Rigorous simulation methods

There is a consensus that Maxwell s equations work fine for nanoparticles with size down to at least 1 nm. In other words, a good fit of experimental data can be obtained using a rigorous simulation method and proper data for the complex electric permittivity e or equivalently the complex refractive index h [36]. This makes choosing a particular value of e an important practical question, which can be divided into two parts ... 

The current level of theory (with all of the approximations that it entails) has provided some useful insights into the nature of drug resistance [58] (in a thermodynamic sense) and it is capable of quantitative accuracy. For the type of problem considered here it is a useful alternative to the more rigorous simulation methods or to the more empirical QSAR methods. 

Needless to say, a simplified model leads to corresponding thermodynamic quantities, i.e. not all correlations are included. However, the thermodynamic framework itself is fully internally consistent. This is an important observation, because such a model can for this reason be of use to establish the thermodynamic feasibility of what-if questions. Full control over the absolute deviations from the true thermodynamic behaviour is unfortunately not possible. The approach ignores important (cooperative) fluctuations, and it is expected that especially near phase transitions the approach may give only qualitative results. In particular, comparison of SCF results with experiments or with simulation data can lead to insights into how rigorous the method is. 

The various solution methods were applied to a set of measurements taken at different reactor conditions. Table 5 gives the data used for the reconciliation. The value corresponding to the specific heat is an effective specific heat, calculated from rigorous simulations using the Phenics package, and takes into account the heat of reaction. 

The problems being addressed in recent work carried out in various laboratories include the fundamental nature of the solute-water intermolecular forces, the aqueous hydration of biological molecules, the effect of solvent on biomolecular conformational equilibria, the effect of biomolecule - water interactions on the dynamics of the waters of hydration, and the effect of desolvation on biomolecular association 17]. The advent of present generation computers have allowed the study of the structure and statistical thermodynamics of the solute in these systems at new levels of rigor. Two methods of computer simulation have been used to achieve this fundamental level of inquiry, the Monte Carlo and the molecular dynamics methods. 

Consult the designer and check the feasibility of the separation method by rigorous simulation. If the design is not feasible go back to the point (4) and select another split. If the split is acceptable, find the component distribution in the product streams. Do not optimise the design at this stage. 

As reference we consider the direct separation scheme. The design of columns was done in Aspen Plus by means of shortcut methods followed by rigorous simulation. The energetic consumption depends on the reflux ratio. We assume that the optimal R/Rmi is 1.3, and column pressures of 2 and 1 bar with 0.2 bar pressure drop. Table 11.2 presents the results. Note that the initial feed temperature is 298 K, and therefore the reboiler duty of the first column includes feed preheating. 

Molecular computer simulations are the method of choice when it comes to investigating molecular models like the one discussed earlier, both in the bulk and at interfaces. Contrary to analytical theories, simulation methods can treat a variety of different models on the same statistical-mechanical footing, without mathematically mandated approximations that cannot be rigorously justified and/or tested. In fact, computer simulations have been used as computer experiments to verify or falsify theoretical work. 

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