Editing Simulation (section)

==More examples of simulation==
===Automobiles===
{{Unreferenced section|date=September 2021}}
{{main|Driving simulator}}
An automobile 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 experience.

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.
<gallery mode="packed" heights="150px">
File:Car racing simulator - SBR Racing, Construma, 2015.04.17.JPG|Car racing simulator
File:Vehicle simulator.jpg|right|A soldier tests out a heavy-wheeled-vehicle driving simulator.
</gallery>

===Biomechanics===
{{Unreferenced section|date=September 2021}}
A '''biomechanics simulator'''<!--boldface per [[WP:R#PLA]]--> is a simulation platform for creating dynamic mechanical models built from combinations of rigid and deformable bodies, joints, constraints, and various force actuators. It is specialized for creating biomechanical models of human anatomical structures, with the intention to study their function and eventually assist in the design and planning of medical treatment.

A biomechanics simulator is used to analyze walking dynamics, study sports performance, simulate surgical procedures, analyze joint loads, design medical devices, and animate human and animal movement.

A neuromechanical simulator that combines biomechanical and biologically realistic neural network simulation. It allows the user to test hypotheses on the neural basis of behavior in a physically accurate 3-D virtual environment.

===City and urban===
{{further|Traffic simulation}}
{{Unreferenced section|date=September 2021}}
A city simulator can be a [[city-building 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. [[AnyLogic]] is an example of modern, large-scale urban simulators designed for use by urban planners. City simulators are generally [[Agent (economics)|agent]]-based simulations with explicit representations for [[land use]] and transportation. [[UrbanSim]] and [[Land Use Evolution and Impact Assessment Model|LEAM]] are examples of large-scale urban simulation models that are used by metropolitan planning agencies and military bases for land use and [[transportation planning]].

===Christmas===
{{Further|NORAD Tracks Santa|Google Santa Tracker|emailSanta.com}}
Several Christmas-themed simulations exist, many of which are centred around [[Santa Claus]]. An example of these simulations are websites which claim to allow the user to track Santa Claus. Due to the fact that Santa is a [[legend]]ary character and not a real, living person, it is impossible to provide actual information on his location, and services such as [[NORAD Tracks Santa]] and the [[Google Santa Tracker]] (the former of which claims to use [[radar]] and other technologies to track Santa)<ref>{{cite web|last=Grush|first=Loren|date=24 December 2016|title=The technologies NORAD is 'using' to track Santa Claus today|url=https://www.theverge.com/2016/12/24/14044412/norad-santa-tracker-christmas-eve-sbirs-satellites-jets-radar|access-date=14 November 2020|website=The Verge|language=en}}</ref> display fake, predetermined location information to users. Another example of these simulations are websites that claim to allow the user to email or send messages to Santa Claus. Websites such as [[emailSanta.com]] or Santa's former page on the now-defunct [[Windows Live Spaces]] by [[Microsoft]] use automated [[Computer program|programs]] or scripts to generate personalized replies claimed to be from Santa himself based on user input.<ref>{{cite web|last=Ribeiro|first=Ricky|date=19 December 2012|title=EmailSanta.com: How Santa Claus Went Digital|url=https://biztechmagazine.com/article/2012/12/emailsantacom-how-santa-claus-went-digital|access-date=19 July 2020|website=BizTech Magazine|language=en|quote="It now offers kids and parents personalized messages from Santa, which run from an ASP script that Kerr built himself."}}</ref><ref>{{cite web|last=Vnuk|first=Helen|date=7 December 2017|title=Email Santa and get a reply: the website making my kids believe.|url=https://www.mamamia.com.au/email-santa-and-get-a-reply/|access-date=19 July 2020|website=MamaMia.com.au/|language=en|quote="The one thing that's convinced my daughter, more than anything else, that Santa is real is a website, emailSanta.com."}}</ref><ref name="Microsoft pulls plug on potty-mouth Santa, by John Fontana, 4 Dec 20072">{{cite web|title=Microsoft pulls plug on potty-mouth Santa, by John Fontana, 4 Dec 2007|url=http://www.networkworld.com/news/2007/120407-microsoft-santa-bot.html|url-status=dead|archive-url=https://web.archive.org/web/20121013161007/http://www.networkworld.com/news/2007/120407-microsoft-santa-bot.html|archive-date=13 October 2012|access-date=9 December 2010|publisher=Network World}}</ref><ref name="For a Jolly Good Time, Chat With Santa on Windows Live Messenger, 13 Dec 20062">{{cite web|title=For a Jolly Good Time, Chat With Santa on Windows Live Messenger, 13 Dec 2006|url=https://www.microsoft.com/presspass/press/2006/dec06/12-13SantaIMPR.mspx|url-status=dead|archive-url=https://web.archive.org/web/20071024145241/https://www.microsoft.com/presspass/press/2006/dec06/12-13SantaIMPR.mspx|archive-date=24 October 2007|access-date=9 December 2010|publisher=Microsoft}}</ref>

