Experimental Study on the Thermal Physical Properties of a CNTS-Ammonia Binary Nanofluid

2008 ◽  
Author(s):  
Xuehu Ma ◽  
Fengmin Su ◽  
Zhong Lan ◽  
Jiabin Chen
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Experimental Study ◽  
Surface Tension ◽  
Physical Properties ◽  
Mass Fraction ◽  
Experimental Values ◽  
Thermal Physical Properties ◽  
Thermal Conductivities

In this paper, carbon nanotubes—ammonia nanofluids (the binary nanofluids) have been prepared by two steps method. And the thermal conductivity, surface tension and kinetic viscosity of the binary nanofluid have been measured. The effects of the mass fraction of carbon nanotubes, the concentration of ammonia and temperature on the thermal physical properties of the binary nanofluid have been systematically studied. On the base, the effective thermal conductivities of the binary nanofluids have been calculated using the models in the literatures, and have been compared with the experimental values.

Experimental Study on Thermophysical Properties of Nanotubes and Nanofluids

2007 ◽  
Author(s):  
Xing Zhang ◽  
Motoo Fujii
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Experimental Study ◽  
Thermophysical Properties ◽  
Experimental Studies ◽  
Spherical Nanoparticles ◽  
Hot Wire ◽  
Transient Short Hot Wire ◽  
Different Diameters ◽  
Thermal Conductivities

This paper reviews the studies of the thermophysical properties of nanotubes and nanofluids and reports the experimental studies on the thermal conductivity of individual carbon nanotubes and nanofluids containing spherical and cylindrical nanoparticles. The thermal conductivity of a single carbon nanotube has been measured by a suspended sample-attached T-type nanosensor. The size effect of the different diameters on the thermal conductivity has been observed experimentally. The effective thermal conductivity and thermal diffusivity of Au/toluene, Al2O3/water, TiO2/water, CuO/water and carbon nanofibers (CNFs)/water nanofluids have been measured by using the transient short-hot-wire technique. The measured results demonstrate that the effective thermal conductivities of CNFs/water nanofluids are much greater than those of nanofluids containing spherical nanoparticles. However, the effective thermal conductivities do not show any anomalous enhancements and can be accurately predicted by the existing formulas.

A closed-form model for estimating the effective thermal conductivities of carbon nanotube–polymer nanocomposites

2018 ◽  
Vol 233 (8) ◽  
pp. 2909-2919 ◽  
Author(s):  
Reza Moheimani ◽  
M Hasansade
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Closed Form ◽  
Polymer Nanocomposites ◽  
Micromechanical Model ◽  
Polymer Nanocomposite ◽  
Interfacial Thermal Resistance ◽  
Full Potential ◽  
Thermal Conductivities

This paper describes a closed-form unit cell micromechanical model for estimating the effective thermal conductivities of unidirectional carbon nanotube reinforced polymer nanocomposites. The model incorporates the typically observed misalignment and curvature of carbon nanotubes into the polymer nanocomposites. Also, the interfacial thermal resistance between the carbon nanotube and the polymer matrix is considered in the nanocomposite simulation. The micromechanics model is seen to produce reasonable agreement with available experimental data for the effective thermal conductivities of polymer nanocomposites reinforced with different carbon nanotube volume fractions. The results indicate that the thermal conductivities are strongly dependent on the waviness wherein, even a slight change in the carbon nanotube curvature can induce a prominent change in the polymer nanocomposite thermal conducting behavior. In general, the carbon nanotube curvature improves the nanocomposite thermal conductivity in the transverse direction. However, using the straight carbon nanotubes leads to maximum levels of axial thermal conductivities. With the increase in carbon nanotube diameter, an enhancement in nanocomposite transverse thermal conductivity is observed. Also, the results of micromechanical simulation show that it is necessary to form a perfectly bonded interface if the full potential of carbon nanotube reinforcement is to be realized.

