Modelling and measuring the thermal conductivity of multi-metallic Zn/Cu nanofluid

Modelling and measuring the thermal conductivity of multi-metallic Zn/Cu nanofluid A metallic nanofluid is a suspension of metallic nanoparticles in a base fluid. Multi-metallic nanoparticles are a combination of two or more types of metallic particles. Such multi-metallic nanoparticles were suspended in water using an ultrasonic vibrator for different total volume fractions and different ratios of metallic/metallic nanoparticles. A transient hot wire setup was built to measure the thermal conductivity of the nanofluid at different temperatures. The experimental results were in good agreement with the results in the literature. Then, the experimental results were used as input data for an adaptive neural fuzzy inference system (ANFIS) to predict the thermal conductivity of the multi-metallic nanofluid. The maximum deviation between the ANFIS results and experimental measurements was 1 %. The predicted results and the experimental data were compared with other models. The ANFIS model was found to have good ability to predict the thermal conductivity of the multi-metallic nanofluid over the range of the experimental results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Chemical Intermediates Springer Journals

Modelling and measuring the thermal conductivity of multi-metallic Zn/Cu nanofluid

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Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer Science+Business Media B.V.
Subject
Chemistry; Catalysis; Physical Chemistry; Inorganic Chemistry
ISSN
0922-6168
eISSN
1568-5675
D.O.I.
10.1007/s11164-012-0799-z
Publisher site
See Article on Publisher Site

Abstract

A metallic nanofluid is a suspension of metallic nanoparticles in a base fluid. Multi-metallic nanoparticles are a combination of two or more types of metallic particles. Such multi-metallic nanoparticles were suspended in water using an ultrasonic vibrator for different total volume fractions and different ratios of metallic/metallic nanoparticles. A transient hot wire setup was built to measure the thermal conductivity of the nanofluid at different temperatures. The experimental results were in good agreement with the results in the literature. Then, the experimental results were used as input data for an adaptive neural fuzzy inference system (ANFIS) to predict the thermal conductivity of the multi-metallic nanofluid. The maximum deviation between the ANFIS results and experimental measurements was 1 %. The predicted results and the experimental data were compared with other models. The ANFIS model was found to have good ability to predict the thermal conductivity of the multi-metallic nanofluid over the range of the experimental results.

Journal

Research on Chemical IntermediatesSpringer Journals

Published: Sep 26, 2012

References

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