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Record W4317581752 · doi:10.2514/6.2023-0973

Topology Optimization of Stringer and Frame Locations Using Equivalent Radiated Power

2023· article· en· W4317581752 on OpenAlexaff
Wesley Dossett, Adam McKenzie, Luke Crispo, Ilyong Kim

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsQueen's University
Fundersnot available
KeywordsFuselageStringerTopology optimizationAcousticsStructural engineeringEngineeringSound powerPower (physics)Noise (video)Computer scienceSound (geography)Finite element methodPhysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-0973.vid Fuselage structures transmit vibrations into the cabin and generate sound experienced by passengers and crew. However, acoustics is generally not considered during fuselage structural design. The main source of noise reduction coming from adding damping material to the fuselage. This paper proposes a methodology for incorporating acoustics optimization into the structural design of the fuselage. Equivalent radiated power was used within Altair OptiStruct as an indirect method for minimizing radiated sound power. The frames and stringers were removed from the fuselage and replaced with a 3D design space for topology optimization. Optimization results were used to determine frame and stringer locations. Standard frame and stringer cross-sections were used. The results were compared to a baseline design with standard stiffener spacing.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.013
GPT teacher head0.244
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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Same venueAIAA SCITECH 2023 ForumSame topicTopology Optimization in EngineeringFrench-language works237,207