James J. Nutaro - Building Software for Simulation: Theory and Algorithms, with Applications in C++

BUILDING SOFTWARE FOR SIMULATION

Copyright 2011 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Library of Congress Cataloging-in-Publication Data:

Nutaro, James J.
Building software for simulation: theory and algorithms with applications in C++ / James J. Nutaro
p. cm.
Includes bibliographical references and index.
ISBN 978-0-470-41469-9 (cloth)

PREFACE

Building Software for Simulation is different from many other books on simulation because its focuses on the design and implementation of simulation software; by culminating in a complete system for simulation, this book makes itself unique. The design and construction of simulation software has been a topic persistently absent from textbooks even though many, if not most, simulation projects require the development of new software. By addressing this important topic, Building Software for Simulation will, I hope, complement other excellent textbooks on modeling and simulation. This book is intended as both an introduction to simulation programming and a reference for experienced practitioners. I hope you will find it useful in these respects.

This book approaches simulation from the perspective of Zeiglers theory of modeling and simulation, introducing the theorys fundamental concepts and showing how to apply these to problems in engineering. The original concept of the book was not so ambitious; its early stages more closely resembled a cookbook for building simulators, focusing almost exclusively on algorithms, examples of simulation programs, and guidelines for the object-oriented design of a simulator. The book retains much of this flavor, demonstrating each concept and algorithm with working code. Unlike a cookbook, however, concepts and algorithms discussed in the text are not disembodied; their origins in the theory of modeling and simulation are made apparent, and this motivates and provides greater insight into their application.

Chapters 3, 4, and 5, are the centerpiece of the text. I begin with discrete-time systems, their properties and structure, simulation algorithms, and applications. Discrete-time system will be familiar to most readers and if not, they are easily grasped. Discrete-time systems are generalized to introduce discrete event systems; this approach leads naturally to Zeiglers discrete-event system specification, its properties and structures, and simulation procedures. The central three chapters conclude with methods for modeling and simulating systems that have interacting continuous and discrete-event dynamics.

The three main chapters are bracketed by applications to robotics, control and communications, and electrical power systems. These examples are more complicated than might be expected in a textbook; three examples occupy two complete chapters. They are, however, described in sufficient detail for a student to reproduce the printed results and to go a step further by exploring unanswered questions about the example systems. The books appendixes discuss technical problems that do not fit cleanly into the narrative of the manuscript: testing and design, parallel computing, and a brief review of mathematical topics needed for the examples.

Many people contributed advice and guidance as the book evolved. I am particularly grateful to Vladimir Protopopescu at Oak Ridge National Laboratory for his review of and critical commentary on my rough drafts; his advice had a profound impact on the organization of the text and my presentation of much of the material. Im also grateful to Angela, who reviewed very early drafts and remarked only rarely on the state of the yard and unfinished projects around the house. Last, but not least, thanks to Joe and Jake, who, in the early morning hours while I worked, quietly (for the most part) entertained themselves.

JIM NUTARO

Oak Ridge, Tennessee
December 2009

CHAPTER 1

INTRODUCTION

Simulation has made possible systems that would otherwise be impracticable. The sophisticated controls in modern aircraft and automobiles, the powerful microprocessors in desktop computers, and space-faring robots are possible because simulations reduce substantially the need for expensive prototypes. These complicated systems are designed with the aid of sophisticated simulators, and the simulation software itself has therefore become a major part of most engineering efforts. A projects success may hinge on the construction of affordable, reliable simulators.

Good software engineering practices and a serviceable software architecture are essential to building software for any purpose, and simulators are no exception. The cost of a simulator is determined less by the technical intricacy of its subject than by factors common to all software: the clarity and completeness of requirements, the design and development processes that control complexity, effective testing and maintenance, and the ability to adapt to changing needs. Small software projects that lack any of these attributes are expensive at best, and the absence of some or all of these points is endemic to projects that fail.

It is nonetheless common for the design of a complicated simulator to be driven almost exclusively by consideration of the objects being simulated. The project begins with a problem that is carefully circumscribed: for example, to calculate the time-varying voltages and currents in a circuit, to estimate the in-process storage requirements of a manufacturing facility, or to determine the rate at which a disease will spread through a population. Equipped with an appropriate set of algorithms, the scientist or engineer crafts a program to answer the question at hand. The end result has three facets: the model, an algorithm for computing its trajectories, and some means for getting data into and out of the simulator. The first of these are the reason why the simulator is being built. The other two, however, often constitute the majority of the code. Because they are secondary interests, their scope and size are reduced by specialization; peculiarities of the model are exploited as the simulator is built, and so its three aspects become inextricably linked.