THERMAL CONDUCTIVITY AND HEAT TRANSFER IN A SEMICONDUCTOR NANOTUBE

2008 ◽  
Author(s):  
Leila Choobineh ◽  
Afrasiab Raisi ◽  
Mahmood Yaghoubi
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity

ALCOA QC-10

Alloy Digest ◽  
2009 ◽  
Vol 58 (7) ◽  
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Aluminum Alloys ◽  
Physical Properties ◽  
Tensile Properties ◽  
Mold Material ◽  
Cast Products

Abstract Aluminum has long been accepted as a mold material. This alloy has a combination of faster machining, highest heat transfer, lighter weight, higher strength in thick sections, and greater thermal conductivity than other aluminum alloys. This datasheet provides information on physical properties, hardness, elasticity, and tensile properties. It also includes information on forming and machining. Filing Code: AL-423. Producer or source: Alcoa Forged and Cast Products.

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.

Maximizing Heat Transfer Through Joint Fin Systems

2005 ◽  
Vol 128 (2) ◽  
pp. 203-206 ◽  
Author(s):  
A.-R. A. Khaled
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Thermal Performance ◽  
Critical Length ◽  
The Other ◽  
Rigid Fixation ◽  
Convection Coefficient ◽  
Heat Transfer Characteristics ◽  
Cold Side ◽  
Transfer Characteristics

Heat transfer through joint fins is modeled and analyzed analytically in this work. The terminology “joint fin systems” is used to refer to extending surfaces that are exposed to two different convective media from its both ends. It is found that heat transfer through joint fins is maximized at certain critical lengths of each portion (the receiver fin portion which faces the hot side and the sender fin portion that faces the cold side of the convective media). The critical length of each portion of joint fins is increased as the convection coefficient of the other fin portion increases. At a certain value of the thermal conductivity of the sender fin portion, the critical length for the receiver fin portion may be reduced while heat transfer is maximized. This value depends on the convection coefficient for both fin portions. Thermal performance of joint fins is increased as both thermal conductivity of the sender fin portion or its convection coefficient increases. This work shows that the design of machine components such as bolts, screws, and others can be improved to achieve favorable heat transfer characteristics in addition to its main functions such as rigid fixation properties.

scholarly journals Effects of the Wall Properties on the Cooling Efficiency in a Thermosyphon Containing PCM Suspensions

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 572
Author(s):  
Ching-Jenq Ho ◽  
Shih-Ming Lin ◽  
Chi-Ming Lai
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Wall Thickness ◽  
Thermal Conductivity Ratio ◽  
Heat Transfer Efficiency ◽  
Wall Properties ◽  
Heating Section ◽  
Cooling Section ◽  
Optimum Heat ◽  
Parameter Ranges

This study explores the effects of pipe wall properties (thermal conductivity k and wall thickness tw) on the heat transfer performance of a rectangular thermosyphon with a phase change material (PCM) suspension and a geometric configuration (aspect ratio = 1; dimensionless heating section length = 0.8; dimensionless relative elevation between the cooling and the heating sections = 2) that ensures the optimum heat transfer efficiency in the cooling section. The following parameter ranges are studied: the dimensionless loop wall thickness (0 to 0.5), wall-to-fluid thermal conductivity ratio (0.1 to 100), modified Rayleigh number (1010 to 1011), and volumetric fraction of PCM particles (0 to 10%). The results show that appropriate selection of k and tw can lead to improved heat transfer effectiveness in the cooling section of the PCM suspension-containing rectangular thermosyphon.

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