Viscosity prediction

For both polar and nonpolar nonhydrocaihon gaseous mixtui es at low pressui es, the most accurate viscosity prediction method is the method of Brokaw. The method is quite accurate but requires the dipole moment and the Stockmayer energy parameter (e/A ) for polar components as well as pure component viscosities, molecular weights, the normal boding point, and the hq-uid molar volume at the normal boding point. The Technical Data Manual should be consulted for the fidl method. 

TABLE 2-398 Group Contribution Values for Liquid Viscosity Prediction... 

Viscosities of the siloxanes were predicted over a temperature range of 298-348 K. The semi-log plot of viscosity as a function of temperature was linear for the ring compounds. However, for the chain compounds, the viscosity increased rapidly with an increase in the chain length of the molecule. A simple 2-4-1 neural network architecture was used for the viscosity predictions. The molecular configuration was not considered here because of the direct positive effect of addition of both M and D groups on viscosity. The two input variables, therefore, were the siloxane type and the temperature level. Only one hidden layer with four nodes was used. The predicted variable was the viscosity of the siloxane. 

For all the siloxanes the network was trained at two temperature levels 25°C and 75°C. The trained network was then tested for its viscosity predictions at 50°C. The network training and testing results are shown in Fig. 8. The rms error for this prediction was 0.002. 

Figure 9 Comparison of viscosity predictions between ANN model and Batschinski s equation.

The effective viscosity predicted with micropolar theory is in very close agreement with that found by experimental results in a previous work. This does not adequately assure that it is the only possible way to explain the traits of thin him lubrication, but it shows the roles the microstructure and microrotation of the particles will play in the lubrication process in the very thin him EHL situation. 

Nonequilibrium molecular dynamics simulations provide another approach for viscosity predictions. This approach as well as equilibrium molecular dynamics was reviewed. 

The variations in the intrinsic viscosity predicted by the primary electroviscous effect are often small, and it is difficult to attribute variations in the experimentally observed [17] from the Einstein value of 2.5 to the above effect since such variations can be caused easily by small amounts of aggregation. Nevertheless, Booth s equation has been found to be adequate in most cases. Further discussions of this and related issues are available in advanced books (Hunter 1981). 

The dynamic RIS model developed for investigating local chain dynamics is further improved and applied to POE. A set of eigenvalues characterizes the dynamic behaviour of a given segment of N motional bonds, with v isomeric states available to each bond. The rates of transitions between isomeric states are assumed to be inversely proportional to solvent viscosity. Predictions are in satisfactory agreement with the isotropic correlation times and spin-lattice relaxation times from 13C and 1H NMR experiments for POE. 

Cao, W., et al., Group-Contribution Viscosity Predictions of Liquid Mixtures Using UNIFAC-VLE Parameters. Ind. Eng. Chem. Res., 1993 32, 2088-2092. 

Compared experimental measurements for Newtonian and power law fluids to theoretical predictions and showed, that the apparent viscosity predicted by the Bostwick measurement must be correlated with flow behavior during processing and thus could be very useful to incorporate into food process design and control. 

The ability to correctly reproduce the viscosity dependence of the dephasing is a major accomplishment for the viscoelastic theory. Its significance can be judged by comparison to the viscosity predictions of other theories. As already pointed out (Section II.C 22), existing theories invoking repulsive interactions severely misrepresent the viscosity dependence at high viscosity. In Schweizer-Chandler theory, there is an implicit viscosity dependence that is not unreasonable on first impression. The frequency correlation time is determined by the diffusion constant D, which can be estimated from the viscosity and molecular diameter a by the Stokes-Einstein relation ... 

The infinite viscosity prediction of Eq. (3.2) as approaches its maximum value is not necessarily correct. In the case of a spatially periodic lattice (Section VII), a rigorous analysis incorporating the time dependence of the relative positions of adjacent spheres in a shear flow provides results counter... 

In plastics, relative viscosity can be defined as the ratio of the viscosity of a concentrate to that of the neat carrier polymer at the same temperature. In this use, relative viscosity predicts relative jetness—defined in the same manner—more accurately than the carbon black loading in a concentrate. A mismatch between masterbatch and letdown viscosities causes incomplete mixing and is the reason that relative jetness can decrease with an increase in relative viscosity [5]. 

One should be careful because this utilizes the volume shifted PR equation. Substituting other density data will result in increased errors in the viscosity predictions. 

Figure 4.12 The points give the measured viscosity-temperature relationship for e-terphenyl, while the shaded regions are the viscosities predicted by the Adam-Gibbs equation (4-10) using A5(7 ) measured for o-terphenyl. The two shaded regions represent alternative fits of the Adam-Gibbs parameters, one fit to the high-temperature, and the other to the low-temperature, data. (From Greet and Turnbull, reprinted with permission, from J. Chem. Phys. 47 2185, Copyright 1967, American Institute of Physics.)...

Zupan, J. Kristi, J. Viscosity prediction of lipophilic semisolid emulsion systems by neural network modelling. Int. J. 83. Pharm. 2000, 196, 37-50. 

The free-volume parameters were estimated from viscosity and temperature data of pure components and the binary interaction parameter between the component and the polymer was determined using the group-contribution lattice-fluid equation of state (GCLF-EOS) (Alvarez, 2005, Alvarez et al, 2008). The innovative application of zero shear viscosity predicted data was proposed in this work for POMS free-volume parameters, as an alternative when experimental polymeric membrane viscosity data are scarce or inexistent. 

The development of new polymeric structures for different technological applications usually requires knowledge about properties of this material. The prediction of properties using additive group contribution method is a valuable procedure adopted during the developments presented here. The group contribution method concept was applied to obtain viscosity data versus temperature, an intermediate step of the free-volume parameters estimation procedure (equation (2) inputs). Detailed concepts about prediction of polymer properties were studied and applied as presented in specific literature (Van Krevelen, 1992 Bicerano, 2002). Equations (4) and (5) are the key equations of the procedure to obtain zero shear viscosity predicted data. The references adopted in this section also allows to predict many others polymer properties. 

Statistical theories of surface viscosity predict values of 10 10 g/sec or smaller. ESR spin exchange effects provide a method that may be unique for measuring such small values. 

The HCToolkit is a set of Perl modules that implement four equations of state, two flash algorithms and a multi-component, multiphase, temperature- and pressure-dependent viscosity prediction. The modules have been successfully run on MS Windows, Mac OSX and Redhat Linux. These modules can be called from another Perl code, or (via an ActiveX interface) from a front-end written in either Visual Basic or a VBA application such as Excel. 

The indicated above importance of polymer melts viscosity causes the appearance of a considerable number of theoretical treatments, describing this property on the basis of either representations, mainly fiom the point of view of free volume [3], In the present chapter polymer melts viscosity is treated within the framework of fractal analysis [5]. This is due to the fact, that the macromolecular coil in polymer solutions and melts is a fiactal [6] that creates prerequisites for the polymer melt viscosity prediction quite at the synthesis stage. The authors of Ref [7] demonstrated the possibility of polymer melts viscosity description and prediction within the fiamework of fiactal analysis on the example of two polymers of different classes—aromatic poly-ethersulfonoformals (APESF) and high-density polyethylene (HOPE). MFI values were determined on the automatic capillary viscometer IIRT-A at temperatures and loads, listed in Table 1. The fiactal dimension of a macromo-... 

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