Simulation code

Monte Carlo Simulation code (written in Visual Basic)... 

Matthews, S.D., 1977, MOCARS A Monte Carlo Simulation Code for Determining Distribution and Simulation Limits, TREE-1138, July. 

This section does not contain any fundamentals or mathematics bur tries to describe the basic energy flows and the methods used in thermal building-dynamics simulation codes to model these. Also, the methods are described without stating the underlying algorithms and equations, for which the reader is referred to the literature and references. A short outline of how these models affect the application possibilities and limits is given at the end of this section and also in Section 11.3.7. 

In thermal building-dynamics simulation codes, outdoor conditions are mostly input by the so-called weather data file, containing (usually hourly) data for air temperature, wind speed and direction, air humidity, and global and diffuse solar radiation on horizontal surfaces. 

In reality, heat is conducted in all three spatial dimensions. While specific building simulation codes can model the transient and steady-state two-dimensional temperature distribution in building structures using finite-difference or finite-elements methods, conduction is normally modeled one-... 

To describe rhe dynamic thermal behavior of the envelope and internal structural elements, the following two methods are most often used In thermal building simulation codes ... 

For the calculation of such glazing in building-dynamics simulation codes, each pane is considered as a layer, exchanging energy with the other panes, with the room, and with the exterior. 

Moisture-transport simulation includes transport as well as storage phenomena, quite similar to the thermal dynamic analysis, where heat transfer and heat storage in the building elements are modeled. The moisture content in the building construction can influence the thermal behavior, because material properties like conductance or specific heat depend on moisture content. In thermal building-dynamics simulation codes, however, these... 

Systems and control configurations as required may not be available in the simulation code or may not be able to be described and modeled in the required detail. Depending on the application, an optional system extension or change can be defined using the existing model. Otherwise, a new model has to be developed and integrated into the code. In these cases, it is decisive for the selection of code used whether and how easily such an extension can be implemented. 

Transfer through Windows ) and made available in the simulation code by means of a database. 

Van den Berg, A. C. 1980. BLAST—a 1-D variable flame speed blast simulation code using a Flux-Corrected Transport algorithm. Prins Maurits Laboratory TNO report no. PML 1980-162. 

We would like to end our contribution by reporting some recently obtained results, which stress the two main practical advantages of the UHC regime discussed in Sect. 10.2 the possibility of using very thin targets and the improved shot-to-shot reproducibility. This latter point makes UHC interaction experiments a good benchmark to test simulation codes and/or analytical models. 

In this section we consider general process simulator codes rather than specialized codes that apply only to one plant. To fnesh equation-based process simulators with optimization codes, a number of special features not mentioned in Chapter 8 must be implemented. 

As to the automatic generation of exact derivatives in existing modular-based process simulator codes directly from the code itself, refer to Griewank and Corliss (1991) or Bischof et al. (1992). 

Mirroring a good model of the world in your code is good for simulations Every time you change your picture of the world, you can more easily find which parts of the simulation code to change. This is how OOP originated with the Simula language. 

We have seen that Lagrangian PDF methods allow us to express our closures in terms of SDEs for notional particles. Nevertheless, as discussed in detail in Chapter 7, these SDEs must be simulated numerically and are non-linear and coupled to the mean fields through the model coefficients. The numerical methods used to simulate the SDEs are statistical in nature (i.e., Monte-Carlo simulations). The results will thus be subject to statistical error, the magnitude of which depends on the sample size, and deterministic error or bias (Xu and Pope 1999). The purpose of this section is to present a brief introduction to the problem of particle-field estimation. A more detailed description of the statistical error and bias associated with particular simulation codes is presented in Chapter 7. 

In general, all MC simulation codes enjoy the following favorable attributes. 

On the other hand, as was pointed out above, all MC simulation codes suffer from statistical noise that must be minimized (or at least understood) before valid comparisons can be made with experimental data (or other CFD methods). 

The three-dimensional FDM technique provided an excellent prediction of the pressure at 5.6 diameters from the start of the screw, as shown in Fig. 7.16. The method, however, is difficult to use and requires relatively long computational times on a fast computer. This example is an excellent test case for determining the acceptability of a simulation code. 

In this chapter, we review the current status of doping of SiC by ion implantation. Section 4.2 examines as-implanted depth profiles with respect to the influence of channeling, ion mass, ion energy, implantation temperature, fluence, flux, and SiC-polytype. Experiments and simulations are compared and the validity of different simulation codes is discussed. Section 4.3 deals with postimplant annealing and reviews different annealing concepts. The influence of diffusion (equilibrium and nonequilibrium) on dopant profiles is discussed, as well as a comprehensive review of defect evolution and electrical activation. Section 4.4 offers conclusions and discusses technology barriers and suggestions for future work. 

This section provides a brief description of theoretical bases of the Monte Carlo track simulation codes we have developed for electrons and ions. Our database of Monte Carlo track simulation codes include electrons (code kurbuc 10 eV to 10 MeV) [174], protons lephist —1 keV to 1 MeV) [175], alpha particles (leahist —1 keV to 8 MeV) [176], all ions... 

It is important that the computer code chosen is suitable for carrying out physical property calculations for pure gassy systems. Most simulation codes require the reaction mechanism to be sufficiently well understood that data including stoichiometric coefficients for the reaction and the molecular weight of the evolved gas(es) can be supplied. It is recommended that these data be derived from suitable adiabatic experiments (see Annex 2). A few codes make direct use of adiabatic experimental data, so that a full understanding of the reaction is not required. Most codes assume that the evolved gas can be treated as ideal, and, if this is not the case, an appropriate code must be found. 

---