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9.3.3 Simulation‐Based Design Using SysML Part 2: Celebrating Diversity by Example
Author(s) -
Peak Russell S.,
Burkhart Roger M.,
Friedenthal Sanford A.,
Wilson Miyako W.,
Bajaj Manas,
Kim Injoong
Publication year - 2007
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2007.tb02965.x
Subject(s) - systems modeling language , executable , computer science , programming language , modular design , software engineering , component (thermodynamics) , modeling language , representation (politics) , benchmark (surveying) , systems engineering , unified modeling language , engineering , software , physics , geodesy , politics , political science , law , thermodynamics , geography
These two companion papers present foundational principles of parametrics in OMG SysML™ and their application to simulation‐based design. Parametrics capabilities have been included in SysML to support integrating engineering analysis with system requirements, behavior, and structure models. This Part 2 paper walks through SysML models for a benchmark tutorial on analysis templates utilizing an airframe system component called a flap linkage. This example highlights how engineering analysis models, such as stress models, are captured in SysML, and then executed by external tools including math solvers and finite element analysis solvers. We summarize the multi‐representation architecture (MRA) method and how its simulation knowledge patterns support computing environments having a diversity of analysis fidelities, physical behaviors, solution methods, and CAD/CAE tools. SysML and composable object (COB) techniques described in Part 1 together provide the MRA with graphical modeling languages, executable parametrics, and reusable, modular, multi‐directional capabilities. We also demonstrate additional SysML modeling concepts, including packages, building block libraries, and requirements‐verification‐simulation interrelationships. Results indicate that SysML offers significant promise as a unifying language for a variety of models—from top‐level system models to discipline‐specific leaf‐level models.