Experimental Studies of Thermal Conductivity of Three Biodiesel Compounds: Methyl Pentanoate, Methyl Octanoate, and Methyl Decanoate

2021 ◽  
Author(s):  
Xiong Zheng ◽  
Dan Qu ◽  
Yanqiong Bao ◽  
Guangzhao Qin ◽  
Yu Liu ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Experimental Studies ◽  
Methyl Octanoate ◽  
Methyl Decanoate

Possible Mechanisms for Thermal Conductivity Enhancement in Nanofluids

2006 ◽  
Author(s):  
Amit Gupta ◽  
Xuan Wu ◽  
Ranganathan Kumar
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Brownian Motion ◽  
Brownian Dynamics ◽  
Lattice Vibration ◽  
Experimental Studies ◽  
High Heat ◽  
Conductivity Enhancement ◽  
High Heat Transfer ◽  
Dynamics Simulations

This study discusses the merits of various physical mechanisms that are responsible for enhancing the heat transfer in nanofluids. Experimental studies have cemented the claim that ‘seeding’ liquids with nanoparticles can increase the thermal conductivity of the nanofluid by up to 40% for metallic and oxide nanoparticles dispersed in a base liquid. Experiments have also shown that the rise in conductivity of the nanofluid is highly dependent on the size and concentration of the nanoparticles. On the theoretical side, traditional models like Maxwell or Hamilton-Crosser models cannot explain this unusually high heat transfer. Several mechanisms have been postulated in the literature such as Brownian motion, thermal diffusion in nanoparticles and thermal interaction of nanoparticles with the surrounding fluid, the formation of an ordered liquid layer on the surface of the nanoparticle and microconvection. This study concentrates on 3 possible mechanisms: Brownian dynamics, microconvection and lattice vibration of nanoparticles in the fluid. By considering two nanofluids, copper particles dispersed in ethylene glycol, and silica in water, it is determined that translational Brownian motion of the nanoparticles, presence of an interparticle potential and the microconvection heat transfer are mechanisms that play only a smaller role in the enhancement of thermal conductivity. On the other hand, the lattice vibrations, determined by molecular dynamics simulations show a great deal of promise in increasing the thermal conductivity by as much as 23%. In a simplistic sense, the lattice vibration can be regarded as a means to simulate the phononic transport from solid to liquid at the interface.

Quantitative Experimental Study of SiO2-Water Nanofluids on Backward-Facing Step Flow under Low Reynolds Number

2018 ◽  
Vol 916 ◽  
pp. 221-225
Author(s):  
Ji Zu Lv ◽  
Liang Yu Li ◽  
Cheng Zhi Hu ◽  
Min Li Bai ◽  
Sheng Nan Chang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Reynolds Number ◽  
Volume Fraction ◽  
Pure Water ◽  
Experimental Studies ◽  
Flow Characteristics ◽  
Thermal Engineering ◽  
Heat Transfer Medium ◽  
Step Flow

Nanofluids is an innovative study of nanotechnology applied to the traditional field of thermal engineering. It refers to the metal or non-metallic nanopowder was dispersed into water, alcohol, oil and other traditional heat transfer medium, to prepared as a new heat transfer medium with high thermal conductivity. The role of nanofluids in strengthening heat transfer has been confirmed by a large number of experimental studies. Its heat transfer mechanism is mainly divided into two aspects. On the one hand, the addition of nanoparticles enhances the thermal conductivity. On the other hand, due to the interaction between the nanoparticles and base fluid causing the changes in the flow characteristics, which is also the main factor affecting the heat transfer of nanofluids. Therefore, a intensive study on the flow characteristics of nanofluids will make the study of heat transfer more meaningful. In this experiment, the flow characteristics of SiO2-water nanofluids in two-dimensional backward step flow are quantitatively studied by PIV. The results show that under the same Reynolds number, the turbulence of nanofluids is larger than that of pure water. With the increase of nanofluids volume fraction, the flow characteristics are constantly changing. The quantitative analysis proved that the nanofluids disturbance was enhanced compared with the base liquid, which resulting in the heat transfer enhancement.

Use of Fuel Assembly/Backfill Gas Effective Thermal Conductivity Models to Predict Basket and Fuel Cladding Temperatures Within a Rail Package During Normal Transport

2006 ◽  
Author(s):  
Miles Greiner ◽  
Kishore Kumar Gangadharan ◽  
Mithun Gudipati
Keyword(s):  
Thermal Conductivity ◽  
Fuel Assembly ◽  
Effective Thermal Conductivity ◽  
Wall Temperature ◽  
Experimental Studies ◽  
Temperature Profiles ◽  
Fuel Cladding ◽  
Dimensional Finite Element ◽  
Normal Transport ◽  
Conductivity Models

Two-dimensional finite element thermal simulations of a generic rail package designed to transport twenty-one spent PWR assemblies were performed for normal transport conditions. Effective thermal conductivity models were employed within the fuel assembly/backfill gas region. Those conductivity models were developed by other investigators assuming the basket wall temperature is uniform. They are typically used to predict the maximum fuel cladding temperature near the package center. The cladding temperature must not exceed specified limits during normal transport. This condition limits the number and heat generation rate of fuel assembles that can transported. The current work shows the support basket wall temperatures in the periphery of the package are highly non-uniform. Moreover the thermal resistance of those regions significantly affects the maximum fuel clad temperature near the package center. This brings the validity of the fuel/backfill gas thermal conductivity models into question. The non-uniform basket wall temperature profiles quantified in this work will be used in future numerical and experimental studies to develop new thermal models of the fuel assembly/backfill gas regions. This will be an iterative process, since the assembly/backfill model affects the predicted basket wall temperature profiles.

scholarly journals Analytical and numerical assessment of the effect of highly conductive inclusions distribution on the thermal conductivity of particulate composites

2019 ◽  
Vol 53 (25) ◽  
pp. 3499-3514 ◽  
Author(s):  
Kamran A Khan ◽  
Falah Al Hajeri ◽  
Muhammad A Khan
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Numerical Models ◽  
Random Network ◽  
Experimental Studies ◽  
Percolation Model ◽  
Spherical Particles ◽  
Particulate Composites ◽  
Advanced Composites ◽  
Micromechanical Models

Highly conductive composites have found applications in thermal management, and the effective thermal conductivity plays a vital role in understanding the thermo-mechanical behavior of advanced composites. Experimental studies show that when highly conductive inclusions embedded in a polymeric matrix the particle forms conductive chain that drastically increase the effective thermal conductivity of two-phase particulate composites. In this study, we introduce a random network three dimensional (3D) percolation model which closely represent the experimentally observed scenario of the formation of the conductive chain by spherical particles. The prediction of the effective thermal conductivity obtained from percolation models is compared with the conventional micromechanical models of particulate composites having the cubical arrangement, the hexagonal arrangement and the random distribution of the spheres. In addition to that, the capabilities of predicting the effective thermal conductivity of a composite by different analytical models, micromechanical models, and, numerical models are also discussed and compared with the experimental data available in the literature. The results showed that random network percolation models give reasonable estimates of the effective thermal conductivity of the highly conductive particulate composites only in some cases. It is found that the developed percolation models perfectly represent the case of conduction through a composite containing randomly suspended interacting spheres and yield effective thermal conductivity results close to Jeffery's model. It is concluded that a more refined random network percolation model with the directional conductive chain of spheres should be developed to predict the effective thermal conductivity of advanced composites containing highly conductive inclusions.

Sign in / Sign up

Export Citation Format

Share Document