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(computer science)
The development and use of computer models for the study of actual or postulated dynamic systems.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.


The process of representing or modeling a situation.
Illustrated Dictionary of Architecture Copyright © 2012, 2002, 1998 by The McGraw-Hill Companies, Inc. All rights reserved


the electronic copying or modelling of unique objects. The most famous sociological application of the term is by BAUDRILLARD (1983). He contends that contemporary society is so thoroughly saturated with electronic models and versions of unique objects that distinctions between reality and fiction are no longer valid. See also AURA, HYPERREALITY.
Collins Dictionary of Sociology, 3rd ed. © HarperCollins Publishers 2000


A broad collection of methods used to study and analyze the behavior and performance of actual or theoretical systems. Simulation studies are performed, not on the real-world system, but on a (usually computer-based) model of the system created for the purpose of studying certain system dynamics and characteristics. The purpose of any model is to enable its users to draw conclusions about the real system by studying and analyzing the model. The major reasons for developing a model, as opposed to analyzing the real system, include economics, unavailability of a “real” system, and the goal of achieving a deeper understanding of the relationships between the elements of the system.

Simulation can be used in task or situational training areas in order to allow humans to anticipate certain situations and be able to react properly; decision-making environments to test and select alternatives based on some criteria; scientific research contexts to analyze and interpret data; and understanding and behavior prediction of natural systems, such as in studies of stellar evolution or atmospheric conditions.

With simulation a decision maker can try out new designs, layouts, software programs, and systems before committing resources to their acquisition or implementation; test why certain phenomena occur in the operations of the system under consideration; compress and expand time; gain insight about which variables are most important to performance and how these variables interact; identify bottlenecks in material, information, and product flow; better understand how the system really operates (as opposed to how everyone thinks it operates); and compare alternatives and reduce the risks of decisions.

The word “system” refers to a set of elements (objects) interconnected so as to aid in driving toward a desired goal. This definition has two connotations: First, a system is made of parts (elements) that have relationships between them (or processes that link them together). These relationships or processes can range from relatively simple to extremely complex. One of the necessary requirements for creating a “valid” model of a system is to capture, in as much detail as possible, the nature of these interrelationships. Second, a system constantly seeks to be improved. Feedback (output) from the system must be used to measure the performance of the system against its desired goal. Both of these elements are important in simulation. See Systems engineering

Systems can be classified in three major ways. They may be deterministic or stochastic (depending on the types of elements that exist in the system), discrete-event or continuous (depending on the nature of time and how the system state changes in relation to time), and static or dynamic (depending on whether or not the system changes over time at all). This categorization affects the type of modeling that is done and the types of simulation tools that are used.

Models, like the systems they represent, can be static or dynamic, discrete or continuous, and deterministic or stochastic. Simulation models are composed of mathematical and logical relations that are analyzed by numerical methods rather than analytical methods. Numerical methods employ computational procedures to run the model and generate an artificial history of the system. Observations from the model runs are collected, analyzed, and used to estimate the true system performance measures. See Model theory

There is no single prescribed methodology in which simulation studies are conducted. Most simulation stuides proceed around four major areas: formulating the problem, developing the model, running the model, and analyzing the output. Statistical inference methods allow the comparison of various competing system designs or alternatives. For example, estimation and hypothesis testing make it possible to discuss the outputs of the simulation and compare the system metrics.

Many of the applications of simulation are in the area of manufacturing and material handling systems. Simulation is taught in many engineering and business curricula with the focus of the applications also being on manufacturing systems. The characteristics of these systems, such as physical layout, labor and resource utilization, equipment usage, products, and supplies, are extremely amenable to simulation modeling methods. See Computer-integrated manufacturing, Flexible manufacturing system

McGraw-Hill Concise Encyclopedia of Engineering. © 2002 by The McGraw-Hill Companies, Inc.


(simulation, system)
Attempting to predict aspects of the behaviour of some system by creating an approximate (mathematical) model of it. This can be done by physical modelling, by writing a special-purpose computer program or using a more general simulation package, probably still aimed at a particular kind of simulation (e.g. structural engineering, fluid flow). Typical examples are aircraft flight simlators or electronic circuit simulators. A great many simulation languages exist, e.g. Simula.

See also emulation, Markov chain.

Usenet newsgroup: news:comp.simulation.
This article is provided by FOLDOC - Free Online Dictionary of Computing (


The mathematical representation of the interaction of real-world objects. See scientific application and simulator.
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References in periodicals archive ?
The quantities of detected faults have been calculated for each one hundred of test patterns, when the fault simulation was carried out for all test sequences and test patterns individually.
What's Wrong with Social Simulations? ECKHART ARNOLD
The Appendix includes a well-established collection of 'Simulation Resources' for equation-based modeling, molecular dynamics, epidemiological modeling, agent-based modeling, system dynamics, cellular modeling and simulation, as well as systems modeling and design.
The Sidra simulation team has also been instrumental in forming the Qatar Simulation Consortium, a group of simulation professionals from Sidra, HMC, the University of Calgary-Qatar, and College of the North Atlantic-Qatar.
The use of simulation has allowed the easy progression to teaching integrative assessment skills to second-year students at Whitireia.
"Users do not need a simulation background to take advantage of Autodesk Simulation DFM -- the software is highly intuitive and easily integrates into any design workflow."
To the best of our knowledge, none of the current works on the MDE practices in the simulation field, addresses the issue of the Simulation Platform Description Model (SPDM), i.e., the description of simulation platforms that support the execution of simulation experiments.
From the perspective of simulated patients not being allowed to die, goals of the simulation are to assess how teams manage and intervene during a learning scenario.
Discovery Live offers some multi-physics simulation, allows input and change of initial boundary conditions such as internal or external flow, and defining inlets and outlets.
Making use of external interface of Fluent and Simulink, the following three approaches can achieve Simulink/Fluent collaborative simulation: Fluent is embedded in Simulink [4, 5]; that is, Fluent simulation model is encapsulated to a simulation module which has the same attribute and operation style with the build-in module and participates in the running of the Simulink simulation model; Simulink is embedded in Fluent [1]; that is, Simulink simulation model is compiled into C function for Fluent/UDF to call; Simulink and Fluent run parallelly that is to achieve simulation collaboration through the global variate [1] or a kind of process blocking technology [2, 3].
During this two-day workshop, NLN simulation educators will meet with faculty and/or simulation staff to a) discuss the target program of study for thoughtful simulation integration into an existing curriculum map; b) identify scenarios that can be mapped to course, lab, and/or clinical objectives based on goals and initiatives established for the program; and c) outline a staffing plan to meet the designed simulation program blueprint.
Simulation is defined as "the artificial representation of a phenomenon or activity that allows participants to experience a realistic situation without real-world risks".

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