Simulation Practice and Theory最新文献

The paper presents a study of several alternatives of a fault-tolerant process controller design. We compare controller architectures based on different amount of hardware redundancy with those using time redundancy. The system behaviour is evaluated by means of a process-oriented simulation model enabling software injection of faults. As an overall measure of controller design quality (which includes both performance and reliability) we use the numerical error of the output. The results obtained on the model are used to show the dependence of the output error upon the relative speed of computation and upon the rate of faults damaging the data. Thus for every set of parameters, a system configuration which gives the best results, can be determined.

We replace the classical procedure for recursive linear interpolation with a technique based on simple fuzzy-set logic. This approach makes it much easier to construct multi-input interpolation procedures. The resulting function generators produce the same output as the classical formulas with slightly fewer CPU cycles if we precompute and store some coefficients before the simulation. Lin and Zhang's algorithm [K.-C. Lin, B. Zhang, Simulation 68 (3) (1997) 157–163], which precomputes and stores more interpolation-polynomial parameters, is even faster but requires much more runtime storage in the case of large function tables.

The tool of iteration function systems (IFS) was explored to simulate particle coarsening. This approach avoids assumptions like the mean field and the continuity equation for the particle size distribution. The stochastic character of the transport of mass, growing and dissolving of particles was taken into consideration. Studies of the interface-controlled and diffusion-controlled coarsening are presented. The usual theoretical asymptotic characteristics of particle coarsening were obtained, although the resulted size distributions showed differences that should be investigated. The IFS seems to be a powerful tool to simulate complex coarsening cases in a more simple way than other approaches.

In the present paper the behavior of the interior permanent magnet synchronous motor (IPMSM) fed by a current controlled voltage source inverter under static and dynamic conditions is presented. A method is explained, which estimates the rotor position angles and rotating speeds at several conditions by measuring the instantaneous values of stator voltages and currents. The estimator is an extended Kalman filter (EKF). The simulation results under four different dynamic conditions, namely the starting, loading of the motor, reversing of speed, and starting with unknown initial rotor position value, are presented.

Computer simulation is a well-established decision support tool in the manufacturing industry. The rapid development and deployment of simulation models however, are inhibited by factors such as inefficient data collection, lengthy model documentation, and poorly planned experimentation. Typically, more than one third of project time is spent on identification, collection, validation, and analysis of input data. Whilst most research work has been focused on statistical techniques for data analysis, less attention has been paid to the development of systematic approaches to input data gathering. This paper presents a methodology for rapid identification and collection of input data in batch manufacturing environments. A functional module library and a reference data model, both developed using the IDEF (Integrated computer aided manufacturing DEFinition) family of constructs, are the core elements of the methodology. The paper also identifies the major causes behind the inefficient collection of data.

We construct a reactive lattice-gas cellular automaton (LGCA) for reaction–diffusion systems and provide extensive discussion of its software coding aspects. The software coding aspects provide rationale for some choices in the construction of LGCA which has been inspired by molecular dynamics. Portability of the C language source code, of the data structures, and of the data formats is discussed and explained. We illustrate the ideas behind the development of LGCA and its code by considering a particular reacting system, the Sel'kov model with immobile complexing species. We demonstrate usefulness of LGCA modelling of reactive systems by presenting various simulation results. We compare these results with the standard numerical simulations of reaction–diffusion equations. We conclude the paper by discussing how LGCA methodology can be applied and extended to other contexts.

An activated sludge process is considered in this work for comparative tests of new integration algorithms. Based on the configuration of the process and on the process kinetics for heterotrophic bacterial growth, the mathematical model of the considered process has been derived in the form of a state ordinary differential equation system. The state ordinary differential equation system describing the considered process may be both stiff and non-stiff for operator's control changes of the oxygen feeding flow rate. In the work, new discrete response equivalent (DRE) integration algorithms are proposed for simulation runs with a fixed integration step size, which is independent of the process dynamics (this possibility is due to self-adaptive features of the algorithms). The proposed algorithms have been compared with most other frequently used integration algorithms. The comparative tests show that, among the compared algorithms, only the DRE integration algorithms may be used with a fixed, arbitrarily chosen integration step size for simulation of the state ordinary differential equation system which may be both stiff and non-stiff during simulation.

This is the introduction to our Bondgraph (BG) issue, which deals with several problems of practical simulation. First we define the art of Bondgraphing, where causality is stressed. One chapter makes a comparison of traditional versus BG modeling. Then follows some help with practical simulation, the paracheck and compact physical units. Next there is estimation of time constants and resonance frequencies, which determine the required time step in the simulation. Finally, a trap is shown with quadratic resistances, which really comes from faulty physical reasoning. We conclude with the advantages and limitations of BG and with some suitable computer programs.