simulator n : a machine that simulates an environment for the purpose of training or research
- Italian: simulatore
A simulation is an imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviours of a selected physical or abstract system.
Historically, the word had negative connotations:
- …for Distinction Sake, a Deceiving by Words, is commonly called a Lye, and a Deceiving by Actions, Gestures, or Behavior, is called Simulation… Robert South (1643–1716)
However, the connection between simulation and dissembling later faded out and is now only of linguistic interest.
Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action.
Key issues in simulation include acquisition of valid source information about the referent, selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.
Classification and terminology
Historically, simulations used in different fields developed largely independently, but 20th century studies of Systems theory and Cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept.
Physical and interactive simulation
A computer simulation (or "sim") is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables, predictions may be made about the behaviour of the system., and human systems in economics and social science (the computational sociology) as well as in engineering to gain insight into the operation of those systems. A good example of the usefulness of using computers to simulate can be found in the field of network traffic simulation. In such simulations, the model behaviour will change each simulation according to the set of initial parameters assumed for the environment.
Traditionally, the formal modeling of systems has been via a mathematical model, which attempts to find analytical solutions enabling the prediction of the behaviour of the system from a set of parameters and initial conditions. Computer simulation is often used as an adjunct to, or substitution for, modeling systems for which simple closed form analytic solutions are not possible. There are many different types of computer simulation, the common feature they all share is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states would be prohibitive or impossible.
Several software packages exist for running computer-based simulation modeling (e.g. Monte Carlo simulation and stochastic modeling) that makes the modeling almost effortless.
Modern usage of the term "computer simulation" may encompass virtually any computer-based representation.
In Computer science, simulation has some specialized meanings: Alan Turing (1912-1954) used the term "simulation" to refer to what happens when a digital computer runs a state transition table (runs a program) that describes the state transitions, inputs and outputs of a subject discrete-state machine. The computer simulates the subject machine. Accordingly, in theoretical computer science the term simulation is a relation between state transition systems, useful in the study of operational semantics.
Less theoretically, an interesting application of computer simulation is to simulate computers using computers. In computer architecture, a type of simulator, typically called an emulator, is often used to execute a program that has to run on some inconvenient type of computer, or in a tightly controlled testing environment (see Computer architecture simulator). For example, simulators have been used to debug a microprogram or sometimes commercial application programs, before the program is downloaded to the target machine. Since the operation of the computer is simulated, all of the information about the computer's operation is directly available to the programmer, and the speed and execution of the simulation can be varied at will.
Simulators may also be used to interpret fault trees, or test VLSI logic designs before they are constructed. Symbolic simulation uses variables to stand for unknown values.
In the field of optimization, simulations of physical processes are often used in conjunction with evolutionary computation to optimize control strategies.
Simulation in education and training
Simulation is often used in the training of civilian and military personnel. This usually occurs when it is prohibitively expensive or simply too dangerous to allow trainees to use the real equipment in the real world. In such situations they will spend time learning valuable lessons in a "safe" virtual environment. Often the convenience is to permit mistakes during training for a safety-critical system.
Training simulations typically come in one of three categories:
- "live" simulation (where real people use simulated (or "dummy") equipment in the real world);
- "virtual" simulation (where real people use simulated equipment in a simulated world (or "virtual environment")), or
- "constructive" simulation (where simulated people use simulated equipment in a simulated environment). Constructive simulation is often referred to as "wargaming" since it bears some resemblance to table-top war games in which players command armies of soldiers and equipment that move around a board.
In standardized tests, "live" simulations are sometimes called "high-fidelity", producing "samples of likely performance", as opposed to "low-fidelity", "pencil-and-paper" simulations producing only "signs of possible performance", but the distinction between high, moderate and low fidelity remains relative, depending on the context of a particular comparison.
Simulations in education are somewhat like training simulations. They focus on specific tasks. The term 'microworld' is used to refer to educational simulations which model some abstract concept rather than simulating a realistic object or environment, or in some cases model a real world environment in a simplistic way so as to help a learner develop an understanding of the key concepts. Normally, a user can create some sort of constructions within the microworld which will behave in a way consistent with the concepts being modeled. Seymour Papert was one of the first to advocate the value of microworlds, and the Logo programming environment developed by Papert is one of the most famous microworlds.
Management games (or business simulations) have been finding favour in business education in recent years. Business simulations that incorporate a dynamic model enable experimentation with business strategies in a risk free environment and provide a useful extension to case study discussions.
