9.3.3 Simulation‐Based Design Using SysML Part 2: Celebrating Diversity by Example
Bibliographic record
Abstract
Abstract 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".