===Classroom of the future===
{{Unreferenced section|date=September 2021}}
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 [[Competence (human resources)|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.{{Citation needed|date=November 2007}}

The simulator will be more than a "living" textbook, it will become an integral a part of the practice of medicine.{{Citation needed|date=November 2007}} The simulator environment will also provide a standard platform for curriculum development in institutions of medical education.

===Communication satellites===
{{Unreferenced section|date=September 2021}}
Modern satellite communications systems ([[Satcom (satellite)|SATCOM]]) are often large and complex with many interacting parts and elements. In addition, the need for broadband connectivity on a moving vehicle has increased dramatically in the past few years for both commercial and military applications. To accurately predict and deliver high quality of service, SATCOM system designers have to factor in terrain as well as atmospheric and meteorological conditions in their planning. To deal with such complexity, system designers and operators increasingly turn towards computer models of their systems to simulate real-world operating conditions and gain insights into usability and requirements prior to final product sign-off. Modeling improves the understanding of the system by enabling the SATCOM system designer or planner to simulate real-world performance by injecting the models with multiple hypothetical atmospheric and environmental conditions. 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 yet living a lifelike experience (or at least it is the goal). Often the convenience is to permit mistakes during training for a safety-critical system.

===Digital lifecycle===
{{Unreferenced section|date=September 2021}}
[[File:ugs-nx-5-engine-airflow-simulation.jpg|thumb|right|Simulation of airflow over an engine]]

Simulation solutions are being increasingly integrated with [[computer-aided]] solutions and processes ([[computer-aided design]] or CAD, [[computer-aided manufacturing]] or CAM, [[computer-aided engineering]] or CAE, etc.). 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 successful use of simulation, early in the lifecycle, has been largely driven by increased integration of simulation tools with the entire set of CAD, CAM and product-lifecycle management solutions. Simulation solutions can now function across the extended enterprise in a [[CAD data exchange|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.

===Disaster preparedness===
{{main|Emergency management}}
Simulation training has become a method for preparing people for disasters. Simulations can replicate emergency situations and track how learners respond thanks to a lifelike experience. Disaster preparedness simulations can involve training on how to handle [[terrorism]] attacks, natural disasters, [[pandemic]] outbreaks, or other life-threatening emergencies.

One organization that has used simulation training for disaster preparedness is CADE (Center for Advancement of Distance Education). CADE<ref>{{Cite web |url=http://www.uic.edu/sph/cade/ |title=CADE |access-date=26 August 2009 |archive-date=7 September 2009 |archive-url=https://web.archive.org/web/20090907080719/http://www.uic.edu/sph/cade/ |url-status=live }}</ref> has used a video game to prepare emergency workers for multiple types of attacks. As reported by News-Medical.Net, "The video game is the first in a series of simulations to address bioterrorism, pandemic flu, smallpox, and other disasters that emergency personnel must prepare for.<ref>News-Medical.: [http://www.news-medical.net/news/2005/10/27/14106.aspx "Net article-."] {{Webarchive|url=https://web.archive.org/web/20121005075209/http://www.news-medical.net/news/2005/10/27/14106.aspx |date=5 October 2012 }}</ref>" Developed by a team from the [[University of Illinois at Chicago]] (UIC), the game allows learners to practice their emergency skills in a safe, controlled environment.

