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A simulation is an approximate imitation of the operation of a process or system that represents its operation over time.
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution.
A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources. These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs are key components along with sea ice and land-surface components.
Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows. Computers are used to perform the calculations required to simulate the free-stream flow of the fluid, and the interaction of the fluid with surfaces defined by boundary conditions. With high-speed supercomputers, better solutions can be achieved, and are often required to solve the largest and most complex problems. Ongoing research yields software that improves the accuracy and speed of complex simulation scenarios such as transonic or turbulent flows. Initial validation of such software is typically performed using experimental apparatus such as wind tunnels. In addition, previously performed analytical or empirical analysis of a particular problem can be used for comparison. A final validation is often performed using full-scale testing, such as flight tests.
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of or the outcome of a real-world or physical system. Since they allow to check the reliability of chosen mathematical models, computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.
Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.
Computer-aided engineering (CAE) is the broad usage of computer software to aid in engineering analysis tasks. It includes finite element analysis (FEA), computational fluid dynamics (CFD), multibody dynamics (MBD), durability and optimization. It is included with computer-aided design (CAD) and computer-aided manufacturing (CAM) in the collective abbreviation "CAx".
An integrator in measurement and control applications is an element whose output signal is the time integral of its input signal. It accumulates the input quantity over a defined time to produce a representative output.
Computational science, also known as scientific computing or scientific computation (SC), is a rapidly growing field that uses advanced computing capabilities to understand and solve complex problems. It is an area of science which spans many disciplines, but at its core, it involves the development of models and simulations to understand natural systems.
A physics engine is computer software that provides an approximate simulation of certain physical systems, such as rigid body dynamics, soft body dynamics, and fluid dynamics, of use in the domains of computer graphics, video games and film (CGI). Their main uses are in video games, in which case the simulations are in real-time. The term is sometimes used more generally to describe any software system for simulating physical phenomena, such as high-performance scientific simulation.
In control theory, Advanced process control (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems. Advanced process controls are usually deployed optionally and in addition to basic process controls. Basic process controls are designed and built with the process itself, to facilitate basic operation, control and automation requirements. Advanced process controls are typically added subsequently, often over the course of many years, to address particular performance or economic improvement opportunities in the process.
Computational science and engineering (CSE) is a relatively new discipline that deals with the development and application of computational models and simulations, often coupled with high-performance computing, to solve complex physical problems arising in engineering analysis and design as well as natural phenomena. CSE has been described as the "third mode of discovery".
Web-based simulation (WBS) is the invocation of computer simulation services over the World Wide Web, specifically through a web browser. Increasingly, the web is being looked upon as an environment for providing modeling and simulation applications, and as such, is an emerging area of investigation within the simulation community.
Dynamic decision-making (DDM) is interdependent decision-making that takes place in an environment that changes over time either due to the previous actions of the decision maker or due to events that are outside of the control of the decision maker. In this sense, dynamic decisions, unlike simple and conventional one-time decisions, are typically more complex and occur in real-time and involve observing the extent to which people are able to use their experience to control a particular complex system, including the types of experience that lead to better decisions over time.
SLinCA@Home was a research project that uses Internet-connected computers to do research in fields such as physics and materials science.
Quasi-opportunistic supercomputing is a computational paradigm for supercomputing on a large number of geographically disperse computers. Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic resource sharing.
Computational astrophysics refers to the methods and computing tools developed and used in astrophysics research. Like computational chemistry or computational physics, it is both a specific branch of theoretical astrophysics and an interdisciplinary field relying on computer science, mathematics, and wider physics. Computational astrophysics is most often studied through an applied mathematics or astrophysics programme at PhD level.
Combustion models for CFD refers to combustion models for computational fluid dynamics. Combustion is defined as a chemical reaction in which a hydrocarbon fuel reacts with an oxidant to form products, accompanied with the release of energy in the form of heat. Being the integral part of various engineering applications like: internal combustion engines, aircraft engines, rocket engines, furnaces, and power station combustors, combustion manifests itself as a wide domain during the design, analysis and performance characteristics stages of the above-mentioned applications. With the added complexity of chemical kinetics and achieving reacting flow mixture environment, proper modeling physics has to be incorporated during computational fluid dynamic (CFD) simulations of combustion. Hence the following discussion presents a general outline of the various adequate models incorporated with the Computational fluid dynamic code to model the process of combustion.
Physically based animation is an area of interest within computer graphics concerned with the simulation of physically plausible behaviors at interactive rates. Advances in physically based animation are often motivated by the need to include complex, physically inspired behaviors in video games, interactive simulations, and movies. Although off-line simulation methods exist to solve most all of the problems studied in physically-based animation, these methods are intended for applications that necessitate physical accuracy and slow, detailed computations. In contrast to methods common in offline simulation, techniques in physically based animation are concerned with physical plausibility, numerical stability, and visual appeal over physical accuracy. Physically based animation is often limited to loose approximations of physical behaviors because of the strict time constraints imposed by interactive applications. The target frame rate for interactive applications such as games and simulations is often 25-60 hertz, with only a small fraction of the time allotted to an individual frame remaining for physical simulation. Simplified models of physical behaviors are generally preferred if they are more efficient, easier to accelerate, or satisfy desirable mathematical properties. Fine details are not important when the overriding goal of a visualization is aesthetic appeal or the maintenance of player immersion since these details are often difficult for humans to notice or are otherwise impossible to distinguish at human scales.
FEATool Multiphysics is a physics, finite element analysis (FEA), and PDE simulation toolbox. FEATool Multiphysics features the ability to model fully coupled heat transfer, fluid dynamics, chemical engineering, structural mechanics, fluid-structure interaction (FSI), electromagnetics, as well as user-defined and custom PDE problems in 1D, 2D (axisymmetry), or 3D, all within a graphical user interface (GUI) or optionally as script files. FEATool has been employed and used in academic research, teaching, and industrial engineering simulation contexts.
References
- ↑ Bullock, Seth; John Cartlidge; Martin Thompson (2002). "Prospects for Computational Steering of Evolutionary Computation". Workshop Proceedings of the Eighth International Conference on Artificial Life. MIT Press. pp. 131–137.
- ↑ Love, Peter; Jeremy Martin (2000). "Steering High Performance Parallel Programs: A Case Study". Communicating Process Architectures 2000. IOS Press. pp. 99–108.
- ↑ Vetter, Jeffrey (1997). "Computational Steering Annotated Bibliography". SIGPLAN Notices . 32 (6): 40–44. doi:10.1145/261353.261359.
- ↑ Vetter, Jeffrey; Reed, Daniel (November 2000). "Real-time Performance Monitoring, Adaptive Control, and Interactive Steering of Computational Grids". International Journal of High Performance Computing Applications . 14: 357–366. doi:10.1177/109434200001400407.
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