Modeling thermal conductivity, specific heat, and density of milk: A neural network approach

The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.

Changes of density, thermal conductivity, thermal diffusivity, and specific heat of plums during drying

The thermal properties of plums (Prunus domestica) and prunes were investigated in the moisture content of 14.2-80.4% (wet basis) near room temperature (approximately 28 degrees C). The apparent density of the fruits increased from 1042.9 to 1460.0 kg/m(3), and the bulk density increased from 706.6 to 897.5 kg/m(3) as the plums were dried, following classical empirical models as a function of moisture content. It was found that specific heat, effective thermal diffusivity, and effective thermal conductivity of the prunes increased with the moisture content of the samples, which can be represented by using different empirical models.

Thermal conductivity features of ZnO-based varistors using the laser-pulse method

The thermal conductivity of several commercial ZnO-based varistor systems was determined based on the laser-pulse method, a technique that proved extremely useful and easy to apply. Using this technique, the thermal conductivity was found to be dependent on the microstructural features of the devices, involving the mean grain size and phase composition. Among the phases existing in commercial ZnO-based varistors, ZniSb2O12 and Bi2O3 were found to contribute strongly to the thermal conductivity of the devices. (C) 2003 Elsevier B.V. All rights reserved.

Density, heat capacity and thermal conductivity of liquid egg products

Density, heat capacity and thermal conductivity of liquid egg products, such as egg white, egg yolk, whole egg and various white and yolk blends, were determined as affected by temperature and water content ranging from 273 to 311 K and 51.8 to 88.2% (mass), respectively. Polynomial models fitted the experimental data very well, showing a linear relationship both for temperature and water content. (c) 2005 Published by Elsevier Ltd.

A comparative study of thermal conductivity in ZnO- and SnO2-based varistor systems

We performed a comparative study of electrical and thermal properties of ZnO- and SnO2-based varistor. The electrical properties of commercial ZnO-based varistor are equivalent to that found in SnO2-based varistor system. In spite of this, the SnO2 showed a thermal conductivity higher than commercial samples of ZnO-based varistor, which allied with its simpler microstructure and lower dopant concentration is a remarkable result that point out to the use of this system to compete commercially with ZnO-based varistor devices.

Freezing point and thermal conductivity of tropical fruit pulps: Mango and papaya

The freezing point depression of mango and papaya pulps was measured by using a simple apparatus, consisting of two major sections: a freezing vessel and a data acquisition system. The thermal conductivity of both pulps as a function of frozen water fraction and temperature was also investigated by using a coaxial dual-cylinder apparatus. Thermal conductivity above the initial freezing point was well fitted by polynomial equations. Below the freezing point, the thermal conductivity was strongly affected by both the frozen water fraction and temperature. Simple equations in terms of frozen water fraction and temperature could be fitted to the experimental data of freezing point depression and thermal conductivity.

Effective Thermal Conductivity Estimates Of Particulate Mixtures

Several theories have been advanced to estimate effective thermal conductivities of particulate mixtures, but most theories have focused on the dilute case. A method is proposed to estimate the effective thermal conductivity coefficient of mixtures of arbitrary concentration. Earlier the authors developed a theory to determine the expected contact area between different species. This theory is employed to determine the Kapitza resistance of the heterogeneous mixture and forms part of an overall theory to estimate the effective thermal conductivity. Results are compared with other theoretical estimates and with experiments.

In-plane thermal conductivity modeling of carbon filled liquid crystal polymer based resins

Adding conductive carbon fillers to insulating thermoplastic resins increases composite electrical and thermal conductivity. Often, as much of a single type of carbon filler is added to achieve the desired conductivity, while still allowing the material to be molded into a bipolar plate for a fuel cell. In this study, varying amounts of three different carbons (carbon black, synthetic graphite particles, and carbon fiber) were added to Vectra A950RX Liquid Crystal Polymer. The in-plane thermal conductivity of the resulting single filler composites were tested. The results showed that adding synthetic graphite particles caused the largest increase in the in-plane thermal conductivity of the composite. The composites were modeled using ellipsoidal inclusion problems to predict the effective in-plane thermal conductivities at varying volume fractions with only physical property data of constituents. The synthetic graphite and carbon black were modeled using the average field approximation with ellipsoidal inclusions and the model showed good agreement with the experimental data. The carbon fiber polymer composite was modeled using an assemblage of coated ellipsoids and the model showed good agreement with the experimental data.

Thermal conductivity at DSDP Leg 60 Holes

A total of 1547 thermal conductivity values were determined by both the NP (needle probe method) and the QTM (quick thermal conductivity meter) on 1319 samples recovered during DSDP Leg 60. The NP method is primarily for the measurement of soft sedimentary samples, and the result is free from the effect of porewater evaporation. Measurement by the QTM method is faster and is applicable to all types of samples-namely, sediments (soft, semilithified, and lithified) and basement rocks. Data from the deep holes at Sites 453, 458, and 459 show that the thermal conductivity increases with depth, the rate of increase ranging from (0.18 mcal/cm s °C)/100 m at Site 459 to (0.72 mcal/cm s °C)/100 m at Site 456. A positive correlation between the sedimentary accumulation rate and the rate of thermal conductivity increase with depth indicates that both compaction and lithification are important factors. Drilled pillow basalts show nearly uniform thermal conductivity. At She 454 the thermal conductivity of one basaltic flow unit was higher near the center of the unit and lower toward the margin, reflecting variable vesicularity. Hydrothermally altered basalts at Site 456 showed higher thermal conductivity than fresh basalt because secondary calcite, quartz, and pyrite are generally more thermally conductive than fresh basalt. The average thermal conductivity in the top 50 meters of sediments correlates inversely with water depth because of dissolution of calcite, a mineral with high thermal conductivity, from the sediments as the water depth exceeds the lysocline and the carbonate compensation depth. Differences between the Mariana Trench data and the Mariana Basin and Trough data may reflect different abundances of terrigenous material in the sediment. There are remarkable correlations between thermal conductivity and other physical properties. The relationship between thermal conductivity and compressional wave velocity can be used to infer the ocean crustal thermal conductivity from the seismic velocity structure.