Predictive material models

Holmgren T, Persson L, Olofsson U, Andersson P, Haglund P (2010) Predictive emission model for organic compounds added to materials in consumer goods. Extended abstract, SET AC Milan 2011... 

Of course, more complicated situations and conditions will require more sophisticated mathematical treatment, especially for the driving force, but the basic flux relationships are similar for any liquid and gas migration through the subsurface. If the hydraulic conductivities and diffusion coefficients are known for the materials and each migrating fluid of interest, then predictive computer models can often handle the difficult calculations associated with multiple fluids, multiple pressures, and multiple types of materials. 

Baner, A., Brandsch J. Franz, R. and Pringer, O. (1996). The application of a predictive migration model for evaluating the compliance of plastic materials with European food regulations. Food Addit. Contain., 13, 5, 587-601. 

PP bead foams were subjected to oblique impacts, in which the material was compressed and sheared. This strain combination could occur when a cycle helmet hit a road surface. The results were compared with simple shear tests at low strain rates and to uniaxial compressive tests at impact strain rates. The observed shear hardening was greatest when there was no imposed density increase and practically zero when the angle of impact was less than 15 degrees. The shear hardening appeared to be a unique function of the main tensile extension ratio and was a polymer contribution, whereas the volumetric hardening was due to the isothermal compression of the cell gas. Foam material models for finite element analysis needed to be reformulated to consider the physics of the hardening mechanisms, so their predictions were reliable for foam impacts in which shear occurred. 16 refs. 

The discussion in the previous section suggests that the track of a heavy ion becomes more like that of a fast electron with increasing velocity. Therefore one expects that in the high velocity limit the yields of water products with heavy ions are the same as with fast electrons or y-rays. The yields for the major products of water radiolysis in fast electron or y-radiolysis are given in Table 1. These values were taken from a number of different sources in conjunction with the results predicted by model calculations [73,116,119-123]. Material balance shows that almost four molecules of water are decomposed for every 100 eV of energy absorbed by fast electrons or y-rays. Because only about six water molecules are initially decomposed, most of the water products escape intraspur reactions in fast electron or y-radiolysis. 

To study different operating conditions in the pilot plant, a steady-state process simulator was used. Process simulators solve material- and energy-balance, but they do not generally integrate the equations of motion. The commercially-available program, Aspen Plus Tm, was used in this example. Other steady-state process simulators could be used as well. To describe the C02-solvent system, the predictive PSRK model [11,12], which was found suitable to treat this mixture, was applied. To obtain more reliable information, a model with parameters regressed from experimental data is required. 

Because diffusion dominates the transport of contaminants in barriers and columns constructed of low-permeability materials, model calibrations and predictions are extremely sensitive to the form of the specified boundary conditions. Two issues are of particular importance 1) treatment of the entrance mixing zone in laboratory columns, and 2) specification of appropriate BCs to represent a slurry wall under field conditions. 

Therefore under a constant stress, the modeled material will instantaneously deform to some strain, which is the elastic portion of the strain, and after that it will continue to deform and asynptotically approach a steady-state strain. This last portion is the viscous part of the strain. Although the Standard Linear Solid Model is more accurate than the Maxwell and Kelvin-Voigt models in predicting material responses, mathematically it returns inaccurate results for strain under specific loading conditions and is rather difficult to calculate. 

Glimm and Sharp [307] proposed a challenge to the twenty-first century researcher to consider multiscale materials modeling as a new paradigm in order to realize more accurate predictive capabilities. The context is to predict the macroscale/ structural scale behavior without disregarding the important smaller scale features. 

Models to predict materials performance in industrial applications, or to assess the environmental consequences of some industrial activity, are a major immediate need. The requirement for such models is driven by environmental concerns, such as a desire to avoid groundwater contamination, and industrial concerns such as the necessity of reducing costs by extending plant lifetimes and operating efficiencies. Since many materials corrosion and mineral dissolution processes are electrochemical in nature, electrochemical techniques are commonly used in the study and development of solutions to these problems. 

Here we outline an approach that has been taken to develop Poly(dimethylsiloxane) (PDMS) systems, which have property changes that are easier to predict. The problems associated with the inclusion of a filler phase that is required for PDMS systems to have many useful physical properties have been addressed by producing nano filled equivalents. It is shown that such systems offer easier control over the materials produced whilst also resulting in a simplification of physical properties. The production of foamed systems, which introduces an additional variable, is also discussed. The influence of foam structure upon the measured properties of a material is outlined and implications for sample production and the development of predictive ageing models are explored. 

Frauenheim T, Seifert G, Elstner M, Hajnal Z, Jungnickel G, Porezag D, Suhai S, Scholz R (2000) A self-consistent charge density-functional based tight-binding method for predictive materials simulations in physics, chemistry, and biology (2000) Physica Status Solid B 217 41-62 Espan ol P (1998) Fluid particle model. Phys Rev E 57 2930-2948... 

---