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Building Software for Simulation: Theory and Algorithms, by James J. Nutaro

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By James J. Nutaro

A precise consultant to the layout and implementation of simulation software

This e-book deals a concise advent to the artwork of creating simulation software program, accumulating crucial strategies and algorithms in a single position. Written for either participants new to the sphere of modeling and simulation in addition to skilled practitioners, this advisor explains the layout and implementation of simulation software program utilized in the engineering of enormous platforms whereas proposing the proper mathematical components, idea discussions, and code development.

The e-book methods the subject from the point of view of Zeigler's idea of modeling and simulation, introducing the theory's basic techniques and exhibiting how you can follow them to engineering difficulties. Readers will research 5 important abilities for development simulations of complex systems:

  • Working with primary abstractions for simulating dynamic systems
  • Developing simple simulation algorithms for non-stop and discrete occasion models
  • Combining non-stop and discrete occasion simulations right into a coherent whole
  • Applying recommendations for checking out a simulation
  • Understanding the theoretical foundations of the modeling constructs and simulation algorithms

The important chapters of the booklet introduce, clarify, and display the weather of the idea which are most vital for construction simulation instruments. they're bracketed by means of purposes to robotics, regulate and communications, and electrical energy structures; those complete examples truly illustrate how the innovations and algorithms are placed to exploit. Readers will discover the layout of object-oriented simulation courses, simulation utilizing multi-core processors, and the mixing of simulators into higher software program systems.

The specialize in software program makes this publication relatively valuable for machine technological know-how and computing device engineering classes in simulation that concentrate on development simulators. it's essential analyzing for undergraduate and graduate scholars learning modeling and simulation, in addition to for working towards scientists and engineers inquisitive about the improvement of simulation instruments.

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Extra resources for Building Software for Simulation: Theory and Algorithms, with Applications in C++

Example text

The simulation engine is simplest for discrete-time systems. Once the essential concepts and software artifacts are established, extending them to discrete-event and continuous systems is straightforward. The structure, if not the details, of the simulation engine persists in the general case; models continue to be derived from Atomic and Network classes, and the Simulator computes their dynamic behavior. The discrete-time, discrete-event, and continuous simulations differ mainly in the algorithms that are used to advance the simulation clock.

The symbol (or word) delta is commonly used to represent change. This usage is introduced in first- and second-year calculus and physics courses. Use of the symbol (or word) lambda for output is relatively uncommon. The method name output func seemed, therefore, less likely to confuse the programmer. P1: OSO c03 JWBS040-Nutaro August 30, 2010 14:14 Printer Name: Yet to Come ATOMIC MODELS 45 input and output objects are managed. This, however, forces every class that uses the Atomic interface to be a generic class, and therefore the entire simulation engine must be implemented as a set of generic classes.

For example, z < t0 , t3 > can be dissected into z a < t0 , t2 > and z b < t2 , t3 > such that z = z a · z b . 1 Concatenating two trajectories. P1: OSO c03 JWBS040-Nutaro August 30, 2010 14:14 Printer Name: Yet to Come ATOMIC MODELS 35 The time-shifting operator τ moves the starting and ending times of the trajectory by equal distances. For example, applying τ with the argument 1 to a trajectory z a [0, 2] gives τ (1, z a [0, 2]) = z b [1, 3]. The new trajectory z b is the same as z a but is defined over a different interval; in this example, z a (0) = z b (1), z a (1) = z b (2), and z a (2) = z b (3).

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