The Thermophysical Data

Properties, such as density, enthalpy, entropy, etc., for given species or mixture of them, are referred to as thermophysical properties and their values, as thermophysical data. A typical example is the Steam Tables. Such data involve two types of quantities  

The first type refers to the properties that can be measured in the laboratory  

The second type involves all other properties such as, enthalpy, entropy, etc., that cannot be determined experimentally. There is no such a thing, for example, as an entropymeter.  

Their values are derived from the measurable quantities through relationships based on the laws of thermodynamics. They are, therefore, conceptual quantities and this is why they are more difficult to comprehend. We will refer to them as thermodynamic properties and their values as thermodynamic data. 

The availability of thermophysical data is essential to the application of thermodynamics in Chemical Engineering calculations. The first law, for example, provides the necessary relationship for the evaluation of the amount of heat involved in changing the temperature of a given fluid. Unless, however, the required heat capacity data is available, no numerical answer can be obtained. 

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For the thermophysical data of solid and liquid iridium, the following uncertainties should be applied temperatures below 2400 K, 4%, temperatures above 2400 K, 1.7 %, normal spectral emittance, 6%, enthalpy in tfie solid state, 4%, enthalpy in the liquid state, 2.5%, heat of fusion, 8%, isobaric heat capacity, 8%, electrical resistivity at initial geometry solid, 4%, liquid, 3.5%, electrical resistivity including volume expansion solid and liquid, 6%, thermal conductivity, 12%, and finally thermal difliisivity, 16%. 

Such sensitivity analysis is a valuable tool for planning the experimental program for the phase equilibrium measurements, which are often needed to complement the literature data. Another field of application of such sensitivity analysis is the assessment of possible errors in process simulation results due to uncertainties of the thermophysical data. In the case studied here, the sensitivity analysis showed that the two binary systems, where data was missing (acetic add + hexyl acetate and 1-hexanol + hexyl acetate) have an important influence on the process model predictions. 

As a consequence, heat transfer to gases in high-pressure processes is enhanced when the process conditions become pseudocritical. A successful method is to calculate the improved heat transfer when designing a heat transfer apparatus for options of heating and cooling as w is the relation of the thermophysical data at wall and bulk temperature. Polyakov [36] and Krasnoshchekov [37] developed the following equations ... 

We proceed with a discussion - again brief - of the Foundations of thermodynamics the Laws and the Thermophysical data, so that we become aware of their importance from the very beginning of this endeavor. 

The DIPPR is a research organization sponsored by the AlChE (American Institute of Chemical Engineers). Its objective is to develop a thermophysical data bank for the components most frequently encountered in the chemical industry. 

Material Properties Numerical Data System Purdue University Purdue University (CINDAS) evaluated data compiled, correlated, analyzed, and synthesized to generate values for the thermophysical, mechanical, and electrical properties of materials... 

The same data on physical properties of liquid refrigerants R-N (R-11, R-12, R-13, R-21, R-22, R-113) and their vapor are presented in Tables 7.3-7.8. The detailed data on thermophysical properties of different refrigerants (density, enthalpy, heat capacity, viscosity, thermal conductivity and diffusivity) are found in books by Platzer et al. (1990), Andersen (1959), and Danilova et al. (1976). 

The geometry of the tubes allows the heat transfer being considered one dimensional, and each tube to be a lumped system in front of the ambient air. This two conditions are fulfilled when Bi < 0.1 (Biot number Bi = a /(/(2a ), where R is the radius of the sample, X its thermal conductivity and a the heat transfer coefficient between the tube and the environment). Once the temperature-time curves of the PCM and the reference substance are obtained (Figure 160), the data can be used to determine the thermophysical properties of the PCM. 

Klamt, A., Eckert, F., 2000, COSMO-RS a Novel and Efficient method for the a Priori Prediction of Thermophysical Data of Liquids. Fluid Phase Equilibria., 172, 43. 

Since detailed chemical kinetic mechanisms involve the participation of a large number of species in a large number of elementary reactions, sensitivity and reaction path analyses are also essential elements of DCKM. Sensitivity analysis provides a means to assess the limits of confidence we must put on our model predictions in view of uncertainties that exist in reaction rate parameters and thermochemical and thermophysical data utilized, as well as the initial and boundary conditions used in the modeling work. Through... 

C12 A. Klamt and F. Eckert, COSMO-RS A novel and efficient method for the a priori prediction of thermophysical data of liquids, Fluid Phase Equilib., 172 (2000) 43. 

The thermophysical properties, such as glass transition, specific heat, melting point, and the crystallization temperature of virgin polymers are by-and-large available in the literature. However, the thermal conductivity or diffusivity, especially in the molten state, is not readily available, and values reported may differ due to experimental difficulties. The density of the polymer, or more generally, the pressure-volume-temperature (PVT) diagram, is also not readily available and the data are not easily convertible to simple analytical form. Thus, simplification or approximations have to be made to obtain a solution to the problem at hand. 

