Computer simulations input data

Unfortunately the address of the gateway in the control computer used for the data transfer was the same as that used to connect to the distributed control system (dcs). As a result data flowed from the simulator through the control computer to the dcs and replaced the current input data by historic data. Some conditions on the plant started to change, but fortunately this was soon noticed by alert operators, and the plant was brought back under control. 

If Leung s method is inapplicable due to the presence of external heating, then alternative hand calculation methods are given in Annex 5 or a computer simulation could be used (see Annex 4). In either case, the thermal data should be obtained in a small-scale test which also simulates the external heat input. 

The compositions of the polymers were determined by simulation of their spectra using a computer curve simulator-plotting program developed by B. L. Bruner at the University of Kentucky. An example of the output of the program is shown in Fig. 4. The input data are the positions, amplitudes, and... 

The main purpose of quantum-chemical modeling in materials simulation is to obtain necessary input data for the subsequent calculations of thermodynamic and kinetic parameters required for the next steps of multiscale techniques. Quantum-chemical calculations can also be used to predict various physical and chemical properties of the material in hand (the growing film in our case). Under quantum-chemical, we mean here both molecular and solid-state techniques, which are now implemented in numerous computer codes (such as Gaussian [25], GAMESS [26], or NWCHEM [27] for molecular applications and VASP [28], CASTEP [29], or ABINIT [30] for solid-state applications). 

The key methods that are the focus of this section are categorized as analytical versus numerical methods. Analytical methods can be solved using explicit equations. In some cases, the methods can be conveniently applied using pencil and paper, although for most practical problems, such methods are more commonly coded into a spreadsheet or other software. Analytical methods can provide exact solutions for some specific situations. Unfortunately, such situations are not often encountered in practice. Numerical methods require the use of a computer simulation package. They offer the advantage of broader applicability and flexibility to deal with a wide range of input distribution types and model functional forms and can produce a wide variety of output data. 

Mujtaba (1989) simulated the same example for the first product cut using a reflux ratio profile very close to that used by Nad and Spiegel in their own simulation and a nonideal phase equilibrium model (SRK). The purpose of this was to show that a better model (model type IV) and better integration method achieves even a better fit to their experimental data. Also the problem was simulated using an ideal phase equilibrium model (Antoine s equation) and the computational details were presented. The input data to the problem are given in Table 4.7. 

Vapour phase enthalpies were calculated using ideal gas heat capacity values and the liquid phase enthalpies were calculated by subtracting heat of vaporisation from the vapour enthalpies. The input data required to evaluate these thermodynamic properties were taken from Reid et al. (1977). Initialisation of the plate and condenser compositions (differential variables) was done using the fresh feed composition according to the policy described in section 4.1.1.(a). The simulation results are presented in Table 4.8. It shows that the product composition obtained by both ideal and nonideal phase equilibrium models are very close those obtained experimentally. However, the computation times for the two cases are considerably different. As can be seen from Table 4.8 about 67% time saving (compared to nonideal case) is possible when ideal equilibrium is used. 

Table 3-17 gives the Reynolds number, friction factor, and pressure drop of catalyst pellets of 0.25 inch and at different particle length. Table 3-18 shows a typical input data and computer output with PL = 0.25 inch. The simulation exercise gives a pressure drop of 68.603 Ib/in. The results show that the pressure drop in a packed bed depends on size and shape of the particles. 

A computer model has been developed which can generate realistic concentration versus time profiles of the chemical species formed during photooxidation of hydrocarbon polymers using as input data a set of elementary reactions with corresponding rate constants and initial conditions. Simulation of different mechanisms for stabilization of clear, amorphous linear polyethylene as a prototype suggests that the optimum stabilizer would be a molecularly dispersed additive in very low concentration which can trap peroxy radicals and also decompose hydroperoxides. 

The steps in assembling the computational tools needed to simulate the explosive fracture of oil shale have been described. The resulting code, with its input data, was then used to simulate three explosive field experiments. The results of the calculations are in good agreement with what actually occurred in the field. Further detailed comparisons are in progress for these experiments and the others that have been conducted. As this is done, improvements will be made in the input data and in the code physics. 

In principle, molecular dynamics should provide the best solution to all these problems, allowing the calculation of thermodynamically stable phases at any temperature or pressure, as well as the simulation of some kinetic processes. Its results, however, also depend on the choice of potential functions, and there is no evidence that potentials suitable for such detailed applications are available now, or will be available in the near future. Huge amounts of computing times may be wasted when using a complex computational procedure whose key input data are of questionable accuracy. 

---