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Record W2898899354 · doi:10.1115/detc2018-85385

Effect of Stiffener Configuration on Bulkhead Modal Parameters

2018· article· en· W2898899354 on OpenAlexafffund
Braden T. Warwick, Chris K. Mechefske, Il Yong Kim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBulkhead (partition)ModalStructural engineeringModal analysisBoundary value problemEngineeringComputer scienceFinite element methodMathematicsMathematical analysisMaterials science

Abstract

fetched live from OpenAlex

Optimization of bulkhead stiffener configuration has been an active area of research over the past decade, but no real practical solutions have been generated. This research investigates bulkhead stiffener configuration on a rudimentary level, by analyzing the modal parameters of three different stiffener configurations. Experimental data was used to validate the computational models of two modified bulkhead stiffener configurations. Operational boundary conditions were then applied to the computational models to assess the modal density of the modified bulkheads within the aircraft engine rotational frequency range. Removal of one horizontal stiffener reduced the overall stiffener mass by 12.2% without generating any modes within 4% of the engine rotational frequency. The inconsistencies of natural frequency changes due to stiffener configuration highlights the difficulty with applying generalized optimization approaches without a thorough understanding of the modes of interest. The results of this work suggest that the fundamental analysis performed herein is necessary to generate a complete understanding of the modal parameters of the bulkhead prior to performing in-depth optimization work.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.238

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)0.0000.000

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 head0.006
GPT teacher head0.229
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2018
Admission routes2
Has abstractyes

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