Thermal conductivity of a two-layer wall for a time-varying heat-transfer coefficient and ambient temperature

1970 ◽  
Vol 18 (2) ◽  
pp. 239-242 ◽  
Author(s):  
I. M. Prikhod'ko
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Ambient Temperature ◽  
Transfer Coefficient ◽  
Time Varying

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

Experimental Convective Heat Transfer With Nanofluids

2008 ◽  
Author(s):  
S. Kabelac ◽  
K. B. Anoop
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Turbulent Regime ◽  
Heat Transfer Characteristics ◽  
Forced Convective Heat Transfer ◽  
Transfer Characteristics

Nanofluids are colloidal suspensions with nano-sized particles (<100nm) dispersed in a base fluid. From literature it is seen that these fluids exhibit better heat transfer characteristics. In our present work, thermal conductivity and the forced convective heat transfer coefficient of an alumina-water nanofluid is investigated. Thermal conductivity is measured by a steady state method using a Guarded Hot Plate apparatus customized for liquids. Forced convective heat transfer characteristics are evaluated with help of a test loop under constant heat flux condition. Controlled experiments under turbulent flow regime are carried out using two particle concentrations (0.5vol% and 1vol %). Experimental results show that, thermal conductivity of nanofluids increases with concentration, but the heat transfer coefficient in the turbulent regime does not exhibit any remarkable increase above measurement uncertainty.

Investigations on Transient Natural Convection in Boron Nitride-Mineral Oil Nanofluid Systems

2012 ◽  
Author(s):  
Shijo Thomas ◽  
C. B. Sobhan ◽  
Jaime Taha-Tijerina ◽  
T. N. Narayanan ◽  
P. M. Ajayan
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Natural Convection ◽  
Boron Nitride ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Convective Heat Transfer Coefficient ◽  
Mineral Oil

Nanofluids are suspensions or colloids produced by dispersing nanoparticles in base fluids like water, oil or organic fluids, so as to improve their thermo-physical properties. Investigations reported in recent times have shown that the addition of nanoparticles significantly influence the thermophysical properties, such as the thermal conductivity, viscosity, specific heat and density of base fluids. The convective heat transfer coefficient also has shown anomalous variations, compared to those encountered in the base fluids. By careful selection of the parameters such as the concentration and the particle size, it has been possible to produce nanofluids with various properties engineered depending on the requirement. A mineral oil–boron nitride nanofluid system, where an increased thermal conductivity and a reduced electrical conductivity has been observed, is investigated in the present work to evaluate its heat transfer performance under natural convection. The modified mineral oil is produced by chemically dispersing boron nitride nanoparticles utilizing a one step method to obtain a stable suspension. The mineral oil based nanofluid is investigated under transient free convection heat transfer, by observing the temperature-time response of a lumped parameter system. The experimental study is used to estimate the time-dependent convective heat transfer coefficient. Comparisons are made with the base fluid, so that the enhancement in the heat transfer coefficient under natural convection situation can be estimated.

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