Model-Based Design Flow Driven by Integrated Modular Avionic Simulations
Bibliographic record
Abstract
The Integrated Modular Avionics (IMA) architecture has been a crucial concern for the aerospace industry in developing more complex systems, while seeking to reduce space, weight and power (SWaP), as well as development, certification and production time. From a software perspective, that objective pushes developers to migrate toward safety critical space and time partitioning environment. However, mainstream commercial real-time operating systems (RTOS) offering such partitioning can be restrictive in early development due to very high licensing costs. That situation is even more striking when considering that low-cost alternatives could instead be used for system modeling and early simulation before acquisition of a target platform. This paper reviews existing low-cost and open-source development environments to propose a novel design flow. The proposed methodology starts with model-based analysis in the AADL modeling language. Then, configuration files and software integration code are generated and executed using the Simulated IMA (SIMA) software from GMV. A case study experiment was created using a Multi-purpose Control and Display Unit (MCDU) communicating with an external Flight Management System (FMS) simulation provided by our industrial partner CMC Electronics. Results show reduction of time for system and partition configurations from hours to seconds, notably by reducing human error. It also proves useful in identifying design flaws in early development as well as facilitating software architectural exploration for integrated modular avionics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".