The Emergency Simulation Program (ESP) at the British Columbia Institute of Technology (BCIT), Vancouver, British Columbia, Canada is another example of an organization that uses simulation to train for emergency situations. ESP uses simulation to train on the following situations: forest fire fighting, oil or chemical spill response, earthquake response, law enforcement, municipal firefighting, hazardous material handling, military training, and response to terrorist attack<ref name="straylightmm.com">{{cite web |url=http://www.straylightmm.com/ |title=Emergency Response Training<!-- Bot generated title --> |access-date=24 June 2009 |archive-url=https://web.archive.org/web/20030312001333/http://www.straylightmm.com/ |archive-date=12 March 2003 |url-status=dead}}</ref> One feature of the simulation system is the implementation of "Dynamic Run-Time Clock," which allows simulations to run a 'simulated' time frame, "'speeding up' or 'slowing down' time as desired"<ref name="straylightmm.com"/> Additionally, the system allows session recordings, picture-icon based navigation, file storage of individual simulations, multimedia components, and launch external applications.

At the University of Québec in Chicoutimi, a research team at the outdoor research and expertise laboratory (Laboratoire d'Expertise et de Recherche en Plein Air – LERPA) specializes in using wilderness backcountry accident simulations to verify emergency response coordination.

Instructionally, the benefits of emergency training through simulations are that learner performance can be tracked through the system. This allows the developer to make adjustments as necessary or alert the educator on topics that may require additional attention. Other advantages are that the learner can be guided or trained on how to respond appropriately before continuing to the next emergency segment—this is an aspect that may not be available in the live environment. Some emergency training simulators also allow for immediate feedback, while other simulations may provide a summary and instruct the learner to engage in the learning topic again.

In a live-emergency situation, emergency responders do not have time to waste. Simulation-training in this environment provides an opportunity for learners to gather as much information as they can and practice their knowledge in a safe environment. They can make mistakes without risk of endangering lives and be given the opportunity to correct their errors to prepare for the real-life emergency.

===Economics===
{{Unreferenced section|date=September 2021}}
'''Simulations in economics'''<!--boldface per [[WP:R#PLA]]--> and especially in [[macroeconomics]], judge the desirability of the effects of proposed policy actions, such as [[fiscal policy]] changes or [[monetary policy]] changes. A mathematical model of the economy, having been fitted to historical economic data, is used as a proxy for the actual economy; proposed values of [[government spending]], taxation, [[open market operations]], etc. are used as inputs to the simulation of the model, and various variables of interest such as the [[inflation rate]], the [[unemployment rate]], the [[balance of trade]] deficit, the government [[budget deficit]], etc. are the outputs of the simulation. The simulated values of these variables of interest are compared for different proposed policy inputs to determine which set of outcomes is most desirable.<ref>{{Cite web|url=https://www.imf.org/external/pubs/ft/fandd/basics/models.htm|title=Finance & Development}}</ref>

===Engineering, technology, and processes===
<!--If the above heading is changed, update #links in other articles that point to it, per [[MOS:HEAD]]-->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 (waves)#Phase shift|phase shift]] caused by an actual [[transmission line]]. Similarly, [[dummy load]]s may be used to simulate [[Electrical impedance|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'': [[emulator|emulate]].<ref name="FS1037C">[[Federal Standard 1037C]]</ref>

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 [[similitude (model)|dynamic similitude]]. Physical and chemical simulations have also direct realistic uses, rather than research uses; in [[chemical engineering]], for example, [[process simulation]]s are used to give the process parameters immediately used for operating chemical plants, such as oil refineries. Simulators are also used for plant operator training. It is called Operator Training Simulator (OTS) and has been widely adopted by many industries from chemical to oil&gas and to the power industry. This created a safe and realistic virtual environment to train board operators and engineers. [[MiMiC Simulation Software|Mimic]] is capable of providing high fidelity dynamic models of nearly all chemical plants for operator training and control system testing.