Sewell and co workers [145-148] have performed molecular dynamics simulations using the HMX model developed by Smith and Bharadwaj [142] to predict thermophysical and mechanical properties of HMX for use in mesoscale simulations of HMX-containing plastic-bonded explosives. Since much of the information needed for the mesoscale models cannot readily be obtained through experimental measurement, Menikoff and Sewell [145] demonstrate how information on HMX generated through molecular dynamics simulation supplement the available experimental information to provide the necessary data for the mesoscale models. The information generated from molecular dynamics simulations of HMX using the Smith and Bharadwaj model [142] includes shear viscosity, self-diffusion [146] and thermal conductivity [147] of liquid HMX. Sewell et al. have also assessed the validity of the HMX flexible model proposed by Smith and Bharadwaj in molecular dynamics studies of HMX crystalline polymorphs. 

Empirical approaches are useful when macroscale HRR measurements are available but little or no information is available regarding the thermophysical properties, kinetic parameters, and heats of reaction that would be necessary to apply a more comprehensive pyrolysis model. Although these modeling approaches are crude in comparison with some of the more refined solid-phase treatments, one advantage is that all required input parameters can be obtained from widely used bench-scale fire tests using well-established data reduction techniques. As greater levels of complexity are added, establishing the required input parameters (or material properties ) for different materials becomes an onerous task. 

RBDOPT is coded in an object oriented environment using (C++) and uses distributed computing techniques to speed up the calculations. The thermophysical properties are estimated using Physical Property Data Service (PPDS). PPDS supports over 900 components and 36 physical property routes including NRTL, SRK, UNIFAC and UNIQUAC. The general structure of RBDOPT is shown in Figure 9.13. RBDOPT defines a batch distillation column as an object. Procedures... 

The time that simulation programs spend in the physical property routines has long been a point of con cern. Both Leesley and Heyen (3jU and Barrett and Walsh (39) suggest the use of approximations to the thermodynamic functions during the simulation to save time. Mah (40)reports on the effects that poor thermophysical data can have on a design. 

The values used in Tables II and III were taken into consideration for the standard exergies. All the other data were obtained from the Uhde thermophysical properties program package. 

This Is a liquid-phase catalytic reaction system and the reaction conditions are very close to the critical conditions of the reactants propylene and benzene. The values of the thermo-physical properties (e.g., heat of formation and heat capacity) are generally not available at the reaction conditions and are difficult to evaluate accurately. We evaluated how well the thermophysical properties were estimated by simulating a commercial cumene reactor, and comparing the adiabatic temperature rise of the simulation with that of the observed data available. 

We will review the basic quantities of thermodynamics energy, temperature, heat, work, and the ideal gas law. These quantities will be used to explain the principles of thermophysics and thermochemistry, which will be applied to the specific reactions of combustion and detonation. Using the thermochemical data of heats of detonation or explosion, we will see how to calculate adiabatic reaction temperatures. These data in turn will be used to analyze or predict pressures of explosions in closed vessels. We shall also see how, using thermochemical data, to predict detonation velocities and detonation pressures. 

In Chapter 8, along with tables of measured thermophysical data, we saw some fairly simple techniques for estimating these values when experimental results are not available. Among these techniques were Kopp s Rule for the heat capacity of both liquids and solids, and Trouton s ratio for latent heats of fusion and vaporization, along with Kistiakowski s temperature correction for the latter. 

For the reasons cited, it is prudent to evaluate plastics for Icmg term stresssupporting applications using linear elastic fracture mechanics in conjunction with other rheological and thermophysical data, particularly regarding long time be-hanor, aging phenomena, and failure modes. 

Touloukian, Y.S., and E.H. Buyco, Thermophysical Properties of Matter—The TPRC Data Series, Plenum, New York, 1983. 

The values in these tables were generated from the NIST REFPROP software (Lemmon, E. W, McLinden, M. O., and Huber, M. L., NIST Standard Reference Database 23 Reference Fluid Thermodynamic and Transport Properties—REFPROP, National Institute of Standards and Technology, Standard Reference Data Program, Gaithersburg, Md., 2002, Version 7.1). The primary source for the thermodynamic properties is Lemmon, E. W, and Span, R., Short Fundamental Equations of State for 20 Industrial Fluids, / Chem. Eng. Data 51(3) 785-850, 2006. The source for viscosity and thermal conductivity is McCarty, R. D., Correlations for the Thermophysical Properties of Xenon, National Institute of Standards and Technology, Boulder, Colo., 1989. 

Like the first edition, the second edition contains five chapters and several appendices, particularly a compilation of thermophysical property data needed for the solution of problems. Changes are made in those chapters presenting heat and mass transfer correlations based on theoretical results or experimental findings. They were adapted to the most recent state of our knowledge. Some of the worked examples, which should help to deepen the comprehension of the text, were revised or updated as well. The compilation of the thermophysical property data was revised and adapted to the present knowledge. 

Not much is known about the thermophysical properties of liquid metals, especially the transport properties such as chemical and thermal diffusivities. The existing data are sparse and the scatter makes it difficult to make an accurate determination of the temperature dependency of these properties. This situation was the motivation for Froberg s experiment on Space-lab-1 in which he measured the temperature dependence of the self-diffusion of Sn from 240°C to 1250°C. He found that the diffusion coefficients were 30-50% lower than the accepted values and seemed to follow a 7 dependence as opposed to the Arrhenius behavior observed in solid state diffusion. ... 

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