Dynamic Property Data

FIGURE 2.13 Thermomechanical spectra fundamentals, (a) Regions of thermomechanical spectrum, (b) viscoelastic parameters of the thermomechanical spectrum. 

FIGURE 2.14 Thermomechanical spectra comparisons, (a) Comparison of three polymers, (b) thermomechanical spectra of polymers normalized by glass transition temperature. 

FIGURE 2.15 Flexure modulus E for epoxy, PES, and PET as a function of temperature determined by the DMA test. (PES, polyether sulfone PET, polyethylene terephthalate.) (From Sepe, M. R, Advanced Materials and Processes Magazine, 4, 32—41, 1992. Reprinted with permission of ASM International.) 

More such dynamic property data can be found in Nielsen, Sepe, and Baer. In later chapters in this text. Chapters 4 and 5, further attention will be given to some other dynamic properties, namely the impact and fatigue properties, respectively. In Chapter 3, mechanical modeling of polymers is considered with the purpose of deriving differential equations to represent the fundamentals of viscoelastic behavior. Before doing so, some additional special topics are considered here. 

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Natural rubber has been used for many years in engine mounts, but there have been some failures in recent years as the underhood temperatures of certain car models have risen. Previous work has shown that brominated isobutylene-p-methylstyrene rubber, when blended at concentrations of20-30phr with NR, result in compounds with favourably low spring rates and significantly improved hot air ageing performance. This article presents some dynamic property data for rubber compounds of... 

The timely acquisition of static and dynamic reservoir data is critical for the optimisation of development options and production operations. Reservoir data enables the description and quantification of fluid and rock properties. The amount and accuracy of the data available will determine the range of uncertainty associated with estimates made by the subsurface engineer. 

If the Bath relaxation constant, t, is greater than O.I ps, you should be able to calculate dynamic properties, like time correlation functions and diffusion constants, from data in the SNP and/or CSV files (see Collecting Averages from Simulations on page 85). 

The basic approach in designing any product made from any material (steel, aluminum, wood, plastic, etc.) involves knowing the behaviors and characteristics of the materials and manufacturing influences on the materials. In turn this knowledge is to be correctly applied such as using, when required, the processed material s static and/or dynamic properties. Should a need arise for data at conditions different from those at which test data are available, with few exceptions, it would not be too difficult or costly to obtain. 

Static and dynamic property The uses of these foams or porous solids are used in a variety of applications such as energy absorbers in addition to buoyant products. Properties of these materials such as a compressive constitutive law or equation of state is needed in the calculation of the dynamic response of the material to suddenly applied loads. Static testing to provide such data is appealing because of its simplicity, however, the importance of rate effects cannot be determined by this one method alone. Therefore, additional but numerically limited elevated strain-rate tests must be run for this purpose. 

The data included provides examples of what are available. As an example static properties (tensile, flexural, etc.) and dynamic properties (creep, fatigue, impact, etc.) can range from near zero too extremely high values. They can be applied in different environments from below the surface of the earth, to over the earth, and into space. 

These models are too simple to reflect realistic dynamic properties of the carbon budget. Even so, they depend on data that are poorly measured or lacking. Many potentially important compartments are missing or assumed to be unimportant. For example, no model considers carbon transported from terrestrial systems to the oceans through rivers and streams. While the amount is very small, it is continuous and cumulative (25)... 

Unlike TMTD, TBzTD is unique since use of small amount of TBzTD (0.1-0.2 phr) with sulfenamide system does not influence the processing characteristics while improving cure rate and dynamic properties. A comparative data are tabulated in Tables 14.6 through 14.10. Details are reported by Datta et al. [24]. 

Equilibrium data correlations can be extremely complex, especially when related to non-ideal multicomponent mixtures, and in order to handle such real life complex simulations, a commercial dynamic simulator with access to a physical property data-base often becomes essential. The approach in this text, is based, however, on the basic concepts of ideal behaviour, as expressed by Henry s law for gas absorption, the use of constant relative volatility values for distillation and constant distribution coeficients for solvent extraction. These have the advantage that they normally enable an explicit method of solution and avoid the more cumbersome iterative types of procedure, which would otherwise be required. Simulation examples in which more complex forms of equilibria are employed are STEAM and BUBBLE. 

A rather crude, but nevertheless efficient and successful, approach is the bond fluctuation model with potentials constructed from atomistic input (Sect. 5). Despite the lattice structure, it has been demonstrated that a rather reasonable description of many static and dynamic properties of dense polymer melts (polyethylene, polycarbonate) can be obtained. If the effective potentials are known, the implementation of the simulation method is rather straightforward, and also the simulation data analysis presents no particular problems. Indeed, a wealth of results has already been obtained, as briefly reviewed in this section. However, even this conceptually rather simple approach of coarse-graining (which historically was also the first to be tried out among the methods described in this article) suffers from severe bottlenecks - the construction of the effective potential is neither unique nor easy, and still suffers from the important defect that it lacks an intermolecular part, thus allowing only simulations at a given constant density. 

Distributions of relaxation or retardation times are useful and important both theoretically and practicably, because // can be calculated from /.. (and vice versa) and because from such distributions other types of viscoelastic properties can be calculated. For example, dynamic modulus data can be calculated from experimentally measured stress relaxation data via the resulting // spectrum, or H can be inverted to L, from which creep can be calculated. Alternatively, rather than going from one measured property function to the spectrum to a desired property function [e.g., Eft) — // In Schwarzl has presented a series of easy-to-use approximate equations, including estimated error limits, for converting from one property function to another (11). 

One key NMR-based study has focused on the evaluation of the dynamic properties of heparin-like hexasaccharides.20 The analysis of Tj, T2 and NOE 13C-NMR data of biologically active synthetic compounds has shown that the sulphation pattern strongly influences the internal dynamics, and supports the importance of the GAGs flexibility on the selectivity of the interaction with fibroblast growth factors. 

However, it has turned out that the most accurate way of fixing these parameters is through matching of simulated phase equilibria to those derived from experiment.33 As a final step, the potential, regardless of its source, should be validated through extensive comparison with available experimental data for structural, thermodynamic, and dynamic properties obtained from simulations of the material of interest, closely related materials, and model compounds used in the parameterization. The importance of potential function validation in simulation of real materials cannot be overemphasized. 

Polybutadiene. 1. Comparison With Experimental Data for Static and Dynamic Properties. 

Because efficient methods for computing free volumes from molecular simulations were introduced only recently, their connections to the dynamical properties of liquids have yet to be explored systematically. Nonetheless, initial investigations have already allowed scrutiny of some historical notions about these properties. Here, we briefly discuss two of these initial studies. Their results illustrate that some early free-volume based ideas about the origins of dynamics are consistent with simulation data, but those ideas will need significant revision if they are to be applied in a general way. 

Aluminum exhibits a modest increase with strain rate which is typically ignored. Lindhoim 1969 surveyed available test data on dynamic properties for a number of materials. This is an extremely useful resource for information on less commonly used materials. 

These analysers exist in many forms but are essentially relatively small bench instruments, which use small test pieces and can be programmed to measure damping and dynamic moduli as a function of temperature and frequency. Apart from their importance for measuring the dynamic properties where these are relevant to service, they allow the generation of a large quantity of data over ranges of temperature and frequency extremely efficiently. Hence, they can be used effectively to obtain modulus even if the application is not dynamic. Another valuable use is to obtain glass transition temperatures. 

Although the apparatus is expensive, DMTA produces a lot of data in a short time and is hence a very efficient way of generating dynamic properties. Furthermore, it has the advantage of being non-destructive and uses a small test piece. 

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