airplane simulator

the simulation is predominant a proceeding for the analysis of dynamic systems. During the simulation experiments at a model are accomplished, in order to win realizations over the material system. In connection with simulation one speaks of the one which can be simulatedSystem and of a simulator as implementation or implementing of a simulation model. The latter represents an abstraction of the system which can be simulated (structure, function, behavior). The expiration of the simulator with concrete values (Parametrisation) is called simulation experiment. Its resultscan be interpreted then and be transferred to the system which can be simulated.

An auto Crashtest for example is a simulation model for material traffic conditions, in which a car is complicated into a traffic accident. The prehistory of the accident becomes, the traffic conditions andthe exact condition of the accident opponent highly simplifies. Also no persons are involved into the simulated accident, but it instead Crashtest Dummies assigned, which have certain mechanical characteristics together with material humans. A simulation model has thus only completely determined aspects alsoa material accident together. Which aspects are this, depends considerably on the question, which is to be answered with the simulation.

Some Simulationen are accomplished by Simulanzien, thus materials, those for research or attempt purposes the characteristics of other materialscopy (simulate). An example of it is ballistic gel in the field of the weapon research. She simulates the behavior of body fabrics and organs when the penetration projectiles.

Horse simulator in the 1. World war

table of contents

reasons for the employment

for the employment of Simulationen can it several reasons give:

  • An investigation at the material system would be ethicalally not justifiable or too dangerous too complex, too expensively. Examples:
    • Driving simulator (too dangerous in the reality)
    • flight simulator for pilot training, adjustment of critical scenarios (engine loss, forced landing)
    • Crashtest (too dangerous or too aufwändig in the reality)
    • Simulation of manufacturing plants before a change (repeated change of the plant in the reality would be too aufwändig and too expensive)
  • the material system does not exist (still). Example: Wind tunnel experiments with airplane models, before the airplane is not manufactured
  • the material system leaves itselfsystem-dependent observe
  • for experiments knows a simulation model substantially more easilyare modified as the material system. Example:Model construction in the town planning
  • accurate reproductibility of the experiments
  • safe ones and economical training. Example: Flight simulation, shooting training
  • the material system is unverstanden or very complex. Example: During the evaluation of scientific experiments those must Results by simulation to be interpretably made
  • play and fun at simulated scenarios.
  • Method in the Pädagogik. Examples: Game of roles, simulation plays

nowadays Simulationen more and more are realized by computers, because computers ideal and very flexible surrounding field for nearlyall kinds of the simulation offer (see also computer simulation).

general fundamental one must differentiate

types and ranges


Simulationen [work on] between Simulationen with and without computers. A simulation is “as if” - playing to the end processes;one can do that also without computers. If today from “simulation” the speech is, one however nearly always means computer simulations. The latters were divided into the ranges

  1. Spielsimulationen, z. B. Flugsimulationen, Rennsimulationen, Wirtschaftssimulationen
  2. enterprise simulation for the outand further training, z. B.Business planning game
  3. technical Simulationen, z. B. Schaltungssimulationen, Atomwaffensimulationen, Windkanalsimulationen u.v.m.
    With small systems offers itself here also by means of Model Checking a verification, which, contrary to the simulation, guarantees all cases takes off, but a high cost of computationhas.
  4. Scientific Simulationen. It gives it in nearly all nature and society sciences:
    • Meteorological simulation to the weather forecast
    • physical simulation and astrophysical simulation
    • chemical simulation
    • biological Simulationen, z. B. Simulation of neural nets (see neural net)
    • socio-economic simulation, z.B.Multi-agent systems
    • u.v.m.

types of Simulationen

much to do…

Here is only mentioned:

theory, model and simulation

a computer simulation consists in the core of a program. Here the rules of the processes are defined. Onespeaks similarly to the “behavior equations” of a mathematically defined system of the “behavior algorithms”. In addition, models can be invented ad hoc, them can from empirical findings or from a theory be derived. E.G. become the weather models from the theory of hydrodynamicsderived, consist the individual behavior equations of Navier Stokes equations (or advancements of it).

of simulation also

borders are set

to queue model multi-level model cellular automat multi-agent model (multi-agent system), which one must always consider.The first border follows from the limitness of the means, i.e. the finiteness of energy (e.g. also computer capacity), time and not least money. A simulation must result in thus also economically seen sense. Due to these restrictions a model must be as simple as possible.That again means that also the results of the simulation represent a rough simplification of the reality. The second border follows from it: A model supplies only in a certain context results, which can be transferred to the reality. Within other parameter ranges can the results directly wrongly its. Therefore the verification of the models for the respective application is an important component of the simulation technique. As possible further borders are inaccuracies of the original data (e.g. Measuring error), as well as subjective obstacles (e.g. information flow lacking over production errors) mentioned.


  • B.P. Zeigler, H. Praehofer, T.G. Kim: Theory OF Modeling and simulation, 2nd edition, Academic press, San Diego 2000, ISBN 0-127-78455-1
  • N. Gilbert, K.G. Troitzsch: Simulation for the social scientist, open University press 1999, ISBN 0-335-19744-2
  • R.M. Fujimoto: Parallel and Distributed simulation of system, Wiley Interscience 1999, ISBN 0-471-18383-0
  • F.E. Cellier: Continuous system modeling. Springer publishing house, New York, 1991, ISBN 0-387-97502-0

see also

Web on the left of


  > German to English > (Machine translated into English)