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Modeling and Simulation
Published in B K Bala, Energy Systems Modeling and Policy Analysis, 2022
It is sometimes possible to solve mathematical models by analytical methods. But for complex systems, the solution of the mathematical model of systems by analytical methods is extremely difficult or may be beyond the reach of today’s mathematics. For such complex systems, only a step-by-step numerical solution is possible. This process of step-by-step numerical solution is called simulation. Simulation models are used in place of real systems. A computer simulation is an inexpensive and rapid method of experimenting with a system to give useful information about the dynamics of the real system. Scenarios based on simulated results can provide guidelines for policy planning and the management of complex and dynamic systems such as energy systems.
Building envelope performance monitoring and modeling of West Coast rainscreen enclosures
Published in Paul Fazio, Hua Ge, Jiwu Rao, Guylaine Desmarais, Research in Building Physics and Building Engineering, 2020
G. Finch, J. Straube, B. Hubbs
The hygrothermal modeling results are only as accurate as the assembly data, material properties, and the interior and exterior conditions input. The comparison of the computer simulation to field data allows for verification of the assumptions, model inputs, and results.
Analytical Modeling of Stochastic Systems
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
A computer simulation is software that operates analogously to the system being modeled. For instance, a computer simulation of a simple queue would have a software module that generates arriving customers, another that handles the waiting line, and another that handles the service process. A simulation of the operation of a tank in the field would typically include modules for the movement of the tank, for the employment of its weapons, for its communication with other tanks and with higher echelons, and also for the terrain and for enemy units, in more or less detail according to the objectives of the model. Simulation can be deterministic or probabilistic, static or dynamic, and (if dynamic) discrete or continuous. Deterministic simulations like flight simulators are common in engineering applications, less so in operations research. A simulation that is probabilistic and static is often called a Monte Carlo simulation; this is the first type of simulation that had an important practical application, solving some problem in nuclear physics before the age of electronic computers, using tables of random numbers as input. A simulation that is probabilistic, dynamic (that is, changing over time), and discrete (changing state only at discrete intervals when particular modeled events happen) is a discrete event simulation, perhaps the most common method of modeling a stochastic system. Simulations that are probabilistic, dynamic, and continuous are harder to work with and much less common in analytical applications.
Constructing a Simulation Surrogate with Partially Observed Output
Published in Technometrics, 2023
Moses Y.-H. Chan, Matthew Plumlee, Stefan M. Wild
Computer simulations are used to understand and analyze systems where directly experimenting on the real system is difficult or infeasible. The output of a simulation model depends on a user-specified input configuration that defines the physical and controllable properties of the system. When a user simulates the system, also referred to as running the simulation, they receive outputs related to quantities of interest to the user. Running a simulation can be computationally expensive; each run can cost thousands of core-hours, see for examples the simulation of storm surge (Plumlee et al. 2021a), influenza spread (Venkatramanan et al. 2021), and nuclear dynamics (Phillips et al. 2021). Because these simulations are expensive, it is often helpful to build an emulator, or “surrogate,” trained on simulation data to predict at unsimulated (i.e., out-of-sample) configurations.
Utilizing a Simulation Approach for Data Analytics Pedagogy
Published in Journal of Computer Information Systems, 2021
In this study, I focused on the use of computer simulation as an approach to teach data analytics. Computer simulation is a computer program that mimics a hypothesized real-world system and produces a numerical representation of the system’s state.12 Previous studies indicate that simulation has been used for teaching multiple concepts in different disciplines to achieve requisite pedagogical outcomes.13 In a managerial economics context, for instance, Washbush and Gosen14 performed exploratory studies to examine the effectiveness of simulation games as a learning technique. As opposed to traditional learning methods where course concepts are presented followed by an application of the concepts to a problem, simulation encourages students to first understand the core concepts while applying the concepts in parallel to a problem.15
Advanced mechanical ventilation modes: design and computer simulations
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Computer simulations are strong tools that allow the designer to observe the possible outcomes of the considered techniques and to compare the advantages/disadvantages before using them in the real (physical) world. Particularly for the applications which are relatively complex such as the advanced ventilation modes, usage of the computer tools has significant importance. Thanks to the simulation environments, the existing algorithms can be further improved and/or new algorithms can be developed. It will provide mechanical ventilator systems to operate in a better way. Even if the theoretical and practical results will not be the same, it still provides valuable information to the physicians, such as the applicability of the modes for the several diseases or scenarios, the waveforms of the pressure-volume-flow variables, the variation of the human ventilatory response or the effects of the advanced modes on the other systems such as cardiovascular system, central nervous system, etc. depending on the details of the studied mathematical model. It is also possible to use the simulation tool for medical education so that the operating principles, ventilator waveforms, and possible strategies can be studied without interfering with the real patients.