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Record W4295953044 · doi:10.4050/f-0078-2022-1229

Airframe Structural Sizing Automation

2022· article· en· W4295953044 on OpenAlex
Anthony D. Joseph, Anne Koeppel, Robert Daley

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsAirframeSizingAutomationProcess (computing)Iterative and incremental developmentStiffnessComputer scienceEngineeringStructural engineeringSimulationMechanical engineeringAerospace engineeringSoftware engineering

Abstract

fetched live from OpenAlex

Determining the optimum cross section for each primary structural member of an airframe structure has always been an iterative process since changing the stiffness of one member redistributes loads. Each iteration of internal loads calculations with a global aircraft finite element model (GFEM) followed by strength and stability checks results in further cross section changes (sizings) to reduce weight or regain positive margins of safety. The handoffs between tools and the update process for the next iteration is time consuming and has many opportunities for errors. This paper will describe a tool developed at Sikorsky to automatically iterate sizings saving development time and executing more sizing iterations than historically possible which saves weight. The tool can operate on metallic and composite structures. The development time and weight savings is critical to support ever shrinking time to fielding/market for commercial and military models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.202
Teacher spread0.195 · 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