Thermal properties of pillared-graphene nanostructures

2015 ◽  
Vol 1727 ◽  
Author(s):  
M. Rifu ◽  
K. Shintani
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Molecular Dynamics ◽  
Thermal Properties ◽  
Molecular Dynamics Simulation ◽  
Dynamics Simulation ◽  
Nonequilibrium Molecular Dynamics ◽  
Nonequilibrium Molecular Dynamics Simulation ◽  
Thermal Conductivities ◽  
Graphene Nanostructures

ABSTRACTThe thermal conductivities of pillared-graphene nanostructures (PGNSs) are obtained using nonequilibrium molecular-dynamics simulation. It is revealed their thermal conductivities are much smaller than the thermal conductivities of carbon nanotubes (CNTs). This fact is explained by examining the density of states (DOS) of the local phonons of PGNSs. It is also found the thermal conductivity of a PGNS linearly decreases with the increase of the inter-pillar distance.

An experimental study on the effect of ultrasonication on thermal conductivity of ferrofluid loaded with carbon nanotubes

2015 ◽  
Vol 617 ◽  
pp. 102-110 ◽  
Author(s):  
A. Shahsavar ◽  
M.R. Salimpour ◽  
M. Saghafian ◽  
M.B. Shafii
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Experimental Study

Experimental Study on Thermal Conductivity Property of Rubber Composites Filled with Carbon Nanotubes

2011 ◽  
Vol 221 ◽  
pp. 373-376 ◽  
Author(s):  
Ze Peng Wang ◽  
Yan He
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Experimental Study ◽  
Carbon Black ◽  
Natural Rubber ◽  
Rubber Composites ◽  
Conductivity Property

Thermal conductivity of rubber composites filled with CNTs (carbon nanotubes) and N234 CB (carbon black) were investigated. Result indicated that Thermal conductivity of NR (natural rubber) filled with CNTs is higher than that of NR filled with CB in the case of the same filling amount. CNTs can better improve the performance of thermal conduct of rubber composites than CB. The more the filling content of CNTs is, the higher thermal conductivity of NR composites.

scholarly journals Исследование коэффициентов вязкости и теплопроводности суспензий с одностенными углеродными нанотрубками

2020 ◽  
Vol 46 (3) ◽  
pp. 27
Author(s):  
А.В. Минаков ◽  
М.И. Пряжников ◽  
Д.В. Гузей ◽  
Д.В. Платонов
Keyword(s):  
Thermal Conductivity ◽  
Carbon Nanotubes ◽  
Experimental Study ◽  
Single Walled Carbon Nanotubes ◽  
Rheological Parameters ◽  
Single Walled Carbon ◽  
Walled Carbon Nanotubes ◽  
Base Liquid ◽  
Newtonian Behavior ◽  
Mass Concentrations

The results of an experimental study of the viscosity and thermal conductivity coefficients of suspensions with single-walled carbon nanotubes are presented. The range of nanotube mass concentrations ranged from 0.05 to 0.25 wt.%. The studied suspensions showed non-Newtonian behavior. Dependences of rheological parameters of suspensions on nanotube concentration were obtained. The influence of the base liquid on the viscosity and thermal conductivity of suspensions was established.

scholarly journals Prediction of the thermal conductivities of wood based on an intelligent algorithm

BioResources ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 1161-1185
Author(s):  
Shiyu Zhou ◽  
Xiaoxia Yang ◽  
Yandong Zhang ◽  
Xiaoping Liu ◽  
Yucheng Zhou
Keyword(s):  
Thermal Conductivity ◽  
Conventional Heating ◽  
Theoretical Formula ◽  
Support Vector ◽  
Intelligent Algorithm ◽  
Heating Method ◽  
Intelligent Algorithms ◽  
Thermal Physical Properties ◽  
Floor Heating ◽  
Thermal Conductivities

For thermal comfort and energy-saving performance, a floor-heating method is superior to conventional heating modes, e.g., radiator, fan coil, etc. The floor-heating method has been developed to be a primary indoor heating form. Wood is the most common floor surface material. Due to the anisotropy of wood, it is difficult to obtain a general theoretical formula for its thermal physical properties. In this paper, intelligent algorithms were adopted to predict thermal conductivities of wood. First, the study elaborated frequently used testing methods of thermal conductivity. Next, 130 types of common wood species were measured to form a database of thermal properties. With this database, intelligent algorithms were used to make predictions. For the thermal conductivity predictions that were conducted with support vector machine, the degree of fit between the predicted results and the measured results was not less than 0.87 (k-fold validation). This study validated the feasibility of the usage of the intelligent algorithm for the research and prediction of the thermal conductivities of wood.

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