Under computer simulation or computer simulation refers to the implementation of a simulation using a computer, more precisely, of a computer program. This program describes or defines the simulation model. One of the first computer simulations is one of the Fermi - Pasta - Ulam experiment.
A note on computer simulation in engineering: The simulation of the processes considered are rarely directly optimized, eg the aerodynamic drag of a car. Only by (repeated ) interpretation of the simulation results and building upon changes to the model better solutions for a specific problem can be found.
Types of Simulation
In the static simulation, the time does not matter. The model is static, that is, it only looks at a time, so it is effectively a snapshot.
Monte Carlo simulation
Foot the simulation on random numbers and / or stochastics (probability mathematics), it is called because of the conceptual proximity to gambling by Monte Carlo simulation. This method has been found particularly important in physics applications, and two books one-and- by the same author are among the most cited publications in this science division.
For the models of the dynamic simulation, the time always plays a significant role. The dynamic simulation considered processes or procedures.
In the continuous simulation continuous processes are mapped. This type of simulation uses differential equations to represent physical and biological laws, which are the underlying process to be simulated.
Discrete event simulation
Discrete simulation used the time to elicit a statistically or randomly sized according to time intervals certain events, which in turn, the ( next ) to determine system status.
Also referred to as process simulation or event-driven simulation, is the discrete simulation in production and logistics area their primary application. The vast majority of practical problems is in this range. The models of this simulation are displayed in contrast to the continuous well with standardized elements (eg, random numbers, queues, probability distributions, etc.). Another powerful approach to the development of discrete, event-driven models offers the Petri net theory.
The strength of the discrete simulation is that it in the model involving and at sufficiently frequent tracing a statement about the expected probability of the various system states provides the chance or likelihood. The field of application for this type of simulation is therefore correspondingly large:
- Workflows in production ( all automobile manufacturers are large simulation user)
- Processes of logistics ( supply chains, container handling, etc.)
- Processes with large passenger or freight traffic (airports, large railway stations, as well as highway toll stations, public transportation systems, postal distribution centers, switching stations, etc.)
Simulation of hybrid is referred to when the characteristics of the model to both continuous and discrete simulation has. Such model can be found for example in medical simulations - especially for training purposes - again, in which to be simulated biology is not well known, in order to create a sufficiently detailed, continuous model can.
Under System dynamics is the simulation
- Dynamic and
- With feedback
Understood systems. Essentially, under such simulators
- The feedback behavior of socioeconomic systems ( "Industrial Dynamics ")
- The development of urban centers ( "Urban Dynamics " ) and
- World models, such as the Club of Rome ( "World Dynamics " )
Subsumed. The procedures and tools are almost entirely those of the control technology and cybernetics.
The multi-agent simulation, which can be seen as a special case of the discrete simulation allows to model emergent phenomena and dynamic interactions.
Although a simulation program (simulator ) in principle with any general programming language - in simple cases, even with standard tools such as a spreadsheet - even special simulation languages developed - can be created, have been since the 1960s - after the initial availability of sufficiently fast computer.
Initially, these languages still limited to the purely mathematical or numerical determination and presentation of the simulation curves and results. However, with the advent of increasingly powerful computers in the 1980s, came more and more added to the graphical representation and, more recently, the animation.
In discrete simulation, there are efforts to implement an optimizing process, such as neural networks, genetic algorithms and fuzzy logic. These components are the classic simulators which do not affect optimizing itself, the property of self- search for optimal solutions to add.
The term " digital factory" try large companies - particularly the automotive and aircraft construction - the (mostly animated ) flow simulation method to determine costs, for automating the creation of technical documentation and planning systems for production facilities and equipment to couple to and costs as development times to minimize as well as quality inspection and maintenance expenses.