Development of a High-Fidelity Simulation Model for a Research Environment
Bibliographic record
Abstract
During aircraft development, mathematical models are elaborated from our knowledge of fundamental physical laws. Those models are used to gain knowledge in order to make the best decisions at all development stages. Depending on the application, different models can be used to describe, in one way or another, the aircraft behavior. The goal of this paper is to develop a high-fidelity aircraft simulation model that is exceptionally capable, flexible and responsive to the needs of the researchers. The proposed model includes nonlinear aerodynamic coefficients, a generic engine model and a complete autopilot with auto-landing. The simulation model has been designed to help researchers develop and validate new algorithms for trajectory optimization, control design, stability analysis and parameter estimation. To make it easy to use, the simulation model also includes algorithms for stability and control analysis. Methodologies based on Nelder-Mead's optimization algorithm with a friendly user interface have been developed, allowing the trimming and linearizing of an aircraft's model for any flight condition and any configuration. Similarly, the simulation model includes a flight control system (FCS) and a complete autopilot (AP), allowing aircraft to follow a specific trajectory. The FCS and the AP have been designed and tuned using a modified Genetic Algorithm and the Particle Swarm Optimization algorithm. A level D flight simulator of the Cessna Citation X was used to validate the proposed methodology. The results show that the simulation model presented in this paper is accurate and could be further used to analyze the business aircraft Cessna Citation X's behavior. The simulation model could also be adapted for its use on other aircrafts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".