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# ES93Q - Systems Modelling and Simulation

(15 Credits)

#### Aims:

This module aims to provide students with an introduction to techniques in systems analysis and mathematical modelling for application to physical processes across a range of engineering disciplines. It will be illustrated how the techniques for the analysis of systems, and the general systems approach, are highly relevant to processes of a multi-disciplinary engineering nature.

The module will focus on a broad and generic systems approach to understanding physical systems. Techniques for systems analysis, approaches to systems modelling and the techniques for the simulation of systems models will be considered. In particular, a rigorous approach to the application of physical laws to formulate appropriate dynamical systems representations, and their subsequent analysis using linear and nonlinear methods, will be taught.

The application of appropriate computational tools for systems analysis and simulation will naturally be included. The development of data-driven models and system identification approaches will also be considered with importance placed on the role of model validation.

The examples presented will be drawn from a range of different engineering disciplines to illustrate the advantages of a systems approach.

This module aims to:

• Focus on the application of a systems and modelling approach to the analysis of physical processes across a range of engineering disciplines.
• Introduce the engineering techniques and foundation material needed to perform such analysis.
• Illustrate how these approaches can be used for analysing problems and processes of a multi-disciplinary nature.

#### Learning Outcomes:

At completion, students will be able to:

• Formulate mathematical models of physical processes using appropriate physical laws.
• Understand, and be able to apply, techniques for analysing both linear and nonlinear systems.
• Apply appropriate computational tools, in an appropriate way, for systems analysis and simulation.
• Understand the techniques available for the development of data-driven models and performing system identification/parameter estimation.
• Understand the importance of model validation.

#### Illustrative Bibliography:

Matlab for Control Engineers. Ogata, K. Prentice Hall (2007) ISBN 9780136150770
Advanced Engineering Mathematics. Kreyszig, E. Wiley (2006) ISBN 9780471728979
Modelling and Analysis of Dynamic Systems. Close, Charles M John Wiley and Sons Ltd (1995) ISBN 9780395661581
Applied Non Linear Dynamics a Primer on Stability Chaos & Friends. Kaplan, Daniel Springer-Vlg. (1997) ISBN 9780387944401