Significance of Thermal Conductivity of Molten Polyethylene in Cable Extrusion Process

Volume 3 ◽  
2004 ◽  
Author(s):  
Birgitta Ka¨llstrand ◽  
Carl-Olof Olsson
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
High Voltage ◽  
Outer Layer ◽  
Extrusion Process ◽  
Temperature Profiles ◽  
Line Speed ◽  
Conductor Temperature ◽  
Good Agreement

The dominating parameter for heat transfer during continuous curing of extruded high voltage cables is the thermal conductivity of molten polyethylene. Literature on thermal conductivity has been reviewed, and it is found that there are differences of order 50% between different investigations. From numerical simulations it is found that 20% increase in thermal conductivity corresponds to 8 °C increase in maximum conductor temperature for constant line speed or 16% increase in line speed for optimized crosslinking. The calculated conductor temperature profile is compared with experimental data from the cable manufacturing. The conductor temperature was measured continuously, using an optical fiber embedded in the outer layer of the conductor, while the conductor passed through the extrusion line. The comparison between measured and simulated conductor temperature profiles show good agreement provided that an appropriate value of the thermal conductivity is chosen.

scholarly journals Thermal Conductivities of Unidirectional Materials

1967 ◽  
Vol 1 (2) ◽  
pp. 166-173 ◽  
Author(s):  
George S. Springer ◽  
Stephen W. Tsai
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Thermal Model ◽  
Longitudinal Shear ◽  
Unidirectional Composite ◽  
Shear Loading ◽  
Square Packing ◽  
Good Agreement ◽  
Thermal Conductivities

In this paper the composite thermal conductivities of unidirec tional composites are studied and expressions are obtained for pre dicting these conductivities in the directions along and normal to the filaments. In the direction along the filament an expression is presented based on the assumption that the filaments and matrix are connected in parallel. In the direction normal to the filaments composite thermal conductivity values are obtained first by utiliz ing the analogy between the response of a unidirectional composite to longitudinal shear loading and to transverse heat transfer; second by replacing the filament-matrix composite with an idealized ther mal model. The results of the shear loading analogy agree reason ably well with the results of the thermal model particularly at filament contents below about 60%. These results were also com pared to experimental data reported in the literature and good agreement was found between the data and those theoretical re sults that were derived for circular filaments arranged in a square packing array.

The Analysis of Non-Fourier Heat Transfer Mechanism on Thermal Conductivity for Nanofluids

2007 ◽  
Author(s):  
Jurij Avsec ◽  
Maks Oblak
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Mathematical Model ◽  
Analytical Calculation ◽  
Heat Transfer Mechanism ◽  
Influence Of Temperature ◽  
The Mathematical Model ◽  
Brown Motion ◽  
Good Agreement

The paper features the mathematical model representing the analytical calculation of thermal conductivity for nanofluids. The mathematical model was developed on the basis of statistical nano-mechanics. We have made the detailed analysis of the influence of temperature dependence on thermal conductivity for nanofluids. On this basis are taken into account the influences such as formation of nanolayer around nanoparticles, the Brown motion of solid nanoparticles and influence of diffusive-ballistic heat transport. The analytical results obtained by statistical mechanics are compared with the experimental data and they show relatively good agreement.

scholarly journals Relationship between the Transport Coefficients of Polar Substances and Entropy

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 13
Author(s):  
Ivan Anashkin ◽  
Sergey Dyakonov ◽  
German Dyakonov
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Diffusion Coefficient ◽  
High Pressures ◽  
Thermal Conductivity Coefficient ◽  
Good Description ◽  
Transport Coefficients ◽  
Viscosity Coefficient ◽  
Wide Region ◽  
Good Agreement

An expression is proposed that relates the transport properties of polar substances (diffusion coefficient, viscosity coefficient, and thermal conductivity coefficient) with entropy. To calculate the entropy, an equation of state with a good description of the properties in a wide region of the state is used. Comparison of calculations based on the proposed expressions with experimental data showed good agreement. A deviation exceeding 20% is observed only in the region near the critical point as well as at high pressures.

Forced Convection Heat Transfer of Nanofluids: A Review

2017 ◽  
Author(s):  
Aditya Kuchibhotla ◽  
Debjyoti Banerjee
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Heat Capacity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Specific Heat ◽  
Forced Convection ◽  
Convection Heat Transfer ◽  
Convection Heat

