Description

This two-day short course, based on two semester-long graduate-level systems engineering courses, provides an overview of the use of Modeling and Simulation (M&S) in the Systems Engineering process.  After an introduction of key M&S terms and processes, the course presents an overview of the types of models and simulations used across the phases of the Systems Engineering life cycle, from system needs analysis through system sustainment.  Examples are given for several types of systems, including systems developed under the U.S. Department of Defense (DoD) acquisition process.  The course then provides information on advanced M&S and Digital Engineering topics, including the U.S. DoD Digital Engineering Strategy; the Unified Modeling Language (UML) and the Systems Modeling Language (SysML); interoperable and live-virtual-constructive (LVC) simulation; collaborative environments and asset repositories for Digital Engineering; and modeling the natural and man-made environments.

Who Should Attend:

This short course is intended for systems engineers who are seeking a broad understanding of how models and simulations are used across all phases of the systems engineering process, as well as basic information on evolving and emerging modeling and simulation techniques and processes that support Digital Engineering.  The short course is not intended to provide a “deep dive” into any of the topics discussed, but will provide for a basic understanding of each topic, and will cite resources that participants can access to obtain more detailed information, through an extensive list of references.

Course Outline:

1. Overview of Modeling and Simulation. Definitions and Distinguishing Characteristics; Views and Categories of Models and Simulations; Resolution, Aggregation, and Fidelity; Overview of the Model/Simulation Development Process; Important M&S-Related Processes; M&S as a Professional Discipline
2. 2.      M&S in System Needs and Opportunities Analysis. Needs vs. Opportunities for New or Improved Systems; The U.S. Military Process for Capabilities-Based Assessment; Commercial System Processes; M&S Use in Operational Analysis, Functional Analysis, and Feasibility Determination
3. 3.      M&S in Concept Exploration and Evaluation. Effectiveness Simulations and Their Components; Analyses of Alternatives; Ensuring a “Level Playing Field”; System Effectiveness Simulation Examples
4. 4.      M&S in Design and Development. Range of Engineering Disciplines Needed for System Design and Development Simulations; Simulating Interactions between System Components; Time Management in Simulations Interacting at Run-Time; Examples of Interacting Simulations for Design and Development
5. 5.      M&S in Integration and Test & Evaluation. Simulation Use During Integration; Planning for Use of Models and Simulations During T&E; Simulation Use During Testing; Post-Test Evaluation Using Models and Simulations
6. 6.      M&S in Production and Sustainment. Planning for Use of Models and Simulations During Production; Model and Simulation Use During Production; Systems Operation Simulations; Reliability Modeling, Logistics Simulations, and Ownership Cost Modeling
7. 7.      The U.S. DoD Digital Engineering Strategy. U.S. DoD Definition of Digital Engineering; The Goals of the Digital Engineering Strategy
8. 8.      Modeling Languages: UML and SysML. History of Graphical Modeling Languages; Unified Modeling Language (UML) Diagrams; Systems Modeling Language (SysML) Diagrams
9. Interoperable Simulation - the High Level Architecture (HLA). The History of Interoperable Simulation; Why the HLA is Important for Systems Engineering; Components of the HLA Standard; The Distributed Simulation Engineering and Execution Process (DSEEP)
10. Live-Virtual-Constructive (LVC) Simulation Techniques. Differentiating Live, Virtual, and Constructive Simulations – A Review; Why LVC Simulation Federations Are Important for Systems Engineering; Interoperability Standards for LVC Simulations; Issues in LVC Simulation Federations; The DSEEP Multi-Architecture Overlay and the Federation Execution Agreements Template
11. Collaborative Environments for Digital Engineering. Background: Studies on M&S for System Acquisition; Definition of a Collaborative Environment (CE); Characteristics of a CE; A Reference Model for a CE; Examples of CE Architectures
12. M&S Asset Repositories - Construction and Use. Definitions: Repository, Catalog, and Registry; Issues in the Discovery and Reuse of M&S Assets; Desired Features for Repositories; Metadata (Data About the Data), with an Example; Catalog and Repository Examples; Putting Collaborative Environments and Repositories Together for Digital Engineering
13. Modeling the Natural Environment. Definition of the Natural Environment; Overview of the Air, Ground, Maritime, and Space Environments; Separating the Natural Environment from Sources and Sensors; Issues in Aggregation of Natural Environment Representations; Environmental Modeling Standards: SEDRIS
14. Modeling the Man-Made Environment. Definition of a Man-Made Environment; Distinguishing the Man-Made Environment from the Natural Environment and Friendly/Threat Systems; Some Man-Made Environment Modeling Examples; Man-Made Environment Modeling Standards: Shapefiles
15. The Future of M&S in Systems Engineering. Acquisition M&S Research Areas; Model Based Systems Engineering (MBSE) and Model Based Engineering (MBE); Levels of Interoperability, and Moving from Syntactic to Semantic Interoperability; Simulation Composability

Instructor(s):

James E. Coolahan, Ph.D., is the Chief Technology Officer of Coolahan Associates, LLC, having retired from full-time employment at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) in December 2012 after 40 years of service. He chaired the M&S Committee of the Systems Engineering Division of the National Defense Industrial Association for seven years, and currently teaches courses in M&S for Systems Engineering in the JHU Engineering for Professionals M.S. program. He holds B.S. and M.S. degrees in aerospace engineering from the University of Notre Dame and the Catholic University of America, respectively, and M.S. and Ph.D. degrees in computer science from JHU and the University of Maryland, respectively.