===Ergonomics===
'''Ergonomic simulation'''<!--boldface per [[WP:R#PLA]]--> involves the analysis of virtual products or manual tasks within a virtual environment. In the engineering process, the aim of ergonomics is to develop and to improve the design of products and work environments.<ref name="Reed, M. P. 2006">Reed, M. P., Faraway, J., Chaffin, D. B., & Martin, B. J. (2006). The HUMOSIM Ergonomics Framework: A new approach to digital human simulation for ergonomic analysis. SAE Technical Paper, 01-2365</ref> Ergonomic simulation utilizes an anthropometric virtual representation of the human, commonly referenced as a mannequin or Digital Human Models (DHMs), to mimic the postures, mechanical loads, and performance of a human operator in a simulated environment such as an airplane, automobile, or manufacturing facility. DHMs are recognized as evolving and valuable tool for performing proactive ergonomics analysis and design.<ref>Chaffin, D. B. (2007). Human motion simulation for vehicle and workplace design. Human Factors and Ergonomics in Manufacturing & Service Industries,17(5), 475–484</ref> The simulations employ 3D-graphics and physics-based models to animate the virtual humans. Ergonomics software uses inverse kinematics (IK) capability for posing the DHMs.<ref name="Reed, M. P. 2006"/>

Software tools typically calculate biomechanical properties including individual muscle forces, joint forces and moments. Most of these tools employ standard ergonomic evaluation methods such as the NIOSH lifting equation and Rapid Upper Limb Assessment (RULA). Some simulations also analyze physiological measures including metabolism, energy expenditure, and fatigue limits Cycle time studies, design and process validation, user comfort, reachability, and line of sight are other human-factors that may be examined in ergonomic simulation packages.<ref>{{cite web|url=http://www.plm.automation.siemens.com/en_us/products/tecnomatix/assembly_planning/jack/index.shtml|title=Jack and Process Simulate Human|work=Siemens PLM Software|url-status=dead|archive-url=https://web.archive.org/web/20130508230912/http://www.plm.automation.siemens.com/en_us/products/tecnomatix/assembly_planning/jack/index.shtml|archive-date=8 May 2013}}</ref>

Modeling and simulation of a task can be performed by manually manipulating the virtual human in the simulated environment. Some ergonomics [[simulation software]] permits interactive, [[real-time simulation]] and evaluation through actual human input via motion capture technologies. However, motion capture for ergonomics requires expensive equipment and the creation of props to represent the environment or product.

Some applications of ergonomic simulation in include analysis of solid waste collection, disaster management tasks, interactive gaming,<ref>Bush, P. M., Gaines, S., Gammoh, F., & Wooden, S. A Comparison of Software Tools for Occupational Biomechanics and Ergonomic Research.</ref> automotive assembly line,<ref>Niu, J. W., Zhang, X. W., Zhang, X., & Ran, L. H. (December 2010). Investigation of ergonomics in automotive assembly line using Jack. industrial Engineering and Engineering Management (IEEM), 2010 IEEE International Conference on (pp. 1381–1385). IEEE.</ref> virtual prototyping of rehabilitation aids,<ref>Beitler, Matthew T., Harwin, William S., & Mahoney, Richard M. (1996) In Proceedings of the virtual prototyping of rehabilitation aids, RESNA 96, pp. 360–363.</ref> and aerospace product design.<ref>G.R. Bennett. The application of virtual prototyping in the development of complex aerospace products. Virtual Prototyping Journal, 1 (1) (1996), pp. 13–20</ref> Ford engineers use ergonomics simulation software to perform virtual product design reviews. Using engineering data, the simulations assist evaluation of assembly ergonomics. The company uses Siemen's Jack and Jill ergonomics simulation software in improving worker safety and efficiency, without the need to build expensive prototypes.<ref>{{Cite web |url=http://blog.industrysoftware.automation.siemens.com/blog/2012/03/21/floor-2012-chicago-auto-show-automation-world-shows-ford-power-simulation/ |title=From the floor of the 2012 Chicago Auto Show: Automation World shows how Ford uses the power of simulation « Siemens PLM Software Blog<!-- Bot generated title --> |access-date=6 December 2012 |archive-date=24 April 2012 |archive-url=https://web.archive.org/web/20120424104132/http://blog.industrysoftware.automation.siemens.com/blog/2012/03/21/floor-2012-chicago-auto-show-automation-world-shows-ford-power-simulation/ |url-status=live }}</ref>

