbasi, F Sourani… - Physica E: Low …, 2017 - Elsevier
In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been
predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1 …

Develop optimal network topology of artificial neural network (AONN) to predict the hybrid nanofluids thermal conductivity according to the empirical data of Al2O3–Cu …

Y Peng, A Parsian, H Khodadadi, M Akbari… - Physica A: Statistical …, 2020 - Elsevier
An artificial neural network (ANN) approach is used to determine the thermal conductivity of
Al 2 O 3–Cu/EG with an equal volume (50: 50). For this purpose, a mixture of Al 2 O 3 and …

The viscosity of nanofluids: a review of the theoretical, empirical, and numerical models

JP Meyer, SA Adio, M Sharifpur… - Heat Transfer …, 2016 - Taylor & Francis
The enhanced thermal characteristics of nanofluids have made it one of the most raplidly
growing research areas in the last decade. Numerous researches have shown the merits of …

A review on nanofluids in minimum quantity lubrication machining

S Chinchanikar, SS Kore, P Hujare - Journal of Manufacturing Processes, 2021 - Elsevier
Nanofluids in minimum quantity lubrication (MQL) machining is gaining grounds with
environment consciousness enhanced laws and regulations consummated. Researchers …

Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network

L Shi, S Zhang, A Arshad, Y Hu, Y He, Y Yan - Renewable and Sustainable …, 2021 - Elsevier
Nanostructured magnetic suspensions have superior thermophysical properties, which have
attracted widespread attention owing to their industrial applications for heat transfer …