CATIA V5-Based Parametric Aircraft Geometry Modeler
Bibliographic record
Abstract
Current transport aircraft are mature systems, thus require increased fidelity at the beginning of the design process to allow further optimization. Furthermore, a desire exists to explore unconventional aircraft configurations at the conceptual level. This has motivated the development of a tool which effectively manages the trade-off between high-fidelity levels, flexibility and short turn-around times. This paper presents a CATIA V5-based parametric aircraft geometry modeler developed by Bombardier Aerospace. The aim of the tool is to provide consistent high-fidelity geometric data early in the conceptual aircraft design process. The intended near-term use of the modeler is two-fold: during the early design phase, the modeler computes geometric data such as areas, volumes, ESDU aircraft parameters, etc. In the competitive analysis domain, the tool provides a high-quality three-dimensional model with manageable effort. In both cases, the engineer is presented with a fully parametric three-dimensional CATIA V5 aircraft model. In the medium-term, use by expert departments is envisioned. Moreover, the geometrical data extracted from CATALIST can be saved into a standard database and fed to various analysis codes to compute aircraft performance characteristics. As aircraft models are in native CATIA V5 format, this fits seamlessly into the established design process at Bombardier Aerospace. Additionally, this approach allows critical analyses to be performed earlier in the conceptual design phase. Aircraft exterior and interior modules are in use today along with systems modules and a basic structural representation. An overview of the key features of the tool is presented along with sample applications. Tool usability and modeling quality have been extensively validated with selected regional aircraft and business jets from Bombardier.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".