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E-raamat: Simulation and Modeling of Systems of Systems [Wiley Online]

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  • Formaat: 400 pages
  • Sari: ISTE
  • Ilmumisaeg: 08-Apr-2011
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118616723
  • ISBN-13: 9781118616727
Teised raamatud teemal:
  • Wiley Online
  • Hind: 216,75 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Simulation and Modeling of Systems of SystemsSuurem pilt
  • Formaat: 400 pages
  • Sari: ISTE
  • Ilmumisaeg: 08-Apr-2011
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118616723
  • ISBN-13: 9781118616727
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118616727
Systems engineering is the design of a complex interconnection of many elements (a system) to maximize a specific measure of system performance. It consists of two parts: modeling, in which each element of the system and its performance criteria are described; and optimization in which adjustable elements are tailored to allow peak performance. Systems engineering is applied to vast numbers of problems in industry and the military. An example of systems engineering at work is the control of the timing of thousands of city traffic lights to maximize traffic flow. The complex and intricate field of electronics and computers is perfectly suited for systems engineering analysis and in turn, advances in communications and computer technology have made more advanced systems engineering problems solvable. Thus, the two areas fed off of one another. This book is a basic introduction to the use of models and methods in the engineering design of systems. It is aimed at students as well as practicing engineers.

The concept of the "systems of systems" is discussed extensively, after a critical comparison of the different definitions and a range of various practical illustrations. It also provides key answers as to what a system of systems is and how its complexity can be mastered.
Introduction xi
Chapter 1 Simulation: History, Concepts, and Examples
1(56)
Pascal Cantot
1.1 Issues: simulation, a tool for complexity
1(13)
1.1.1 What is a complex system?
1(2)
1.1.2 Systems of systems
3(2)
1.1.3 Why simulate?
5(7)
1.1.4 Can we do without simulation?
12(2)
1.2 History of simulation
14(10)
1.2.1 Antiquity: strategy games
14(1)
1.2.2 The modern era: theoretical bases
15(3)
1.2.3 Contemporary era: the IT revolution
18(6)
1.3 Real-world examples of simulation
24(5)
1.3.1 Airbus
24(2)
1.3.2 French defense procurement directorate
26(3)
1.4 Basic principles
29(22)
1.4.1 Definitions
30(7)
1.4.2 Typology
37(14)
1.5 Conclusion
51(1)
1.6 Bibliography
52(5)
Chapter 2 Principles of Modeling
57(42)
Pascal Cantot
2.1 Introduction to modeling
57(1)
2.2 Typology of models
58(8)
2.2.1 Static/dynamic
58(1)
2.2.2 Deterministic/stochastic
59(4)
2.2.3 Qualities of a model
63(3)
2.3 The modeling process
66(25)
2.3.1 Global process
67(1)
2.3.2 Formulation of the problem
68(2)
2.3.3 Objectives and organization
70(1)
2.3.4 Analysis of the system
71(5)
2.3.5 Modeling
76(2)
2.3.6 Data collection
78(4)
2.3.7 Coding/implementation
82(5)
2.3.8 Verification
87(1)
2.3.9 Validation
87(1)
2.3.10 Execution
87(2)
2.3.11 Use of results
89(1)
2.3.12 Final report
90(1)
2.3.13 Commissioning/capitalization
90(1)
2.4 Simulation project management
91(3)
2.5 Conclusion
94(1)
2.6 Bibliography
94(5)
Chapter 3 Credibility in Modeling and Simulation
99(60)
Roland Rabeau
3.1 Technico-operational studies and simulations
99(2)
3.2 Examples of technico-operational studies based on simulation tools
101(1)
3.2.1 Suppression of aerial defenses
101(1)
3.2.2 Heavy helicopters
101(1)
3.3 VV&A for technico-operational simulations
102(6)
3.3.1 Official definitions
102(1)
3.3.2 Credibility
103(2)
3.3.3 Key players in the domain
105(3)
3.4 VV&A issues
108(37)
3.4.1 Elements concerned
108(6)
3.4.2 Verification and validation techniques
114(9)
3.4.3 VV&A approaches
123(18)
3.4.4 Responsibilities in a VV&A process
141(3)
3.4.5 Levels of validation
144(1)
3.4.6 Accreditation
144(1)
3.5 Conclusions
145(7)
3.5.1 Validation techniques
145(2)
3.5.2 Validation approaches
147(3)
3.5.3 Perspectives
150(2)
3.6 Bibliography
152(7)
Chapter 4 Modeling Systems and Their Environment
159(48)
Pascal Cantot
4.1 Introduction
159(1)
4.2 Modeling time
160(3)
4.2.1 Real-time simulation
160(1)
4.2.2 Step-by-step simulation
161(1)
4.2.3 Discrete event simulation
162(1)
4.2.4 Which approach?
162(1)
4.2.5 Distributed simulation
162(1)
4.3 Modeling physical laws
163(3)
4.3.1 Understanding the system
163(1)
4.3.2 Developing a system of equations
164(1)
4.3.3 Discrete sampling of space
165(1)
4.3.4 Solving the problem
166(1)
4.4 Modeling random phenomena
166(12)
4.4.1 Stochastic processes
166(1)
4.4.2 Use of probability
167(4)
4.4.3 Use of statistics
171(2)
4.4.4 Random generators
173(2)
4.4.5 Execution and analysis of results of stochastic simulations
175(3)
4.5 Modeling the natural environment
178(15)
4.5.1 Natural environment
178(1)
4.5.2 Environment databases
178(2)
4.5.3 Production of an SEDB
180(2)
4.5.4 Quality of an SEDB
182(1)
4.5.5 Coordinate systems
183(2)
4.5.6 Multiplicity of formats
185(8)
4.6 Modeling human behavior
193(10)
4.6.1 Issues and limitations
193(1)
