Theoretical and numerical methods uses as design tool for an aircraft : application on three real-world configurations
Bibliographic record
Abstract
The mathematical models needed to represent the various dynamics phenomena have been conceived in many disciplines related to aerospace engineering. Major aerospace companies have developed their own codes to estimate aerodynamic characteristics and aircraft stability in the conceptual phase, in parallel with universities that have developed various codes for educational and research purposes. \n \nThis paper presents a design tool that includes Derivatives code, the new weight functions method and the continuity algorithm. FDerivatives code, developed at the LARCASE laboratory, is dedicated to the analytical and numerical calculations of the aerodynamic coefficients and their corresponding stability derivatives in the subsonic regime. It was developed as part of two research projects. The first project was initiated by CAE Inc. and the Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ), and the second project was funded by NATO in the framework of the NATO RTO AVT–161 « Assessment of Stability and Control Prediction Methods for NATO Air and Sea Vehicles” program. Presagis gave the « Best Simulation Award" to the LARCASE laboratory for FDerivatives and data FLSIM applications. The new method, called the weight functions method, was used as an extension of the former project. Stability analysis of three different aircraft configurations was performed with the weight functions method and validated for longitudinal and lateral motions with the root locus method. The model, tested with the continuity algorithm, is the High Incidence Research Aircraft Model (HIRM) developed by the Swedish Defense Research Agency and implemented in the Aero-Data Model In Research Environment (ADMIRE).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".