===Finance===
{{main|Monte Carlo methods in finance|Mathematical finance}}
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 [[discounts and allowances|discount rates]] and other variables. Simulations are also often used to test a financial theory or the ability of a financial model.<ref>{{cite journal|last1=French|first1=Jordan|title=The one: A simulation of CAPM market returns|journal=The Journal of Wealth Management|volume=20|issue=1|pages=126–147|doi=10.3905/jwm.2017.20.1.126|year=2017|doi-access=free}}</ref>

Simulations are frequently used in financial training to engage participants in experiencing various historical as well as fictional situations. There are stock market simulations, portfolio simulations, risk management simulations or models and forex simulations. Such simulations are typically based on [[stochastic asset model]]s. Using these simulations in a training program allows for the application of theory into a something akin to real life. As with other industries, the use of simulations can be technology or case-study driven.

===Flight===
{{main|Flight simulator}}

[[File:USMC-02023.jpg|thumb|A military flight simulator]]

Flight simulation is mainly used to train pilots outside of the aircraft.<ref name="FAR121">{{cite web|url = https://www.ecfr.gov/current/title-14/part-121/subpart-n |title = FAR 121 Subpart N—Training Program|access-date = 28 April 2013|last = Federal Aviation Administration|author-link = Federal Aviation Administration|date = 25 April 2013}}</ref> In comparison to training in flight, simulation-based training allows for practicing maneuvers or situations that may be impractical (or even dangerous) to perform in the aircraft while keeping the pilot and instructor in a relatively low-risk environment on the ground. For example, electrical system failures, instrument failures, hydraulic system failures, and even flight control failures can be simulated without risk to the crew or equipment.<ref name="allertonCaseFlightSimulation2002">{{cite Q|Q112813480}}</ref>

Instructors can also provide students with a higher concentration of training tasks in a given period of time than is usually possible in the aircraft. For example, conducting multiple [[instrument approach]]es in the actual aircraft may require significant time spent repositioning the aircraft, while in a simulation, as soon as one approach has been completed, the instructor can immediately reposition the simulated aircraft to a location from which the next approach can be begun.

Flight simulation also provides an economic advantage over training in an actual aircraft. Once fuel, maintenance, and insurance costs are taken into account, the operating costs of an FSTD are usually substantially lower than the operating costs of the simulated aircraft. For some large transport category airplanes, the operating costs may be several times lower for the FSTD than the actual aircraft. Another advantage is reduced environmental impact, as simulators don't contribute directly to carbon or noise emissions.<ref>{{cite web|title=Advantages of Simulators (FSTDs) in Helicopter Flight Training|author=European Helicopter Safety Team (EHEST)|publisher=European Union Aviation Safety Agency (EASA)|url=https://www.easa.europa.eu/sites/default/files/dfu/HE-6-Advantages-of-simulators-in-Helicopter-Flight-Training-final.pdf |archive-url=https://ghostarchive.org/archive/20221010/https://www.easa.europa.eu/sites/default/files/dfu/HE-6-Advantages-of-simulators-in-Helicopter-Flight-Training-final.pdf |archive-date=2022-10-10 |url-status=live|access-date=2022-06-29|page=6}}</ref>

There also exist "engineering flight simulators" which are a key element of the [[aircraft design process]].<ref name="allertonImpactFlightSimulation2010">{{cite Q|Q112813532}}</ref> Many benefits that come from a lower number of test flights like cost and safety improvements are described above, but there are some unique advantages. Having a simulator available allows for faster design iteration cycle or using more test equipment than could be fit into a real aircraft.<ref name="allertonPrinciplesFlightSimulation2009">{{cite Q|Q112813340|p=13}}</ref>

