Modeling and simulation

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Modelling and simulation (M&S) is getting information about how something will behave without actually testing it in real life. For instance, if we wanted to design a race car, but weren't sure what type of spoiler would improve traction the most, we would be able to use a computer simulation of the car to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. We're getting useful insights about different decisions we could make for the car without actually building the car.

More generally, M&S is using models, including emulators, prototypes and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably.[1]

The use of M&S within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M&S has already helped to reduce costs, increase the quality of products and systems, and document and archive lessons learned.

M&S is a discipline on its own. Its many application domains often lead to the assumption that M&S is pure application. This is not the case and needs to be recognized by engineering management experts who want to use M&S. To ensure that the results of simulation are applicable to the real world, the engineering manager must understand the assumptions, conceptualizations, and implementation constraints of this emerging field.

Interest in simulations[edit]

Technically, simulation is well accepted. The 2006 National Science Foundation (NSF) Report on “Simulation-based Engineering Science”[2] showed the potential of using simulation technology and methods to revolutionize the engineering science. Among the reasons for the steadily increasing interest in simulation applications are the following:

  1. Using simulations is generally cheaper and safer than conducting experiments with a prototype of the final product. One of the biggest computers worldwide is currently designed in order to simulate the detonation of nuclear devices and their effects in order to support better preparedness in the event of a nuclear explosion. Similar efforts are conducted to simulate hurricanes and other natural catastrophes.
  2. Simulations can often be even more realistic than traditional experiments, as they allow the free configuration of environment parameters found in the operational application field of the final product. Examples are supporting deep water operation of the US Navy or the simulating the surface of neighbored planets in preparation of NASA missions.
  3. Simulations can often be conducted faster than real time. This allows using them for efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained from operational data. This use of simulation adds decision support simulation systems to the tool box of traditional decision support systems.
  4. Simulations allow setting up a coherent synthetic environment that allows for integration of simulated systems in the early analysis phase via mixed virtual systems with first prototypical components to a virtual test environment for the final system. If managed correctly, the environment can be migrated from the development and test domain to the training and education domain in follow-on life cycle phases for the systems (including the option to train and optimize a virtual twin of the real system under realistic constraints even before first components are being built).

Simulation in science[edit]

Modeling and simulation are important in research. Representing the real systems either via physical reproductions at smaller scale, or via mathematical models that allow representing the dynamics of the system via simulation, allows exploring system behavior in an articulated way which is often either not possible, or too risky in the real world.

How modeling extends the scientific method at the base of research

Modeling and simulation as an emerging discipline[edit]

"The emerging discipline of M&S is based on developments in diverse computer science areas as well as influenced by developments in System Theories, Systems Engineering, Software Engineering, Artificial Intelligence, and more. This foundation is as diverse as that of engineering management and brings elements of art, engineering, and science together in a complex and unique way that requires domain experts to enable appropriate decisions when it comes application or development of M&S technology in the context of this paper. The diversity and application-oriented nature of this new discipline some-times results in the challenge, that the supported application domains themselves already have vocabularies in place that are not necessarily aligned between disjunctive domains. A comprehensive and concise representation of concepts, terms, and activities is needed that make up a professional Body of Knowledge for the M&S discipline. Due to the broad variety of contributors, this process is still ongoing."[3]

Padilla et al. recommend in "Do we Need M&S Science" to distinguish between M&S Science, Engineering, and Applications.[4]

  • M&S Science contributes to the Theory of M&S, defining the academic foundations of the discipline.
  • M&S Engineering is rooted in Theory but looks for applicable solution patterns. The focus is general methods that can be applied in various problem domains.
  • M&S Applications solve real world problems by focusing on solutions using M&S. Often, the solution results from applying a method, but many solutions are very problem domain specific and are derived from problem domain expertise and not from any general M&S theory or method.

Models can be composed of different units (models at finer granularity) linked to achieve a specific goal; for this reason they can be also called modelling solutions.