Examples in different areas
A truck simulator provides an opportunity to reproduce the characteristics of real vehicles in a virtual environment. It replicates the external factors and conditions with which a vehicle interacts enabling a driver to feel as if they are sitting in the cab of their own vehicle. Scenarios and events are replicated with sufficient reality to ensure that drivers become fully immersed in the experience rather than simply viewing it as an educational programme.
The simulator provides a constructive experience for the novice driver and enables more complex exercises to be undertaken by the more mature driver. For novice drivers, truck simulators provide an opportunity to begin their career by applying best practice. For mature drivers, simulation provides the ability to enhance good driving or to detect poor practice and to suggest the necessary steps for remedial action. For companies, it provides an opportunity to educate staff in the driving skills that achieve reduced maintenance costs, improved productivity and, most importantly, to ensure the safety of their actions in all possible situations.
Healthcare (clinical) simulatorsMedical simulators are increasingly being developed and deployed to teach therapeutic and diagnostic procedures as well as medical concepts and decision making to personnel in the health professions. Simulators have been developed for training procedures ranging from the basics such as blood draw, to laparoscopic surgery and trauma care. They are also important to help on prototyping new devices for biomedical engineering problems. Currently, simulators are applied to research and development of tools for new therapies, treatments and early diagnosis in medicine.
Many medical simulators involve a computer connected to a plastic simulation of the relevant anatomy. Sophisticated simulators of this type employ a life size mannequin that responds to injected drugs and can be programmed to create simulations of life-threatening emergencies. In other simulations, visual components of the procedure are reproduced by computer graphics techniques, while touch-based components are reproduced by haptic feedback devices combined with physical simulation routines computed in response to the user's actions. Medical simulations of this sort will often use 3D CT or MRI scans of patient data to enhance realism. Some medical simulations are developed to be widely distributed (such as web-enabled simulations that can be viewed via standard web browsers) and can be interacted with using standard computer interfaces, such as the keyboard and mouse.
Another important medical application of a simulator — although, perhaps, denoting a slightly different meaning of simulator — is the use of a placebo drug, a formulation that simulates the active drug in trials of drug efficacy (see Placebo (origins of technical term)).
History of simulation in healthcare
The first medical simulators were simple models of human patients.
Since antiquity, these representations in clay and stone were used to demonstrate clinical features of disease states and their effects on humans. Models have been found from many cultures and continents. These models have been used in some cultures (e.g., Chinese culture) as a "diagnostic" instrument, allowing women to consult male physicians while maintaining social laws of modesty. Models are used today to help students learn the anatomy of the musculoskeletal system and organ systems.
Active modelsActive models that attempt to reproduce living anatomy or physiology are recent developments.
The famous “Harvey” mannikin was developed at the University of Miami and is able to recreate many of the physical findings of the cardiology examination, including palpation, auscultation, and electrocardiography.
Interactive modelsMore recently, interactive models have been developed that respond to actions taken by a student or physician. Until recently, these simulations were two dimensional computer programs that acted more like a textbook than a patient. Computer simulations have the advantage of allowing a student to make judgements, and also to make errors. The process of iterative learning through assessment, evaluation, decision making, and error correction creates a much stronger learning environment than passive instruction.
Computer simulatorsSimulators have been proposed as an ideal tool for assessment of students for clinical skills.
Programmed patients and simulated clinical situations, including mock disaster drills, have been used extensively for education and evaluation. These “lifelike” simulations are expensive, and lack reproducibility. A fully functional "3Pi" simulator would be the most specific tool available for teaching and measurement of clinical skills.
Such a simulator meets the goals of an objective and standardized examination for clinical competence. This system is superior to examinations that use "standard patients" because it permits the quantitative measurement of competence, as well as reproducing the same objective findings.
Classroom of the future
The "classroom of the future" will probably contain several kinds of simulators, in addition to textual and visual learning tools. This will allow students to enter the clinical years better prepared, and with a higher skill level. The advanced student or postgraduate will have a more concise and comprehensive method of retraining — or of incorporating new clinical procedures into their skill set — and regulatory bodies and medical institutions will find it easier to assess the proficiency and competency of individuals.
The classroom of the future will also form the basis of a clinical skills unit for continuing education of medical personnel; and in the same way that the use of periodic flight training assists airline pilots, this technology will assist practitioners throughout their career.
The simulator will be more than a "living" textbook, it will become an integral a part of the practice of medicine. The simulator environment will also provide a standard platform for curriculum development in institutions of medical education.
Military simulations, also known informally as war games, are models in which theories of warfare can be tested and refined without the need for actual hostilities. They exist in many different forms, with varying degrees of realism. In recent times, their scope has widened to include not only military but also political and social factors. Whilst many governments make use of simulation, both individually and collaboratively, little is known about the model's specifics outside professional circles.
