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Record W6945046506 · doi:10.22075/macs.2022.27206.1405

Maximization of Fundamental Frequency of Composite Stiffened Hypar Shell with Cutout by Taguchi Method

2023· article· en· W6945046506 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsHeritage College
Fundersnot available
KeywordsTaguchi methodsShell (structure)Parametric statisticsComposite numberMaximizationFinite element methodVibrationEnhanced Data Rates for GSM EvolutionDegree (music)

Abstract

fetched live from OpenAlex

Composite shells find extensive application in modern civil, aerospace, and marine structures. In order to avoid resonance, such load-carrying shells need to be optimized from a frequency perspective. Composite shell structures often include cutouts for different functional requirements. Obtaining the best combination of design variables like degree of orthotropy, ply orientation, shallowness of the shell, and eccentricity of cutout of laminated shells leads to a problem of combinatorial optimization. This article attempts a numerical study of the free vibration response of composite stiffened hypar shells with cutout using finite element procedure and optimization of different parametric combinations based on the Taguchi approach. Numerical investigations are carried out following the L27 Taguchi design with four design factors, viz., fiber orientation, width/thickness factor of shell, degree of orthotropy, and position of the cutout for different edge constraints. For different shell boundaries considered here, the width/thickness factor emerges as the most influencing factor followed by a degree of orthotropy. The optimum parametric combination for the maximum fundamental frequency of cutout borne stiffened hypar shell is obtained from the analysis.

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 score0.143
Threshold uncertainty score0.869

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.136
GPT teacher head0.492
Teacher spread0.356 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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