Modeling and simulation in pharmacy education[edit]

The shortage of pharmacists in the United States has prompted increases in class sizes and the number of satellite and distance-learning programs at colleges and schools of pharmacy. This rapid expansion has created a burden on existing clinical experimental sites.[5] The Accreditation Council on Pharmacy Education (ACPE) requires at least 1440 hours of advanced pharmacy practice experience (APPE); included among the 1440 hours of APPE, the ACPE requires colleges and schools of pharmacy to provide a minimum of 300 hour of introductory pharmacy practice experience (IPPE) interspersed throughout the first three years of the pharmacy curriculum.[6] Simulation training may be one such model to provide students with the opportunity to apply didactic knowledge and reduce the burden on experiential sites.The inclusion of simulation in IPPEs has gained acceptance and is encourages by ACPE as describe in the Policies and Procedures for ACPE Accreditation of Professional Degree Programs – January 2010. Addendum 1.3, Simulations for Introductory Pharmacy Practices Experiences – Approved June 2010, states: Simulation may not be utilized to supplant or replace the minimum expectation for time spent in actual pharmacy practice settings as set forth in the previously established policy. Beyond the majority of time in actual pharmacy practice settings,colleges and schools may utilize simulation to account for no greater than 20%(e.g., 60 hours of a 300 hour IPPE program) of total IPPE time. Several pharmacy colleges and schools have incorporated simulation as part of their core curricula. At the University of Pittsburgh School of Pharmacy, high-fidelity patient simulators are used to reinforce therapeutics. While the University of Rhode Island College of Pharmacy integrated their simulation program into their pharmacology and medicinal chemistry coursework; and was the first college of pharmacy to purchase a high-fidelity patient simulator. Some pharmacy colleges and schools host virtual reality and full environment simulation programs. For example, Purdue University School of Pharmacy and the university’s Envision Center for Data Perceptualization collaborated with the United States Pharmacopeia (USP) to create a virtual clean room that is USP 797 standards compliant.[7]

Application domains[edit]

There are many categorizations possible, but the following taxonomy has been very successfully used in the defense domain, and is currently applied to medical simulation and transportation simulation as well.

  • Analyses Support is conducted in support of planning and experimentation. Very often, the search for an optimal solution that shall be implemented is driving these efforts. What-if analyses of alternatives fall into this category as well. This style of work is often accomplished by simulysts - those having skills in both simulation and as analysts. This blending of simulation and analyst is well noted in Kleijnen.[8]
  • Systems Engineering Support is applied for the procurement, development, and testing of systems. This support can start in early phases and include topics like executable system architectures, and it can support testing by providing a virtual environment in which tests are conducted. This style of work is often accomplished by engineers and architects.
  • Training and Education Support provides simulators, virtual training environments, and serious games to train and educate people. This style of work is often accomplished by trainers working in concert with computer scientists.

A special use of Analyses Support is applied to ongoing business operations. Traditionally, decision support systems provide this functionality. Simulation systems improve their functionality by adding the dynamic element and allow to compute estimates and predictions, including optimization and what-if analyses.

Individual concepts[edit]

Although the terms “modeling” and “simulation” are often used as synonyms within disciplines applying M&S exclusively as a tool, within the discipline of M&S both are treated as individual and equally important concepts. Modeling is understood as the purposeful abstraction of reality, resulting in the formal specification of a conceptualization and underlying assumptions and constraints. M&S is in particular interested in models that are used to support the implementation of an executable version on a computer. The execution of a model over time is understood as the simulation. While modeling targets the conceptualization, simulation challenges mainly focus on implementation, in other words, modeling resides on the abstraction level, whereas simulation resides on the implementation level.

Conceptualization and implementation – modeling and simulation – are two activities that are mutually dependent, but can nonetheless be conducted by separate individuals. Management and engineering knowledge and guidelines are needed to ensure that they are well connected. Like an engineering management professional in systems engineering needs to make sure that the systems design captured in a systems architecture is aligned with the systems development, this task needs to be conducted with the same level of professionalism for the model that has to be implemented as well. As the role of big data and analytics continues to grow, the role of combined simulation of analysis is the realm of yet another professional called a simulyst - in order to blend algorithmic and analytic techniques through visualizations available directly to decision makers. A study designed for the Bureau of Labor and Statistics [9] by Lee et al. provides an interesting look at how bootstrap techniques (statistical analysis) were used with simulation to generate population data where there existed none.