4.6.2 What is human behavior?
194(2)
4.6.3 The decision process
196(1)
4.6.4 Perception of the environment
197(1)
4.6.5 Human factors
198(1)
4.6.6 Modeling techniques
199(3)
4.6.7 Perspectives
202(1)
4.7 Bibliography
203(4)
Chapter 5 Modeling and Simulation of Complex Systems: Pitfalls and Limitations of Interpretation
207(46)
Dominique Luzeaux
5.1 Introduction
207(2)
5.2 Complex systems, models, simulations, and their link with reality
209(9)
5.2.1 Systems
209(2)
5.2.2 Complexity
211(4)
5.2.3 The difficulty of concepts: models, modeling, and simulation
215(3)
5.3 Main characteristics of complex systems simulation
218(10)
5.3.1 Nonlinearity, the key to complexity
218(5)
5.3.2 Limits of computing: countability and computability
223(3)
5.3.3 Discrete or continuous models
226(2)
5.4 Review of families of models
228(16)
5.4.1 Equational approaches
229(3)
5.4.2 Computational approaches
232(5)
5.4.3 Qualitative phenomenological approaches
237(3)
5.4.4 Qualitative structuralist approach: application of category theory
240(4)
5.5 An example: effect-based and counter-insurgency military operations
244(2)
5.6 Conclusion
246(3)
5.7 Bibliography
249(4)
Chapter 6 Simulation Engines and Simulation Frameworks
253(42)
Pascal Cantot
6.1 Introduction
253(1)
6.2 Simulation engines
254(6)
6.2.1 Evolution of state variables
254(1)
6.2.2 Management of events and causality
255(1)
6.2.3 Simulation modes
256(2)
6.2.4 Example
258(2)
6.3 Simulation frameworks
260(30)
6.3.1 Some basic points on software engineering
260(8)
6.3.2 Frameworks
268(2)
6.3.3 Obstacles to framework use
270(2)
6.3.4 Detailed example: ESCADRE
272(18)
6.4 Capitalization of models
290(1)
6.5 Conclusion and perspectives
291(1)
6.6 Bibliography
292(3)
Chapter 7 Distributed Simulation
295(38)
Louis Igarza
7.1 Introduction
295(10)
7.1.1 The principle
295(2)
7.1.2 History of distributed simulations
297(1)
7.1.3 Some definitions
298(2)
7.1.4 Interoperability in simulation
300(2)
7.1.5 Standardization
302(1)
7.1.6 Advantages and limitations of distributed simulation
303(1)
7.1.7 Other considerations
303(2)
7.2 Basic mechanisms of distributed simulation
305(7)
7.2.1 Some key principles
305(1)
7.2.2 Updating attributes
306(1)
7.2.3 Interactions
307(1)
7.2.4 Time management
308(1)
7.2.5 Dead reckoning
309(1)
7.2.6 Multi-level modeling
310(1)
7.2.7 Section conclusion
311(1)
7.3 Main interoperability standards
312(14)
7.3.1 History
312(1)
7.3.2 HLA
313(6)
7.3.3 DIS
319(2)
7.3.4 TENA
321(3)
7.3.5 The future of distributed simulation: the LVC AR study
324(1)
7.3.6 Other standards used in distributed simulation
325(1)
7.4 Methodological aspects: engineering processes for distributed simulation
326(5)
7.4.1 FEDEP
327(2)
7.4.2 SEDEP
329(1)
7.4.3 DSEEP
330(1)
7.5 Conclusion: the state of the art: toward "substantive" interoperability
331(1)
7.6 Bibliography
331(2)
Chapter 8 The Battle Lab Concept
333(22)
Pascal Cantot
8.1 Introduction
333(3)
8.2 France: Laboratoire Technico-Operationnel (LTO)
336(14)
8.2.1 Historical overview
336(1)
8.2.2 Aims of the LTO
337(1)
8.2.3 Principles of the LTO
338(3)
8.2.4 Services of the LTO
341(1)
8.2.5 Experimental process
342(3)
8.2.6 Presentation of an experiment: PHOENIX 2008
345(4)
8.2.7 Evaluation and future perspectives of the LTO
349(1)
8.3 United Kingdom: the Niteworks project
350(1)
8.4 Conclusion and perspectives
351(1)
8.5 Bibliography
352(3)
Chapter 9 Conclusion: What Return on Investment Can We Expect from Simulation?
355(18)
Dominique Luzeaux
9.1 Returns on simulation for acquisition
355(2)
9.2 Economic analysis of gains from intelligent use of simulations
357(10)
9.2.1 Additional costs of the SBA
358(6)
9.2.2 Additional costs from unexpected events or bad planning
364(3)
9.3 Multi-project acquisition
367(1)
9.4 An (almost) definitive conclusion: conditions for success
368(3)
9.5 Bibliography
371(2)
Author Biographies 373(2)
List of Authors 375(2)
Index 377
Dominique Luzeaux has been employed by the Ministry of Defense for over 20 years. He was Director of the Complex System Engineering Department from 2002 to 2004, and Chief Information Officer from 2005 to 2007. He is currently Deputy Director of the service in charge of the C4ISR (Computerized Command, Control, Communications, Intelligence, Surveillance and Reconnaissance) programs. He has written over 60 articles in international conferences and journals, and teaches robotics, theoretical computer science and system engineering at graduate level.
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