In finance, computer simulations are often used for scenario planning. Risk-adjusted net present value, for example, is computed from well-defined but not always known (or fixed) inputs. By imitating the performance of the project under evaluation, simulation can provide a distribution of NPV over a range of discount rates and other variables.
City simulators / urban simulationA city simulator can be a game but can also be a tool used by urban planners to understand how cities are likely to evolve in response to various policy decisions. UrbanSim (developed at the University of Washington), ILUTE (developed at the University of Toronto) and Distrimobs (developed at the University of Bologna) are examples of modern, large-scale urban simulators designed for use by urban planners. City simulators are generally agent-based simulations with explicit representations for land use and transportation.
A flight simulator is used to train pilots on the ground. It permits a pilot to crash his simulated "aircraft" without being hurt. Flight simulators are often used to train pilots to operate aircraft in extremely hazardous situations, such as landings with no engines, or complete electrical or hydraulic failures. The most advanced simulators have high-fidelity visual systems and hydraulic motion systems. The simulator is normally cheaper to operate than a real trainer aircraft.
Home-built flight simulatorsSome people who use simulator software, especially flight simulator software, build their own simulator at home. Some people — in order to further the realism of their homemade simulator — buy used cards and racks that run the same software used by the original machine. While this involves solving the problem of matching hardware and software — and the problem that hundreds of cards plug into many different racks — many still find that solving these problems is well worthwhile. Some are so serious about realistic simulation that they will buy real aircraft parts, like complete nose sections of written-off aircraft, at aircraft boneyards. This permits people to simulate a hobby that they are unable to pursue in real life.
A robotics simulator is used to create embedded applications for a specific (or not) robot without being dependent on the 'real' robot. In some cases, these applications can be transferred to the real robot (or rebuilt) without modifications. Robotics simulators allow reproducing situations that cannot be 'created' in the real world because of cost, time, or the 'uniqueness' of a resource. A simulator also allows fast robot prototyping. Many robot simulators feature physics engines to simulate a robot's dynamics. A lot of simulators are based on open-source projects such as (Orca OpenSim) and commercial projects (Marilou Robotics Studio, Webots, Microsoft Robotics Studio, Visual Components)
Marine simulatorsBearing resemblance to flight simulators, marine simulators train ships' personnel. The most common marine simulators include:
- Ship's bridge simulators
- Engine room simulators
- Cargo handling simulators
- Communication / GMDSS simulators
Simulators like these are mostly used within maritime colleges, training institutions and navies. They often consist of a replication of a ships' bridge, with operating desk(s), and a number of screens on which the virtual surroundings are projected.
Engineering, technology or process simulation
Simulation is an important feature in engineering systems or any system that involves many processes. For example in electrical engineering, delay lines may be used to simulate propagation delay and phase shift caused by an actual transmission line. Similarly, dummy loads may be used to simulate impedance without simulating propagation, and is used in situations where propagation is unwanted. A simulator may imitate only a few of the operations and functions of the unit it simulates. Contrast with: emulate.
Most engineering simulations entail mathematical modeling and computer assisted investigation. There are many cases, however, where mathematical modeling is not reliable. Simulation of fluid dynamics problems often require both mathematical and physical simulations. In these cases the physical models require dynamic similitude. Physical and chemical simulations have also direct realistic uses, rather than research uses; in chemical engineering, for example, process simulations are used to give the process parameters immediately used for operating chemical plants, such as oil refineries.
Digital Lifecycle Simulation
Simulation solutions are being increasingly integrated with CAx (CAD, CAM, CAE....) solutions and processes. The use of simulation throughout the product lifecycle, especially at the earlier concept and design stages, has the potential of providing substantial benefits. These benefits range from direct cost issues such as reduced prototyping and shorter time-to-market, to better performing products and higher margins. However, for some companies, simulation has not provided the expected benefits.
The research firm Aberdeen Group has found that nearly all best-in-class manufacturers use simulation early in the design process as compared to 3 of 4 laggards who do not.
The successful use of Simulation, early in the lifecycle, has been largely driven by increased integration of simulation tools with the entire CAD, CAM and PLM solution-set. Simulation solutions can now function across the extended enterprise in a multi-CAD environment, and include solutions for managing simulation data and processes and ensuring that simulation results are made part of the product lifecycle history. The ability to use simulation across the entire lifecycle has been enhanced through improved user interfaces such as tailorable user interfaces and "wizards" which allow all appropriate PLM participants to take part in the simulation process.