Stable homogeneous colloidal suspensions of nanoparticles in a liquid solvents are termed as nanofluids. In this review the results for the forced convection heat transfer of nanofluids are gleaned from the literature reports. This study attempts to evaluate the experimental data in the literature for the efficacy of employing nanofluids as heat transfer fluids (HTF) and for Thermal Energy Storage (TES). The efficacy of nanofluids for improving the performance of compact heat exchangers were also explored. In addition to thermal conductivity and specific heat capacity the rheological behavior of nanofluids also play a significant role for various applications. The material properties of nanofluids are highly sensitive to small variations in synthesis protocols. Hence the scope of this review encompassed various sub-topics including: synthesis protocols for nanofluids, materials characterization, thermo-physical properties (thermal conductivity, viscosity, specific heat capacity), pressure drop and heat transfer coefficients under forced convection conditions. The measured values of heat transfer coefficient of the nanofluids varies with testing configuration i.e. flow regime, boundary condition and geometry. Furthermore, a review of the reported results on the effects of particle concentration, size, temperature is presented in this study. A brief discussion on the pros and cons of various models in the literature is also performed — especially pertaining to the reports on the anomalous enhancement in heat transfer coefficient of nanofluids. Furthermore, the experimental data in the literature indicate that the enhancement observed in heat transfer coefficient is incongruous compared to the level of thermal conductivity enhancement obtained in these studies. Plausible explanations for this incongruous behavior is explored in this review. A brief discussion on the applicability of conventional single phase convection correlations based on Newtonian rheological models for predicting the heat transfer characteristics of the nanofluids is also explored in this review (especially considering that nanofluids often display non-Newtonian rheology). Validity of various correlations reported in the literature that were developed from experiments, is also explored in this review. These comparisons were performed as a function of various parameters, such as, for the same mass flow rate, Reynolds number, mass averaged velocity and pumping power.

Post-Test Investigations of BFBT Critical Power Tests With Trace

2009 ◽  
Author(s):  
Marc Thieme ◽  
Wolfgang Tietsch ◽  
Rafael Macian ◽  
Victor Hugo Sanchez Espinoza
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Critical Power ◽  
The Core ◽  
Fine Mesh ◽  
The Us ◽  
Post Test ◽  
Trace Model ◽  
Good Agreement ◽  
Transfer Models

The validation of heat transfer models of safety analysis codes such as TRACE is very important due to the strong interaction of the thermal hydraulics parameters with the core neutronics. TRACE is the reference system code of the US NRC for LWR. It is being developed and extensively validated within the international CAMP-program. In this paper, the validation of heat transfer models of TRACE related to the prediction of the critical power is presented. The validation is based on a large number of critical power tests performed in the NUPEC BFBT (BWR Full-Size Fine-Mesh Bundle Tests) facility in Japan. These tests were analysed with the TRACE Version 5 RC 2. The comparison of predictions with the experimental data shows good agreement. The developed TRACE model and the comparison of experimental data with code results will be presented and discussed.

Experimental and Numerical Studies of Heat Transfer in Human Uteri During Cryoablation

2002 ◽  
Author(s):  
Jim S. Chen ◽  
Kevin Agnissey ◽  
Marla Wolfson ◽  
Charles Philips ◽  
Thomas Shaffer
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Temperature History ◽  
Rubber Sheet ◽  
Numerical Studies ◽  
Numerical Predictions ◽  
Silicone Rubber Sheet ◽  
Nicolson Scheme ◽  
Sheet Good ◽  
Good Agreement

This paper presents experimental and numerical studies of transient heat transfer inside the uterus during application of a PFC (perfluorochemical) fluid into the endometrium cavity in order to achieve cryoablation. The numerical prediction is based on a 1-D finite difference method of the bio-heat equation using the Crank Nicolson scheme. The numerical method is first validated by a 1-D physical model by measuring temperature history at several locations within a silicone rubber sheet. Good agreement, thus positive predictability, was obtained by comparing numerical predictions with the experimental data obtained from eight intact, hysterectomized uteri during cryoablation.

scholarly journals Modeling heat transfer process in soils

2018 ◽  
Vol 251 ◽  
pp. 02048 ◽  
Author(s):  
Ian Ofrikhter ◽  
Alexander Zaharov ◽  
Andrey Ponomaryov ◽  
Natalia Likhacheva
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Analytical Calculation ◽  
Accurate Method ◽  
Heat Transfer Process ◽  
Preliminary Evaluation ◽  
Soil Base ◽  
Undisturbed Sample ◽  
Analytical Formulas

In this paper, a new model is presented for calculating the thermal conductivity of soils, and the main provisions for the derivation of analytical formulas are given. The presented model allows taking into account the density, moisture content and temperature of the soil base. The technique presented in the paper makes it possible to dispense with laborious experiments to estimate the thermal conductivity of the soil. The method of analytical calculation is step by step presented in the paper. Two variants of using the method are proposed: 1) Less accurate method, for preliminary evaluation, without the need to take probe and conduct experiments. 2) More accurate method, with at least one experiment with a disturbed or undisturbed sample. The results of comparison of calculated values of thermal conductivity and experimental data are presented.

