pyACDT: An Object-Oriented Framework for Aircraft Design Modelling and Multidisciplinary Optimization
Bibliographic record
Abstract
We present pyACDT, an object-oriented design framework that facilitates the definition, analysis, and optimization of aircraft concepts. Based on a strong emphasis on object-oriented development, the proposed implementation provides a flexible, efficient and portable architecture where concepts can be rapidly modelled, analyzed on multiple levels of fidelity, and optimized on a monolithic or multi-disciplinary fashion. The architecture top layer is is programmed in Python which allows for the bottom layers a seamless interfacing with codes developed in other programming languages such as C, C++, and Fortran. In this paper we described the development criteria and implementation of pyACDT to provide an scalable, extensible, and reusable framework. This allow designers to focus on the design aspects and the optimization of the aircraft concept they are developing, rather than on the implementation and integration details to accomplished such task.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".