Simulation and gamesMain article: Simulation game
Strategy games — both traditional and modern — may be viewed as simulations of abstracted decision-making for the purpose of training military and political leaders (see History of Go for an example of such a tradition, or Kriegsspiel for a more recent example).
Many other video games are simulators of some kind. Such games can simulate various aspects of reality, from business, to government, to construction, to piloting vehicles (see above).
- Comparison of racing simulators
- Experimentation in silico
- Futures studies
- Mathematical model
- Medical simulation
- Merger simulation
- Mining Simulation
- Monte Carlo simulation
- Molecular dynamics
- Network Simulator
- Pharmacokinetics Simulation
- Placebo (origins of technical term)
- Similitude (model)
- Simulated reality
- Simulation language
- Scientific modeling
- Winsberg, Eric (1999) Sanctioning Models: The epistemology of simulation, in Sismondo, Sergio and Snait Gissis (eds.) (1999), Modeling and Simulation. Special Issue of Science in Context 12.
- Winsberg, Eric (2001), “Simulations, Models and Theories: Complex Physical Systems and their Representations”, Philosophy of Science 68 (Proceedings): 442-454.
- Winsberg, Eric (2003), Simulated Experiments: Methodology for a Virtual World, Philosophy of Science 70: 105–125.
- R. Frigg and S. Hartmann, Models in Science. Entry in the Stanford Encyclopedia of Philosophy.
- S. Hartmann, The World as a Process: Simulations in the Natural and Social Sciences, in: R. Hegselmann et al. (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht: Kluwer 1996, 77–100.
- P. Humphreys, Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press, 2004.
- Roger D. Smith: Simulation Article, Encyclopedia of Computer Science, Nature Publishing Group, ISBN 0-333-77879-0.
- Roger D. Smith: "Simulation: The Engine Behind the Virtual World", eMatter, December, 1999.
- Aldrich, C. (2003). Learning by Doing : A Comprehensive Guide to Simulations, Computer Games, and Pedagogy in e-Learning and Other Educational Experiences. San Francisco: Pfeifer — John Wiley & Sons.
- Aldrich, C. (2004). Simulations and the future of learning: an innovative (and perhaps revolutionary) approach to e-learning. San Francisco: Pfeifer — John Wiley & Sons.
- Percival, F., Lodge, S., Saunders, D. (1993). The Simulation and Gaming Yearbook: Developing Transferable Skills in Education and Training. London: Kogan Page.
- South, R., "A Sermon Delivered at Christ-Church, Oxon., Before the University, Octob. 14. 1688: Prov. XII.22 Lying Lips are abomination to the Lord", pp.519–657 in South, R., Twelve Sermons Preached Upon Several Occasions (Second Edition), Volume I, Printed by S.D. for Thomas Bennet, (London), 1697.
- Of Simulation and Dissimulation An essay by Francis Bacon.
- Wolfe, Joseph & Crookall, David, (1998). Developing a scientific knowledge of simulation/gaming . Simulation & Gaming: An International Journal of Theory, Design and Research, 29(1), 7–19.
- Bibliographies containing more references to be found on the website of the journal Simulation & Gaming.
- Cohen, Steve (2006). Virtual Decisions. Mahwah, New Jersey: Lawrence Erlbaum Associates.
- Hertel, J.P. (2002). Using Simulations to Promote Learning in Higher Education. Sterling, Virginia: Stylus.
- Saunders, D (Ed.). (2000). The International Simulation and Gaming Research Yearbook, volume 8. London: Kogan Page Limited.
- EUROSIM — Federation of European Simulation Societies
- Institute for Simulation and Training, University of Central Florida
- National Center for Simulation
- Simulation Interoperability Standards Organization
- The Society for Modeling and Simulation International (Formerly the Society of Computer Simulation)
- United States Defense Modeling and Simulation Office
- Worldwide Simulation Organizations List
- Winter Simulation Conference
- See list of organisations provided by the journal S&G.
- ACM special interest group on simulation.
- Liophant Simulation.
- Simulation Team - University of Genoa.
- Simulated Medical Procedures
- Nuclear Reactor Simulation - Includes the PC-based Boiling Water Reactor Simulator Program.
- IMTEK Mathematica Supplement (IMS) for open source simulation lectures and packages.
- Simulation — An Enabling Technology in Software Engineering
- Simulation Education
- Worldwide Simulation Course List
- Clinical Training and Education Centre, University of Western Australia
- Multi-Paradigm Simulation Training
- McLeod Institute of Simulation Science
- The NSDL Scout Report - NSF
- Interactive Java Pressure Simulation Applet
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