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Record W4283208372 · doi:10.2514/6.2022-3936

Free-Size Optimization of a Stiffened Panel Using Equivalent Radiated Power

2022· article· en· W4283208372 on OpenAlexaff
Luke Crispo, Wesley Dossett, Adam McKenzie, Ilyong Kim

Bibliographic record

VenueAIAA AVIATION 2022 Forum · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsFuselageEffective radiated powerSound powerAcousticsBroadbandPower (physics)Power optimizationReduction (mathematics)Range (aeronautics)Computer scienceEngineeringSound (geography)Electrical engineeringStructural engineeringAerospace engineeringTelecommunicationsPhysicsMathematicsAntenna (radio)

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2022-3936.vid Acoustic performance is not typically considered during the structural design of an aircraft fuselage, however the noise transmitted by the fuselage during flight can contribute to passenger discomfort. Structural-acoustic optimization approaches have been presented in research, but there is limited implementation in commercial design optimization software. This work investigates equivalent radiated power optimization in Altair OptiStruct as a means of indirectly minimizing radiated sound power over a broadband frequency range. Radiated sound power theory is reviewed and a methodology is presented for free-size optimization for equivalent radiated power. The proposed approach is applied to the design of chemical-milled pockets of a stiffened panel, ultimately achieving a small reduction in sound power compared to five other pocket designs. Further exploration is needed to conclude if equivalent radiated power optimization can achieve a meaningful reduction in sound power.

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)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.240
Teacher spread0.217 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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Same venueAIAA AVIATION 2022 ForumSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207