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Object-Oriented Modeling and Simulation of Multipurpose Batch Plants
Published in Cornelius Leondes, Computer-Integrated Manufacturing, 2019
Sebastian Engell, Martin Fritz, Wöllhaf Konrad
For the plant model, care must be taken regarding on what level of detail modeling should take place. Continuous simulation techniques require the description of a model block using a set of differential equations. This notion, however, does not make sense for a discrete event simulator. Furthermore, creating models in terms of differential equations (or other modeling paradigms like finite state machines) requires expert knowledge that should not be necessary to use the simulator.
Simulink®
Published in Harold Klee, Randal Allen, Simulation of Dynamic Systems with MATLAB® and Simulink®, 2018
PHYSBE is a benchmark simulation of the human circulatory system. It was first introduced by John Mcleod in 1966 in an article titled “PHYSBE … a Physiological Simulation Benchmark Experiment.” Over the years, it has appeared in numerous references involving modeling and simulation. The underlying dynamics have been simulated using the popular continuous simulation programs including Simulink.
Types of Simulation
Published in Raymond J. Madachy, Daniel X. Houston, What Every Engineer Should Know About Modeling and Simulation, 2017
Raymond J. Madachy, Daniel X. Houston
System dynamics is the most widely used form of continuous simulation. System dynamics refers to the simulation methodology pioneered by Jay Forrester, which was developed to model complex continuous systems for improving management policies and organizational structures [5] [6]. Improvement comes from model-based understandings.
The Security-as-a-Service Market for Small and Medium Enterprises
Published in Journal of Computer Information Systems, 2022
Derek L. Nazareth, Jae Choi, Thomas L. Ngo-Ye
Clearly, SECaaS has become a mainstay in the cloud computing market and it is vital to understand its potential growth. A variety of modeling techniques are available to assess the future of the SECaaS market, including forecasting models, econometric models, and simulation. Given the nascent state of the SECaaS market, the paucity of data, and the substantial growth in the cloud computing market, traditional forecasting models are likely to perform poorly and will not be robust. Econometric models offer greater control in terms of model formulation, but lack the ability to capture the inherent dynamism and overall complexity of the SECaaS market. Simulation represents an appropriate option in that it offers the ability to model the phenomenon accurately while incorporating the inherent complexity of the system. Simulation options include discrete event simulation, continuous simulation, agent-based simulation, and system dynamics. Discrete event simulation is effective for modeling transaction-based systems; continuous simulation is useful for real-time systems; and agent-based simulation is appropriate when modeling individual decision makers. System dynamics affords the opportunity to study multiple constructs, both within and across time periods. Based on principles developed by Forrester,25 system dynamics offers a viable option, using first order linear and non-linear equations that associate pertinent factors over time.26 Trcek27 indicated that as a methodology for investigating security, system dynamics is an effective option. This paper employs system dynamics to study the SECaaS market. The model includes SECaaS service providers, customers, SECaaS cost, security threats, relating them to overall market forecast. It affords the opportunity to study the effect of alternative market conditions, and the impact on SECaas providers and customers. Successfully running the model under a variety of different scenarios illustrates its utility as a design science artifact.