Development of a High-Fidelity Simulation Model for a Research Environment
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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