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====Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids====
==Authors=={{image class="right" url="images/publications/NE2012.gif" title="NE2012 cover" alt="NE2012 cover"}}
Giovanni A. Longo, //Department of Management and Engineering, University of Padova, Italy //
Claudio Zilio, //Department of Management and Engineering, University of Padova, Italy //
Elena Ceseracciu, //Department of Management and Engineering, University of Padova, Italy //
Monica Reggiani
==Published in==
NANO ENERGY
In Press,
doi>[[http://dx.doi.org/10.1016/j.nanoen.2011.11.007 10.1016/j.nanoen.2011.11.007]]
==Abstract==
This paper presents a 3-input and a 4-input Artificial Neural Network (ANN) model for the prediction of the thermal conductivity of oxide–water nanofluids. Both models account for the effect of temperature, nanoparticle volume fraction, and nanoparticle thermal conductivity, whereas the 4-input model also considers the effect of nanoparticle cluster average size. The models have been trained on a set of data obtained by the present authors and tested both with cross-validation and on data coming from other authors. Both models show a reasonable agreement in predicting experimental data, even if the 4-input model exhibits better performance. The inclusion of the cluster average size within the input variables improves the predicting performance of the ANN nanofluid thermal conductivity model; however, this parameter is usually missing from data presented in literature. The characteristic parameters of the presented ANN models are fully reported in the paper.
==Keywords==
Nanofluids; Thermal conductivity; Artificial Neural Network
==Bibtex==
%%(latex)
@article{NE2012,
Author = "G. A. Longo and C. Zilio and E. Ceseracciu and M. Reggiani",
Title = {Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids},
Journal = {Nano Energy},
Volume = {},
Number = {0},
Pages = { - },
Year = {2011},
Issn = {2211-2855},
Doi = {10.1016/j.nanoen.2011.11.007},
}
%%
----
====Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids====
==Authors=={{image class="right" url="images/publications/NE2012.gif" title="NE2012 cover" alt="NE2012 cover"}}
Giovanni A. Longo, //Department of Management and Engineering, University of Padova, Italy //
Claudio Zilio, //Department of Management and Engineering, University of Padova, Italy //
Elena Ceseracciu, //Department of Management and Engineering, University of Padova, Italy //
Monica Reggiani
==Published in==
NANO ENERGY
In Press,
doi>[[http://dx.doi.org/10.1016/j.nanoen.2011.11.007 10.1016/j.nanoen.2011.11.007]]
==Abstract==
This paper presents a 3-input and a 4-input Artificial Neural Network (ANN) model for the prediction of the thermal conductivity of oxide–water nanofluids. Both models account for the effect of temperature, nanoparticle volume fraction, and nanoparticle thermal conductivity, whereas the 4-input model also considers the effect of nanoparticle cluster average size. The models have been trained on a set of data obtained by the present authors and tested both with cross-validation and on data coming from other authors. Both models show a reasonable agreement in predicting experimental data, even if the 4-input model exhibits better performance. The inclusion of the cluster average size within the input variables improves the predicting performance of the ANN nanofluid thermal conductivity model; however, this parameter is usually missing from data presented in literature. The characteristic parameters of the presented ANN models are fully reported in the paper.
==Keywords==
Nanofluids; Thermal conductivity; Artificial Neural Network
==Bibtex==
%%(latex)
@article{NE2012,
Author = "G. A. Longo and C. Zilio and E. Ceseracciu and M. Reggiani",
Title = {Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids},
Journal = {Nano Energy},
Volume = {},
Number = {0},
Pages = { - },
Year = {2011},
Issn = {2211-2855},
Doi = {10.1016/j.nanoen.2011.11.007},
}
%%
