Modeling and simulation

About: Modeling and simulation is a research topic. Over the lifetime, 10273 publications have been published within this topic receiving 111550 citations.

Calibration of abstract performance models for system-level design space exploration

Abstract: High-level performance modeling and simulation have become a key ingredient of system-level design as they facilitate early architectural design space exploration. An important precondition for such high-level modeling and simulation methods is that they should yield trustworthy performance estimations. This requires validation (if possible) and calibration of the simulation models, which are two aspects that have not yet been widely addressed in the system-level community. This article presents a number of mechanisms for both calibrating isolated model components as well as a system-level performance model as a whole. We discuss these model calibration mechanisms in the context of our Sesame system-level simulation framework. Two illustrative case studies will also be presented to indicate the merits of model calibration.

Hierarchical approaches to modeling high-power-factor AC-DC converters

Abstract: The utility of a hierarchical approach to modeling and simulation in power electronics is illustrated using a high power factor (HPF) AC-DC switching power converter as an example. The combined use of several modeling techniques and tools is shown to provide comprehensive simulation coverage during analysis and design of the HPF converter. A summary of the various modeling levels, and their application domains and CPU times is given in tabular form. The estimated times are obtained by linear scaling of the measured times for a particular level. The CPU times show the necessity of using a hierarchical approach when simulating extended intervals and/or large circuits. >

Simulation modelling is 50! Do we need a reality check?

Abstract: Simulation modelling is a fascinating research field. The techniques and tools of simulation modelling have been used to research and investigate the behaviour of various systems in a wide range of areas such as commerce, computer networks, defence, health, manufacturing and transportation. Indeed, the study of the use of these techniques and tools, and the development of new forms of these, are a rich source of research in their own right. Simulation modelling is about to reach the 50th anniversary of the development of GSP (General Simulation Program), the first simulation modelling language (Tocher and Owen, 1960). There have been several historical accounts of simulation modelling research. To complement these, we have performed a review of the recent history of simulation modelling. This study targeted three leading journals dedicated to this field. These are the ACM Transactions of Modeling and Computer Simulation, Simulation: Transactions of The Society for Modeling and Simulation International and Simulation Modelling Practice and Theory (formerly Simulation Practice and Theory). The study covered the first 6 years of this century (2000–2005) and included 576 papers. The key observation of this work was the relative lack of ‘real world’ involvement in simulation modelling research and an even greater lack of evidence of ‘real world’ benefit, arguably very alarming outcomes for an applied field. To further investigate this observation two additional surveys were carried out, one to study if real world papers appeared in the more widely known OR/MS literature (837 papers in 12 journals) and one to study if such papers appeared in Manufacturing and Logistics, an application area closely associated with simulation modelling (1077 papers in 10 journals). The results of these surveys confirmed our observations. We ask if this is the natural evolution of a field that has existed for half a century or an indication of a worrying problem? This paper reports on our findings and discusses whether or not simulation modelling research urgently needs to face a ‘reality check.’

Agent-based modeling and simulation of emergent behavior in air transportation

Abstract: Commercial aviation is feasible thanks to the complex socio-technical air transportation system, which involves interactions between human operators, technical systems, and procedures. In view of the expected growth in commercial aviation, significant changes in this socio-technical system are in development both in the USA and Europe. Such a complex socio-technical system may generate various types of emergent behavior, which may range from simple emergence, through weak emergence, up to strong emergence. The purpose of this paper is to demonstrate that agent-based modeling and simulation allows identifying changed and novel rare emergent behavior in this complex socio-technical system. An agent based model of a specific operation at an airport has been developed. The specific operation considered is the controlled crossing by a taxiing aircraft of a runway that is in use for controlled departures. The agent-based model includes all relevant human and technical agents, such as the aircraft, the pilots, the controllers and the decision support systems involved. This agent-based model is used to conduct rare event Monte Carlo (MC) simulations. The MC simulation results obtained confirm that agent based modeling and simulation of a socio-technical air transportation system allows to identify rare emergent behavior that was not identified through earlier, non-agent-based simulations, including human-in-the-loop simulations of the same operation. A typical example of such emergent behavior is the finding that alerting systems do not really reduce the safety risk. Agent based MC simulations of commercial aviation operations has been demonstrated as a viable way to be evaluated regarding rare emergent behaviour. This rare emergent behaviour could not have been found through the more traditional simulation approaches.

Stochastic modeling and simulation of gene networks - A review of the state-of-the-art research on stochastic simulations

Abstract: This paper provides a comprehensive review of the state-of-the-art research on stochastic simulations. It stimulates the interest of tackling the problem of stochastic simulation using statistical signal processing methods, as well as innovative thinking of stochastic modeling of gene networks from the viewpoint of signal processing