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Record W2312198328 · doi:10.2514/6.2008-5955

pyACDT: An Object-Oriented Framework for Aircraft Design Modelling and Multidisciplinary Optimization

2008· article· en· W2312198328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultidisciplinary design optimizationMultidisciplinary approachComputer scienceObject-oriented programmingSystems engineeringObject (grammar)EngineeringArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.278
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it