===Marine===
{{main|Maritime simulator}}
[[File:Szczecin Akademia Morska symulator mostka.jpg|thumb|A ship bridge simulator]]
Bearing resemblance to [[flight simulator]]s, a '''marine simulator''' is meant for training of ship personnel. The most common marine simulators include:<ref name="coastguardGuidelinesSimulatorbasedMarine1985">{{Citation | author1 = ((United States Maritime Administration Office of Shipbuilding, Operations, and Research))| author2 = ((United States Coast Guard Office of Research and Development)) | title = Guidelines for Simulator-based Marine Pilot Training Programs| accessdate = 2022-07-01| date = March 1985| url = https://apps.dtic.mil/sti/pdfs/ADA159765.pdf}}</ref>
* Ship's bridge simulators
* Engine room simulators<ref name="tsoukalasMarineEngineersTraining2008">{{Cite journal| doi = 10.1007/BF03195143| issn = 1654-1642| volume = 7| issue = 2| pages = 429–448| last1 = Tsoukalas| first1 = Vasilios D.| last2 = Papachristos| first2 = Dimitrios A.| last3 = Tsoumas| first3 = Nikolaos K.| last4 = Mattheu| first4 = Elisabeth C.| title = Marine engineers' training: Educational assessment for an engine room simulator| journal = WMU Journal of Maritime Affairs| accessdate = 2022-07-01| date = 2008-10-01| s2cid = 110790495| url = https://doi.org/10.1007/BF03195143}}</ref>
* Cargo handling simulators
* Communication / [[GMDSS]] simulators
* ROV 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 the operating console(s), and a number of screens on which the virtual surroundings are projected.

===Military===
{{main|Military simulation}}
[[File:Гранатометчик тренируется с помощью компьютерного тренажера.jpg|thumb|The grenade launcher trains using a computer simulator]]
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 (for example, the [[Nationlab]] series of strategic exercises in Latin America).<ref>[[The Economist]] provides a current (as of 2012) survey of public projects attempting to simulate some theories in [http://www.economist.com/node/21553006 "The science of civil war: What makes heroic strife"] {{Webarchive|url=https://web.archive.org/web/20221102051751/https://www.economist.com/science-and-technology/2012/04/21/what-makes-heroic-strife |date=2 November 2022 }}.</ref> While many governments make use of simulation, both individually and collaboratively, little is known about the model's specifics outside professional circles.

===Network and distributed systems===
Network and distributed systems have been extensively simulated in other to understand the impact of new protocols and algorithms before their deployment in the actual systems. The simulation can focus on different levels ([[physical layer]], [[network layer]], [[application layer]]), and evaluate different metrics (network bandwidth, resource consumption, service time, dropped packets, system availability). Examples of simulation scenarios of network and distributed systems are:

