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Simulation and Modeling of Systems of Systems

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Introduction
  5. Chapter 1: Simulation: History, Concepts, and Examples
    1. 1.1. Issues: simulation, a tool for complexity
      1. 1.1.1. What is a complex system?
      2. 1.1.2. Systems of systems
      3. 1.1.3. Why simulate?
        1. 1.1.3.1. Simulating complexity
        2. 1.1.3.2. Simulation for cost reduction
        3. 1.1.3.3. Simulation of dangerous situations
        4. 1.1.3.4. Simulation of unpredictable or non-reproducible situations
        5. 1.1.3.5. Simulation of the impossible or prohibited
      4. 1.1.4. Can we do without simulation?
    2. 1.2. History of simulation
      1. 1.2.1. Antiquity: strategy games
      2. 1.2.2. The modern era: theoretical bases
      3. 1.2.3. Contemporary era: the IT revolution
        1. 1.2.3.1. Computers
        2. 1.2.3.2. Flight simulators and image generation
        3. 1.2.3.3. From simulation to synthetic environments
    3. 1.3. Real-world examples of simulation
      1. 1.3.1. Airbus
      2. 1.3.2. French defense procurement directorate
    4. 1.4. Basic principles
      1. 1.4.1. Definitions
        1. 1.4.1.1. System
        2. 1.4.1.2. Model
        3. 1.4.1.3. Simulation
      2. 1.4.2. Typology
        1. 1.4.2.1. Levels of granularity
        2. 1.4.2.2. Architecture
        3. 1.4.2.3. Uses
        4. 1.4.2.4. Other classifications
    5. 1.5. Conclusion
    6. 1.6. Bibliography
  6. Chapter 2: Principles of Modeling
    1. 2.1. Introduction to modeling
    2. 2.2. Typology of models
      1. 2.2.1. Static/dynamic
      2. 2.2.2. Deterministic/stochastic
      3. 2.2.3. Qualities of a model
        1. 2.2.3.1. Simplicity
        2. 2.2.3.2. Accuracy
        3. 2.2.3.3. Validity
        4. 2.2.3.4. Efficiency
    3. 2.3. The modeling process
      1. 2.3.1. Global process
      2. 2.3.2. Formulation of the problem
      3. 2.3.3. Objectives and organization
      4. 2.3.4. Analysis of the system
        1. 2.3.4.1. Static system analysis
        2. 2.3.4.2. Dynamic system analysis
      5. 2.3.5. Modeling
      6. 2.3.6. Data collection
        1. 2.3.6.1. Destination of data
        2. 2.3.6.2. Data sources
        3. 2.3.6.3. Data verification and validation
      7. 2.3.7. Coding/implementation
        1. 2.3.7.1. Choice of architecture
        2. 2.3.7.2. Non-transparency of material
        3. 2.3.7.3. Program quality
      8. 2.3.8. Verification
      9. 2.3.9. Validation
        1. 2.3.10. Execution
        2. 2.3.11. Use of results
        3. 2.3.12. Final report
        4. 2.3.13. Commissioning/capitalization
    4. 2.4. Simulation project management
    5. 2.5. Conclusion
    6. 2.6. Bibliography
  7. Chapter 3: Credibility in Modeling and Simulation
    1. 3.1. Technico-operational studies and simulations
    2. 3.2. Examples of technico-operational studies based on simulation tools
      1. 3.2.1. Suppression of aerial defenses
      2. 3.2.2. Heavy helicopters
    3. 3.3. VV&A for technico-operational simulations
      1. 3.3.1. Official definitions
      2. 3.3.2. Credibility
      3. 3.3.3. Key players in the domain
        1. 3.3.3.1. DMSO
        2. 3.3.3.2. International organizations
        3. 3.3.3.3. Development of VV&A methodology in France
    4. 3.4. VV&A issues
      1. 3.4.1. Elements concerned
        1. 3.4.1.1. Conceptual models
        2. 3.4.1.2. Computerized models
        3. 3.4.1.3. Simulations and federations of simulations
        4. 3.4.1.4. Data
        5. 3.4.1.5. Scenarios
      2. 3.4.2. Verification and validation techniques
        1. 3.4.2.1. Informal techniques
        2. 3.4.2.2. Static techniques
        3. 3.4.2.3. Dynamic techniques
        4. 3.4.2.4. Formal techniques
          1. 3.4.2.4.1. Specification
          2. 3.4.2.4.2. Verification
          3. 3.4.2.4.3. Validation
      3. 3.4.3. VV&A approaches
        1. 3.4.3.1. SW-CMM and ISO approaches
          1. 3.4.3.1.1. SW-CMM
          2. 3.4.3.1.2. ISO 9000
        2. 3.4.3.2. RPG on VV&A (DMSO)
        3. 3.4.3.3. Balci’s evaluation (certification) process
        4. 3.4.3.4. ITOP WGE 7.2
        5. 3.4.3.5. Generalized V&V process (D. Brade)
        6. 3.4.3.6. REVVA
        7. 3.4.3.7. V&V guide for the ASCI (Sandia NL): PIRT program
          1. 3.4.3.7.1. Origins
          2. 3.4.3.7.2. Hierarchical decomposition
          3. 3.4.3.7.3. Identification of important physical phenomena: the PIRT method
        8. 3.4.3.8. Hierarchical validation process: MIRT
      4. 3.4.4. Responsibilities in a VV&A process
      5. 3.4.5. Levels of validation
      6. 3.4.6. Accreditation
    5. 3.5. Conclusions
      1. 3.5.1. Validation techniques
      2. 3.5.2. Validation approaches
      3. 3.5.3. Perspectives
    6. 3.6. Bibliography
  8. Chapter 4: Modeling Systems and Their Environment
    1. 4.1. Introduction
    2. 4.2. Modeling time
      1. 4.2.1. Real-time simulation
      2. 4.2.2. Step-by-step simulation
      3. 4.2.3. Discrete event simulation
      4. 4.2.4. Which approach?
