Applying model-based system engineering to modelling and simulation requirements for weapon analysis
Bibliographic record
Abstract
The use of Model-Based Systems Engineering (MBSE) to support the definition of requirements and design of modern complex aerospace systems is becoming increasingly accepted. However, MBSE tools and techniques also have many beneficial applications in the definition of requirements for conceptual modelling, and in the design for modelling, simulation, analysis, and implementation of aerospace systems. An MBSE based methodology, known as the Whole-of-System Analytical Framework (WSAF) [1], was developed to provide structure, rigour and traceability to the definition of modelling and simulation requirements for the analysis of weapon and combat system performance. The WSAF is grounded in standard analysis principles and practises but applies MBSE tools and techniques to support the analyst throughout the process [2]. Employing the WSAF, the study definition for the analysis of a weapon system's performance is achieved by deconstructing the systems of interest into their functional components. This activity considers the weapon “kill chain” from initial target detection through to assessing the effect of the weapon [3], and can include the entire combat system (including third party support platforms). Each system's functionality is then systematically analysed in order to identify the questions that must be addressed by a study. The modelling, simulation and analysis requirements necessary to answer those questions are then derived [1] [2]. By functionally defining the whole weapon system kill chain, analysts are able to ensure that their analysis is complete and rigorous, while the MBSE tools provide the required traceability [2]. The WSAF has been applied across the Australian defence domain and is utilised by one of DST Group's partner organisations, Defence Research and Development Canada (DRDC). This paper describes the WSAF methodology using case studies from DST Group and DRDC. Lessons accumulated by both organisations are discussed, covering the application of MBSE tools and techniques to support weapon and combat system modelling, simulation and analysis for over a decade.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".