* [[Content delivery network]]s<ref>{{cite journal|last1=Filelis-Papadopoulos|first1=Christos K.|last2=Endo|first2=Patricia Takako|last3=Bendechache|first3=Malika|last4=Svorobej|first4=Sergej|last5=Giannoutakis|first5=Konstantinos M.|last6=Gravvanis|first6=George A.|last7=Tzovaras|first7=Dimitrios|last8=Byrne|first8=James|last9=Lynn|first9=Theo|date=1 January 2020|title=Towards simulation and optimization of cache placement on large virtual content distribution networks|journal=Journal of Computational Science|volume=39|pages=101052|doi=10.1016/j.jocs.2019.101052|issn=1877-7503|doi-access=free}}</ref><ref>{{cite journal|last1=Filelis-Papadopoulos|first1=Christos K.|last2=Giannoutakis|first2=Konstantinos M.|last3=Gravvanis|first3=George A.|last4=Endo|first4=Patricia Takako|last5=Tzovaras|first5=Dimitrios|last6=Svorobej|first6=Sergej|last7=Lynn|first7=Theo|date=1 April 2019|title=Simulating large vCDN networks: A parallel approach|journal=Simulation Modelling Practice and Theory|volume=92|pages=100–114|doi=10.1016/j.simpat.2019.01.001|s2cid=67752426|issn=1569-190X}}</ref><ref>{{cite journal|last1=Ibn-Khedher|first1=Hatem|last2=Abd-Elrahman|first2=Emad|last3=Kamal|first3=Ahmed E.|last4=Afifi|first4=Hossam|date=19 June 2017|title=OPAC: An optimal placement algorithm for virtual CDN|journal=Computer Networks|volume=120|pages=12–27|doi=10.1016/j.comnet.2017.04.009|issn=1389-1286}}</ref><ref>{{cite book|last1=Khedher|first1=Hatem|last2=Abd-Elrahman|first2=Emad|last3=Afifi|first3=Hossam|last4=Marot|first4=Michel|title=2017 IEEE 42nd Conference on Local Computer Networks (LCN) |chapter=Optimal and Cost Efficient Algorithm for Virtual CDN Orchestration |date=2017|location=Singapore|publisher=IEEE|pages=61–69|doi=10.1109/LCN.2017.115|isbn=978-1-5090-6523-3|s2cid=44243386}}</ref>
* Smart cities
* Internet of things

===Payment and securities settlement system===
Simulation techniques have also been applied to payment and securities settlement systems. Among the main users are central banks who are generally responsible for the oversight of market infrastructure and entitled to contribute to the smooth functioning of the payment systems.

Central banks have been using payment system simulations to evaluate things such as the adequacy or sufficiency of liquidity available ( in the form of account balances and intraday credit limits) to participants (mainly banks) to allow efficient settlement of payments.<ref>Leinonen (ed.): Simulation studies of liquidity needs, risks and efficiency in payment networks (Bank of Finland Studies E:39/2007) [http://pss.bof.fi/Pages/Publications.aspx Simulation publications] {{Webarchive|url=https://archive.today/20120714010331/http://pss.bof.fi/Pages/Publications.aspx |date=14 July 2012 }}</ref><ref>Neville Arjani: Examining the Trade-Off between Settlement Delay and Intraday Liquidity in Canada's LVTS: A Simulation Approach (Working Paper 2006–20, Bank of Canada) [http://pss.bof.fi/Pages/Publications.aspx Simulation publications] {{Webarchive|url=https://archive.today/20120714010331/http://pss.bof.fi/Pages/Publications.aspx |date=14 July 2012 }}</ref> The need for liquidity is also dependent on the availability and the type of netting procedures in the systems, thus some of the studies have a focus on system comparisons.<ref>Johnson, K.; McAndrews, J.; Soramäki, K. 'Economizing on Liquidity with Deferred Settlement Mechanisms' (Reserve Bank of New York Economic Policy Review, December 2004)</ref>

Another application is to evaluate risks related to events such as communication network breakdowns or the inability of participants to send payments (e.g. in case of possible bank failure).<ref>H. Leinonen (ed.): Simulation analyses and stress testing of payment networks (Bank of Finland Studies E:42/2009) [http://pss.bof.fi/Pages/Publications.aspx Simulation publications] {{Webarchive|url=https://archive.today/20120714010331/http://pss.bof.fi/Pages/Publications.aspx |date=14 July 2012 }}</ref> This kind of analysis falls under the concepts of [[stress testing]] or [[scenario analysis]].

A common way to conduct these simulations is to replicate the settlement logics of the real payment or securities settlement systems under analysis and then use real observed payment data. In case of system comparison or system development, naturally, also the other settlement logics need to be implemented.

To perform stress testing and scenario analysis, the observed data needs to be altered, e.g. some payments delayed or removed. To analyze the levels of liquidity, initial liquidity levels are varied. System comparisons (benchmarking) or evaluations of new netting algorithms or rules are performed by running simulations with a fixed set of data and varying only the system setups.

An inference is usually done by comparing the benchmark simulation results to the results of altered simulation setups by comparing indicators such as unsettled transactions or settlement delays.