Enhancement of heat transfer coefficient multi-metallic nanofluid with anfis modeling for thermophysical properties

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Abstract

The Cu and Zn-water nanofluid is a suspension of the Cu and Zn nanoparticles with the size 50 nm in the water base fluid for different volume fractions to enhance its thermophysical properties. The determination and measuring the enhancement of thermophysical properties depends on many limitations. Nanoparticles were suspended in a base fluid to prepare a nanofluid. A coated transient hot wire apparatus was calibrated after the building of the all systems. The vibroviscometer was used to measure the dynamic viscosity. The measured dynamic viscosity and thermal conductivity with all parameters affected on the measurements such as base fluids thermal conductivity, volume factions, and the temperatures of the base fluid were used as input to the artificial neural fuzzy inference system to modeling both dynamic viscosity and thermal conductivity of the nanofluids. Then, the adaptive neuro-fuzzy inference system modeling equations were used to calculate the enhancement in heat transfer coefficient using computational fluid dynamics software. The heat transfer coefficient was determined for flowing flow in a circular pipe at constant heat flux. It was found that the thermal conductivity of the nanofluid was highly affected by the volume fraction of nanoparticles. A comparison of the thermal conductivity ratio for different volume fractions was undertaken. The heat transfer coefficient of nanofluid was found to be higher than its base fluid. Comparisons of convective heat transfer coefficients for Cu and Zn nanofluids with the other correlation for the nanofluids heat transfer enhancement are presented. Moreover, the flow demonstrates anomalous enhancement in heat transfer nanofluids.

Original languageEnglish
Pages (from-to)1613-1620
Number of pages8
JournalThermal Science
Volume19
Issue number5
DOIs
Publication statusPublished - 2015

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Heat transfer coefficients
Thermal conductivity
Thermodynamic properties
Fluids
Volume fraction
Fuzzy inference
Viscosity
Nanoparticles
Heat transfer
Heat flux
Water
Computational fluid dynamics
Pipe
Wire
Temperature

Keywords

  • ANFIS modeling
  • Cu-water
  • Heat transfer enhancement
  • Nanofluid
  • Zn-water

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

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title = "Enhancement of heat transfer coefficient multi-metallic nanofluid with anfis modeling for thermophysical properties",
abstract = "The Cu and Zn-water nanofluid is a suspension of the Cu and Zn nanoparticles with the size 50 nm in the water base fluid for different volume fractions to enhance its thermophysical properties. The determination and measuring the enhancement of thermophysical properties depends on many limitations. Nanoparticles were suspended in a base fluid to prepare a nanofluid. A coated transient hot wire apparatus was calibrated after the building of the all systems. The vibroviscometer was used to measure the dynamic viscosity. The measured dynamic viscosity and thermal conductivity with all parameters affected on the measurements such as base fluids thermal conductivity, volume factions, and the temperatures of the base fluid were used as input to the artificial neural fuzzy inference system to modeling both dynamic viscosity and thermal conductivity of the nanofluids. Then, the adaptive neuro-fuzzy inference system modeling equations were used to calculate the enhancement in heat transfer coefficient using computational fluid dynamics software. The heat transfer coefficient was determined for flowing flow in a circular pipe at constant heat flux. It was found that the thermal conductivity of the nanofluid was highly affected by the volume fraction of nanoparticles. A comparison of the thermal conductivity ratio for different volume fractions was undertaken. The heat transfer coefficient of nanofluid was found to be higher than its base fluid. Comparisons of convective heat transfer coefficients for Cu and Zn nanofluids with the other correlation for the nanofluids heat transfer enhancement are presented. Moreover, the flow demonstrates anomalous enhancement in heat transfer nanofluids.",
keywords = "ANFIS modeling, Cu-water, Heat transfer enhancement, Nanofluid, Zn-water",
author = "Balla, {Hyder H.} and Shahrir Abdullah and {Wan Mahmood}, {Wan Mohd Faizal} and Rozli Zulkifli and Kamaruzzaman Sopian",
year = "2015",
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T1 - Enhancement of heat transfer coefficient multi-metallic nanofluid with anfis modeling for thermophysical properties

AU - Balla, Hyder H.

AU - Abdullah, Shahrir

AU - Wan Mahmood, Wan Mohd Faizal

AU - Zulkifli, Rozli

AU - Sopian, Kamaruzzaman

PY - 2015

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N2 - The Cu and Zn-water nanofluid is a suspension of the Cu and Zn nanoparticles with the size 50 nm in the water base fluid for different volume fractions to enhance its thermophysical properties. The determination and measuring the enhancement of thermophysical properties depends on many limitations. Nanoparticles were suspended in a base fluid to prepare a nanofluid. A coated transient hot wire apparatus was calibrated after the building of the all systems. The vibroviscometer was used to measure the dynamic viscosity. The measured dynamic viscosity and thermal conductivity with all parameters affected on the measurements such as base fluids thermal conductivity, volume factions, and the temperatures of the base fluid were used as input to the artificial neural fuzzy inference system to modeling both dynamic viscosity and thermal conductivity of the nanofluids. Then, the adaptive neuro-fuzzy inference system modeling equations were used to calculate the enhancement in heat transfer coefficient using computational fluid dynamics software. The heat transfer coefficient was determined for flowing flow in a circular pipe at constant heat flux. It was found that the thermal conductivity of the nanofluid was highly affected by the volume fraction of nanoparticles. A comparison of the thermal conductivity ratio for different volume fractions was undertaken. The heat transfer coefficient of nanofluid was found to be higher than its base fluid. Comparisons of convective heat transfer coefficients for Cu and Zn nanofluids with the other correlation for the nanofluids heat transfer enhancement are presented. Moreover, the flow demonstrates anomalous enhancement in heat transfer nanofluids.

AB - The Cu and Zn-water nanofluid is a suspension of the Cu and Zn nanoparticles with the size 50 nm in the water base fluid for different volume fractions to enhance its thermophysical properties. The determination and measuring the enhancement of thermophysical properties depends on many limitations. Nanoparticles were suspended in a base fluid to prepare a nanofluid. A coated transient hot wire apparatus was calibrated after the building of the all systems. The vibroviscometer was used to measure the dynamic viscosity. The measured dynamic viscosity and thermal conductivity with all parameters affected on the measurements such as base fluids thermal conductivity, volume factions, and the temperatures of the base fluid were used as input to the artificial neural fuzzy inference system to modeling both dynamic viscosity and thermal conductivity of the nanofluids. Then, the adaptive neuro-fuzzy inference system modeling equations were used to calculate the enhancement in heat transfer coefficient using computational fluid dynamics software. The heat transfer coefficient was determined for flowing flow in a circular pipe at constant heat flux. It was found that the thermal conductivity of the nanofluid was highly affected by the volume fraction of nanoparticles. A comparison of the thermal conductivity ratio for different volume fractions was undertaken. The heat transfer coefficient of nanofluid was found to be higher than its base fluid. Comparisons of convective heat transfer coefficients for Cu and Zn nanofluids with the other correlation for the nanofluids heat transfer enhancement are presented. Moreover, the flow demonstrates anomalous enhancement in heat transfer nanofluids.

KW - ANFIS modeling

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KW - Nanofluid

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