Exact, analytic temperature distributions of pin fins with constant thermal conductivity and power-law type heat transfer coefficient

2017 ◽  
Vol 47 (1) ◽  
pp. 42-53
Author(s):  
Elyas Shivanian ◽  
Antonio Campo
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Power Law ◽  
Temperature Distributions ◽  
Pin Fins ◽  
Constant Thermal Conductivity

Exact solution of a nonlinear fin problem of temperature-dependent thermal conductivity and heat transfer coefficient

2020 ◽  
Vol 98 (7) ◽  
pp. 700-712 ◽  
Author(s):  
Sheng-Wei Sun ◽  
Xian-Fang Li
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Temperature Distribution ◽  
Transfer Coefficient ◽  
Power Law ◽  
Temperature Dependent ◽  
Fin Efficiency ◽  
Special Cases ◽  
Temperature Dependent Thermal Conductivity

This paper studies a class of nonlinear problems of convective longitudinal fins with temperature-dependent thermal conductivity and heat transfer coefficient. For thermal conductivity and heat transfer coefficient dominated by power-law nonlinearity, the exact temperature distribution is obtained analytically in an implicit form. In particular, the explicit expressions of the fin temperature distribution are derived explicitly for some special cases. An analytical expression for fin efficiency is given as a function of a thermogeometric parameter. The influences of the nonlinearity and the thermogeometric parameter on the temperature and thermal performance are analyzed. The temperature distribution and the fin efficiency exhibit completely different behaviors when the power-law exponent of the heat transfer coefficient is more or less than negative unity.

scholarly journals Analytical Solutions for Steady Heat Transfer in Longitudinal Fins with Temperature-Dependent Properties

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Partner L. Ndlovu ◽  
Raseelo J. Moitsheki
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Power Law ◽  
Analytical Solutions ◽  
Physical Parameters ◽  
Transform Method ◽  
Fin Efficiency ◽  
Analytical Series

Explicit analytical expressions for the temperature profile, fin efficiency, and heat flux in a longitudinal fin are derived. Here, thermal conductivity and heat transfer coefficient depend on the temperature. The differential transform method (DTM) is employed to construct the analytical (series) solutions. Thermal conductivity is considered to be given by the power law in one case and by the linear function of temperature in the other, whereas heat transfer coefficient is only given by the power law. The analytical solutions constructed by the DTM agree very well with the exact solutions even when both the thermal conductivity and the heat transfer coefficient are given by the power law. The analytical solutions are obtained for the problems which cannot be solved exactly. The effects of some physical parameters such as the thermogeometric fin parameter and thermal conductivity gradient on temperature distribution are illustrated and explained.

Optimum Length for Pin Fins Used in Electronic Cooling

2011 ◽  
Vol 110-116 ◽  
pp. 1667-1673
Author(s):  
M.P Mafeed ◽  
Ali M Salman ◽  
C Prabin ◽  
M.K. Ramis ◽  
M.A. Ali Baig ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Heat Transfer Analysis ◽  
Length Variation ◽  
Optimum Length ◽  
Convective Boundary ◽  
Wide Range ◽  
Pin Fins

The present work deals with the heat transfer analysis of pin fins of various geometries namely ─ circular, triangular and rectangular and thus arrive at the optimum design. To this end, the fin with convective boundary tip is considered and the equation governing the one-dimensional heat conduction in the fin is solved analytically to obtain the temperature distribution and heat transfer rate. A computer code has been developed to generate results for wide range of parameters─ heat transfer coefficient h, thermal conductivity k, and length of the fin l. Results are plotted in the form of temperature variation, heat transfer variation and optimum length variation. From the detailed discussion of the results it can be concluded optimum length decreases with increasing heat transfer coefficient and it increases with increasing thermal conductivity. It can be also concluded that the optimum length is minimum for a triangular fin compared to rectangular and circular fins.

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.

Heat Transfer Characteristics of a Flow Passage With Long Pin Fins and Improving Heat Transfer Coefficient by Adding Turbulence Promoters on a Endwall

2001 ◽  
Author(s):  
K. Takeishi ◽  
T. Nakae ◽  
K. Watanabe ◽  
M. Hirayama
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Aspect Ratio ◽  
Transfer Coefficient ◽  
High Heat ◽  
Atmospheric Conditions ◽  
Pin Fin ◽  
High Heat Transfer ◽  
Turbine Vanes ◽  
Pin Fins

Pin fins are normally used for cooling the trailing edge region of a turbine, where their aspect ratio (height H/diameter D) is characteristically low. In small turbine vanes and blades, however, pin fins may also be located in the middle region of the airfoil. In this case, the aspect ratio can be quite large, usually obtaining values greater than 4. Heat transfer tests, which are conducted under atmospheric conditions for the cooling design of turbine vanes and blades, may overestimate the heat transfer coefficient of the pin-finned flow channel for such long pin fins. The fin efficiency of a long pin fin is almost unity in a low heat transfer situation as it would be encountered under atmospheric conditions, but can be considerably lower under high heat transfer conditions and for pin fins made of low thermal conductivity material. A series of tests with corresponding heat transfer models has been conducted in order to clarify the heat transfer characteristics of the long pin-finned flow channel. It is assumed that heat transfer coefficients can be predicted by the linear combination of two heat transfer equations, which were separately developed for the pin fin surface and for tubes in crossflow. To confirm the suggested combined equations, experiments have been carried out, in which the aspect ratio and the thermal conductivity of the pin were the test parameters. To maintain a high heat transfer coefficient for a long pin fin under high-pressure conditions, the heat transfer was augmented by adding a turbulence promoter on the pin-finned endwall surface. A corresponding equation that describes this situation has been developed. The predicted and measured values showed good agreement. In this paper, a comprehensive study on the heat transfer of a long pin-fin array will be presented.

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.

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