      5. 4.2.5. Distributed simulation
    3. 4.3. Modeling physical laws
      1. 4.3.1. Understanding the system
      2. 4.3.2. Developing a system of equations
      3. 4.3.3. Discrete sampling of space
      4. 4.3.4. Solving the problem
    4. 4.4. Modeling random phenomena
      1. 4.4.1. Stochastic processes
      2. 4.4.2. Use of probability
      3. 4.4.3. Use of statistics
      4. 4.4.4. Random generators
      5. 4.4.5. Execution and analysis of results of stochastic simulations
    5. 4.5. Modeling the natural environment
      1. 4.5.1. Natural environment
      2. 4.5.2. Environment databases
      3. 4.5.3. Production of an SEDB
      4. 4.5.4. Quality of an SEDB
      5. 4.5.5. Coordinate systems
      6. 4.5.6. Multiplicity of formats
    6. 4.6. Modeling human behavior
      1. 4.6.1. Issues and limitations
      2. 4.6.2. What is human behavior?
      3. 4.6.3. The decision process
      4. 4.6.4. Perception of the environment
      5. 4.6.5. Human factors
        1. 4.6.5.1. External moderators
        2. 4.6.5.2. Internal states
      6. 4.6.6. Modeling techniques
        1. 4.6.6.1. Artificial neural networks
        2. 4.6.6.2. Multi-agent systems
        3. 4.6.6.3. Rule-based systems
        4. 4.6.6.4. Fuzzy logic
        5. 4.6.6.5. Finite state automatons
        6. 4.6.6.6. Bayesian networks
        7. 4.6.6.7. Evolutionary algorithms
      7. 4.6.7. Perspectives
    7. 4.7. Bibliography
  9. Chapter 5: Modeling and Simulation of Complex Systems: Pitfalls and Limitations of Interpretation
    1. 5.1. Introduction
    2. 5.2. Complex systems, models, simulations, and their link with reality
      1. 5.2.1. Systems
      2. 5.2.2. Complexity
      3. 5.2.3. The difficulty of concepts: models, modeling, and simulation
        1. 5.2.3.1. Representation
        2. 5.2.3.2. Particular language
        3. 5.2.3.3. Phenomenon
    3. 5.3. Main characteristics of complex systems simulation
      1. 5.3.1. Non-linearity, the key to complexity
        1. 5.3.1.1. Decomposition and reconstruction versus irreversibility
        2. 5.3.1.2. Local to global transition versus chaos
        3. 5.3.1.3. Predictability versus unpredictability
        4. 5.3.1.4. Sensitivity to disturbances
      2. 5.3.2. Limits of computing: countability and computability
      3. 5.3.3. Discrete or continuous models
    4. 5.4. Review of families of models
      1. 5.4.1. Equational approaches
      2. 5.4.2. Computational approaches
      3. 5.4.3. Qualitative phenomenological approaches
        1. 5.4.3.1. Scaling laws
        2. 5.4.3.2. Application of catastrophe theory
      4. 5.4.4. Qualitative structuralist approach: application of category theory
    5. 5.5. An example: effect-based and counter-insurgency military operations
    6. 5.6. Conclusion
    7. 5.7. Bibliography
  10. Chapter 6: Simulation Engines and Simulation Frameworks
    1. 6.1. Introduction
    2. 6.2. Simulation engines
      1. 6.2.1. Evolution of state variables
      2. 6.2.2. Management of events and causality
      3. 6.2.3. Simulation modes
        1. 6.2.3.1. Continuous simulation
        2. 6.2.3.2. Discrete simulation
        3. 6.2.3.3. Mixed simulation
      4. 6.2.4. Example
        1. 6.2.4.1. Continuous approach
        2. 6.2.4.2. Discrete approach
        3. 6.2.4.3. Analytical approach
    3. 6.3. Simulation frameworks
      1. 6.3.1. Some basic points on software engineering
        1. 6.3.1.1. Validity
        2. 6.3.1.2. Reliability
        3. 6.3.1.3. Scalability
        4. 6.3.1.4. Reusability
        5. 6.3.1.5. Compatibility
        6. 6.3.1.6. Efficiency
        7. 6.3.1.7. Portability
        8. 6.3.1.8. Verifiability
        9. 6.3.1.9. Integrity
        10. 6.3.1.10. Ease of use