Convective Heat Transfer Enhancement With Nanofluids: The Effect of Temperature-Variable Thermal Conductivity

2010 ◽  
Author(s):  
Sezer O¨zerinc¸ ◽  
Almıla G. Yazıcıog˘lu ◽  
Sadık Kakac¸
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermal Conductivity ◽  
Heat Transfer Enhancement ◽  
Forced Convection ◽  
Convection Heat Transfer ◽  
Thermal Dispersion ◽  
Convection Heat ◽  
Transfer Enhancement ◽  
Experimental Values

A nanofluid is defined as the suspension of nanoparticles in a base liquid. Studies in the last decade have shown that significant amount of thermal conductivity and heat transfer enhancement can be obtained by using nanofluids. In the first part of this study, classical forced convection heat transfer correlations developed for pure fluids are used to predict the experimental values of heat transfer enhancement of nanofluids. It is seen that the experimental values of heat transfer enhancement exceed the enhancement predictions of the classical correlations. On the other hand, a recent correlation based on the thermal dispersion phenomenon created by the random motion of nanoparticles predicts the experimental data well. In the second part of the study, in order to further examine the validity of the thermal dispersion approach, a numerical analysis of forced convection heat transfer of Al2O3/water nanofluid inside a circular tube in the laminar flow regime is performed by utilizing single phase assumption. A thermal dispersion model is applied to the problem and variation of thermal conductivity with temperature and variation of thermal dispersion with local axial velocity are taken into account. The agreement of the numerical results with experimental data might be considered as an indication of the validity of the approach.

Dual Role of Nanoparticles in the Thermal Conductivity Enhancement of Nanoparticle Suspensions

2005 ◽  
Author(s):  
Calvin H. Li ◽  
G. P. Peterson
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Brownian Motion ◽  
Theoretical Models ◽  
Conductivity Enhancement ◽  
Nanoparticle Suspensions ◽  
The Difference ◽  
Physical Phenomena ◽  
Good Agreement

Experimental evidence exists that the addition of a small quantity of nanoparticles to a base fluid, can have a significant impact on the effective thermal conductivity of the resulting suspension. The causes for this are currently thought to be due to a combination of two distinct mechanisms. The first is due to the change in the thermophysical properties of the suspension, resulting from the difference in the thermal conductivity of the fluid and the particles, and the second is thought to be due to the transport of thermal energy by the particles, due to the Brownian motion of the particles. In order to better understand these phenomena, a theoretical model has been developed that examines the effect of the Brownian motion. In this model, the well-known approach first presented by Maxwell, is combined with a new expression that incorporates the effect of the Brownian motion and describes the physical phenomena that occurs because of it. The results indicate that the enhanced thermal conductivity may not in fact be due to the transport of energy by the particles, but rather, due to the stirring motion caused by the movement of the nanoparticles which enhances the heat transfer within the fluid. The resulting model shows good agreement when compared with the existing experimental data and perhaps more importantly helps to explain the trends observed from a fundamental physical perspective. In addition, it provides a possible explanation for the differences that have been observed between the previously obtained experimental data, the predictions obtained from Maxwell’s equation and the theoretical models developed by other investigators.

Coupled CFD-ANN Procedure for Extending Heat Transfer Correlations Out of Their Range of Validity

2012 ◽  
Author(s):  
Andrea Viano ◽  
Gabriele Ottino ◽  
Luca Ratto ◽  
Giuseppe Spataro
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Nusselt Number ◽  
Gas Turbine ◽  
Training Session ◽  
Nonlinear Problems ◽  
Artificial Neural ◽  
Cfd Analyses ◽  
Good Agreement

The heat transfer coefficient and pressure losses are among the main parameters to be evaluated in gas turbine cooling network design. Due to the complexity of these estimates, correlation-based computations are typically used as a result of time-consuming and expensive experimental activities. One of the main problems that the industry has to face is that these correlations, based on non-dimensional experimental data, produce reliable results in a range of validity typically different from that encountered in gas turbine applications. This paper will present preliminary results of an innovative procedure based on CFD analyses and Artificial Neural Networks, able to extend correlation predictions out of their range of validity, without any additional experimental data. Well-known test cases were replicated by building corresponding CAD geometries which were discretized by means of appropriate meshes, resulting from grid-independence studies. CFD analyses, based on the RANS approach, were performed to overlay the computations of the Nusselt number obtained from experimental activities. A preliminary comparison among turbulence models was carried out to find one leading to a good agreement with the experimental data. Then, an optimization method, based on Evolutionary Algorithms, was applied to the CFD analyses in order to find the best set of constant values for the chosen turbulence model, leading to the most accurate prediction of the experimental dataset. The resulting ad hoc CFD model was adopted in order to analyse test case configurations characterized by parameters within and external to the correlation validity field, building a sufficiently wide feeding database. A feed-forward multi-layer neural network was selected among network architectures typically used in engineering applications for prediction analyses. ANNs were chosen because they enable the solution of these complex nonlinear problems by using simple computational operations. The selected Artificial Neural Network was trained by a back-propagation procedure on the CFD results regarding Nusselt number. The validation of the resulting ANN was performed comparing its outputs with experimental data external to the correlation range of validity, which had not been used in the training session. Good agreement has been found. Results are presented and discussed.

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