Modeling Methodologies and Simulation for Dynamical Systems

Published

August 27, 2012

Author(s)

Ion Matei, Conrad E. Bock

Abstract

Computer-interpretable representations of system structure and behavior are at the center of designing today’s complex systems. Engineers create and review such representations using graphical modeling languages that support specification, analysis, design, verification and validation of systems that include hardware, software, data, personnel, procedures, and facilities, such as the Systems Modeling Language, an extension of the Unified Modeling Language. However, these languages usually do not support detailed enough simulation and performance analysis and must be enhanced with domain specific tools for this purpose. System modeling and simulation tools are often used separately and sequentially, which reduces the efficiency of the design process. As a result, there is an increasing need for integrating different simulation tools under a common framework for integration into system modeling tools. In this report we present results of analyzing modeling methodologies for simulating dynamical systems. We identify two primary methodologies, namely signal-flow and physical-interaction, and introduce the main concepts for building models based on these methodologies. To support the analysis, we give representative implementation examples of these concepts and methodologies in two simulation tools. We identify these concepts and methodologies in order to build an abstract model of simulation tools that facilitates integration of multiple simulation tools into a common framework.

Citation

NIST Interagency/Internal Report (NISTIR) - 7875

Report Number

7875

NIST Pub Series

NIST Interagency/Internal Report (NISTIR)

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.IR.7875

Keywords

modeling methodologies, simulation

Citation

Matei, I. and Bock, C. (2012), Modeling Methodologies and Simulation for Dynamical Systems, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.7875 (Accessed April 25, 2024)