Academic modeling and simulation programs[edit]

Modeling and simulation has only recently become an academic discipline of its own. Formerly, those working in the field usually had a background in engineering.

The following institutions offer degrees in Modeling and Simulation:

Ph D. Programs

Masters Programs

Professional Science Masters Programs

Undergraduate Programs

Modeling and Simulation Body of Knowledge[edit]

The Modeling and Simulation Body of Knowledge (M&S BoK) is the domain of knowledge (information) and capability (competency) that identifies the modeling and simulation (M&S) community of practice and the M&S profession, industry, and market.[10]

The M&S BoK Index is a set of pointers providing handles so that subject information content can be denoted, identified, accessed, and manipulated.[11]

The development of M&S BoK Indices has been championed by SimSummit.

Summary[edit]

In summary, three activities have to be conducted and orchestrated to ensure success: a model must be produced that captures formally the conceptualization, a simulation must implement this model, and management processes must ensure that model and simulation are interconnected and on the current state (which means that normally the model needs to be updated in case the simulation is changed as well).

The military and defense domain, in particular within the United States, has been the main M&S champion, in form of funding as well as application of M&S. E.g., M&S in modern military organizations is part of the acquisition/procurement strategy. Specifically, M&S is used to conduct Events and Experiments that influence Requirements and Training for military Systems. As such, M&S is considered an integral part of systems engineering of military Systems. Other application domains, however, are currently catching up. M&S in the fields of medicine, transportation, and other industries is poised to rapidly outstrip DoD’s use of M&S in the years ahead, if it hasn’t already happened.[12]

See also[edit]

References[edit]

  1. ^ "Department of Defense Modeling and Simulation the term as be defined(M&S) Glossary", DoD 5000.59-M, Department of Defense, 1998 [1]
  2. ^ National Science Foundation (NSF) Blue Ribbon Panel (2006). Report on Simulation-Based Engineering Science: Revolutionizing Engineering Science through Simulation. NSF Press, May
  3. ^ Tolk, Andreas. Engineering Management Challenges for Applying Simulation as a Green Technology. 
  4. ^ Padilla, Jose; S.Y. Diallo, A. Tolk (October 2011). "Do We Need M&S Science?". SCS M&S Magazine (4): 161–166. Retrieved July 1, 2012. 
  5. ^ Vyas, D., Wombwell, E.,Russell, E., & Caligiuri, F. (2010). High-fidelity patient simulationseries to supplement introductory pharmacy practice experiences. Am J Pharm Educ, 74(9), 169.
  6. ^ Accreditation Council forPharmacy Education: Policies and Procedures for ACPE Accreditation forProfessional Degree Programs. (2011). Retrieved July 13, 2013, from https://www.acpe-accredit.org/pdf/FinalS2007Guidelines2.0.pdf
  7. ^ Lin, K., Travlos, D. V.,Wadelin, J. W., & Vlasses, P. H. (2011). Simulation and IntroductoryPharmacy Practice Experiences. AmericanJournal of Pharmaceutical Education, 75(10), 209. doi: 10.5688/ajpe7510209
  8. ^ http://www3.nd.edu/~gmadey/sim06/Classnotes/Validation/kleijnen5.pdf
  9. ^ http://www.bls.gov/osmr/pdf/st130210.pdf
  10. ^ Waite, W. (2004) "Foundations '04: A Workshop for VV&A in the 21st Century, Session 10: V&V Education Initiatives
  11. ^ Waite, W. (2004) "Foundations '04: A Workshop for VV&A in the 21st Century, Session 10: V&V Education Initiatives
  12. ^ Collins, A.J.; S.R. Shefrey, J. Sokolowski, C.D. Turnitsa, E. Weisel (January 2011). "Modeling and Simulation Standards Study: Healthcare Workshop report". VMASC Report, Suffolk VA. 

External links[edit]