Model-Based Systems Engineering Methodology for Implementing Networked Aircraft Control System on Integrated Modular Avionics – Environmental Control System Case Study
Bibliographic record
Abstract
Integrated modular avionics (IMA) architectures host multiple federated avionics applications on a single platform and provide benefits in terms of size, weight, and power, which, however, leads to increased complexity, especially during the development process. To cope efficiently with the high level of complexity, a novel, structured development methodology is required. This paper presents a model-based systems engineering (MBSE) development approach for the so-called “distributed integrated modular architecture” (DIMA). The proposed methodology adapts the open-source Capella tool, based on the Architecture Analysis & Design Integrated Approach (ARCADIA) methodology, to implement a complete design cycle, starting with requirements captured from the aircraft level to streamline the development, culminating in the integration of an avionics application into an ARINC 653 platform. This paper shows how to address the variability of technology implementations at the aircraft and system levels and how the specification artifacts are efficiently managed and traced from the aircraft to the system to the item level to implement the SAE ARP4754A guidelines. The effectiveness of the methodology is presented via a case study of the integration of an environmental control system (ECS) into aircraft control architecture, illustrated for the cabin pressure control system (CPCS). The guidelines derived are applicable to other aircraft systems. In addition, the presented paper provides important insights into the challenges and advantages of the MBSE process over the traditional paper-based specification process.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".