Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{JAMEI:2020:ICHMT,
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author = "Mehdi Jamei and Rashid Pourrajab and
Iman Ahmadianfar and Aminreza Noghrehabadi",
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title = "Accurate prediction of thermal conductivity of
ethylene glycol-based hybrid nanofluids using
artificial intelligence techniques",
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journal = "International Communications in Heat and Mass
Transfer",
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volume = "116",
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pages = "104624",
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year = "2020",
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ISSN = "0735-1933",
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DOI = "doi:10.1016/j.icheatmasstransfer.2020.104624",
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URL = "http://www.sciencedirect.com/science/article/pii/S0735193320301512",
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keywords = "genetic algorithms, genetic programming, Hybrid
nanofluid, Thermal conductivity, Volume fraction, Model
tree",
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abstract = "Accurate prediction of thermal conductivity of hybrid
nanofluids is very important for industries such as
microelectronics and cooling applications that heavily
rely on the heat transfer. Many experimental
investigations are conducted aiming at developing
correlations to predict the relative thermal
conductivity of hybrid nanofluids. However, the
proposed correlations are limited to specific types of
hybrid nanofluids. In this research, for the first time
three soft computing techniques namely, Genetic
programming (GP), Model tree (MT) and Multi linear
regression (MLR) models, were developed and used to
accurately predict the thermal conductivity of various
ethylene glycol (EG)-based hybrid nanofluids. A total
of 275 datasets from literature were collected and
divided into the testing and training groups. The
results obtained from the proposed approaches were
compared with a number of performance metrics and
empirical correlations. The performance criteria
indicated that the GP model for the test dataset (R =
0.950, RMSE = 0.0225) had the best prediction
performance for the relative thermal conductivity of
hybrid nanofluids in comparison to MT (R = 0.928, RMSE
=0.0301) and MLR (R = 0.787, RMSE =0.050),
respectively. Sensitivity analysis showed that the
nanoparticle volume fraction (R = 0.445, SI = 0.0667)
was the most influential factor among all model input
parameters",
- }
Genetic Programming entries for
Mehdi Jamei
Rashid Pourrajab
Iman Ahmadianfar
Aminreza Noghrehabadi
Citations