      2. 6.3.2. Frameworks
      3. 6.3.3. Obstacles to framework use
        1. 6.3.3.1. Organization
        2. 6.3.3.2. Human factors
        3. 6.3.3.3. Diversity of needs
        4. 6.3.3.4. Legacy models
        5. 6.3.3.5. Resistance to change
      4. 6.3.4. Detailed example: ESCADRE
        1. 6.3.4.1. Historical overview
        2. 6.3.4.2. Architecture
        3. 6.3.4.3. Basic concepts
        4. 6.3.4.4. Practical example: the Air application
        5. 6.3.4.5. After ESCADRE: DirectSim
        6. 6.3.4.6. Beyond the framework
    4. 6.4. Capitalization of models
    5. 6.5. Conclusion and perspectives
    6. 6.6. Bibliography
  11. Chapter 7: Distributed Simulation
    1. 7.1. Introduction
      1. 7.1.1. The principle
      2. 7.1.2. History of distributed simulations
      3. 7.1.3. Some definitions
        1. 7.1.3.1. Distributed simulation system
        2. 7.1.3.2. Synthetic environment
        3. 7.1.3.3. “Federation” and “federates”
        4. 7.1.3.4. Entities, objects, attributes, interactions, and parameter
      4. 7.1.4. Interoperability in simulation
      5. 7.1.5. Standardization
      6. 7.1.6. Advantages and limitations of distributed simulation
      7. 7.1.7. Other considerations
        1. 7.1.7.1. Visualization tools
        2. 7.1.7.2. Computer-generated forces
        3. 7.1.7.3. Various tools
    2. 7.2. Basic mechanisms of distributed simulation
      1. 7.2.1. Some key principles
      2. 7.2.2. Updating attributes
      3. 7.2.3. Interactions
      4. 7.2.4. Time management
      5. 7.2.5. Dead reckoning
      6. 7.2.6. Multi-level modeling
      7. 7.2.7. Section conclusion
    3. 7.3. Main interoperability standards
      1. 7.3.1. History
      2. 7.3.2. HLA
        1. 7.3.2.1. Principles of HLA
        2. 7.3.2.2. “Architecture” and “high level”
        3. 7.3.2.3. Functional description and associated vocabulary
          1. 7.3.2.3.1. Federates
          2. 7.3.2.3.2. RTI
          3. 7.3.2.3.3. The normalized interface
        4. 7.3.2.4. The HLA standard
        5. 7.3.2.5. Advantages and drawbacks
      3. 7.3.3. DIS
        1. 7.3.3.1. Principles
      4. 7.3.4. TENA
        1. 7.3.4.1. Main principles
        2. 7.3.4.2. Objectives
        3. 7.3.4.3. Main characteristics
        4. 7.3.4.4. Conclusion on TENA
      5. 7.3.5. The future of distributed simulation: the LVC AR study
      6. 7.3.6. Other standards used in distributed simulation
    4. 7.4. Methodological aspects: engineering processes for distributed simulation
      1. 7.4.1. FEDEP
      2. 7.4.2. SEDEP
      3. 7.4.3. DSEEP
    5. 7.5. Conclusion: the state of the art: toward “substantive” interoperability
    6. 7.6. Bibliography
  12. Chapter 8: The Battle Lab Concept
    1. 8.1. Introduction
    2. 8.2. France: Laboratoire Technico-Opérationnel (LTO)
      1. 8.2.1. Historical overview
      2. 8.2.2. Aims of the LTO
      3. 8.2.3. Principles of the LTO
      4. 8.2.4. Services of the LTO
      5. 8.2.5. Experimental process
      6. 8.2.6. Presentation of an experiment: PHOENIX 2008
      7. 8.2.7. Evaluation and future perspectives of the LTO
    3. 8.3. United Kingdom: the Niteworks project
    4. 8.4. Conclusion and perspectives
    5. 8.5. Bibliography
  13. Chapter 9: Conclusion: What Return on Investment Can We Expect from Simulation?
    1. 9.1. Returns on simulation for acquisition
    2. 9.2. Economic analysis of gains from intelligent use of simulations
      1. 9.2.1. Additional costs of the SBA
      2. 9.2.2. Additional costs from unexpected events or bad planning
    3. 9.3. Multi-project acquisition
    4. 9.4. An (almost) definitive conclusion: conditions for success
    5. 9.5. Bibliography
  14. Author Biographies
  15. List of Authors
  16. Index