Model-Based Systems Engineering Methodology for Implementing Networked Aircraft Control System on Integrated Modular Avionics – Environmental Control System Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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 &amp; 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.</div></div>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it