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Record W4315977458 · doi:10.1016/j.jcomc.2023.100349

Aeroelastic flutter of triply periodic minimal surface (TPMS) beams

2023· article· en· W4315977458 on OpenAlexfundno aff
Mithilesh Kurup, Jeyaraj Pitchaimani

Bibliographic record

VenueComposites Part C Open Access · 2023
Typearticle
Languageen
FieldEngineering
TopicComposite Structure Analysis and Optimization
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaNational Institute of Technology Karnataka, Surathkal
KeywordsFlutterAeroelasticityAerodynamicsBoundary value problemBernoulli's principleEuler's formulaMathematical analysisMathematicsPhysicsMechanics

Abstract

fetched live from OpenAlex

This work unveils the linear aeroelastic flutter attributes of functionally graded triply periodic minimal surface (FG-TPMS) beams. The Euler-Bernoulli theory including neutral axis shift effect is used to model the FG-TPMS beams. The functional grading is achieved by varying the wall thickness of unit cells according to power-law form. Analysis is carried out for four TPMS patterns, mainly gyroid, primitive, diamond and IWP, under various boundary conditions. Using Hamilton's principle, governing differential equations are derived whose solutions are obtained numerically using the Ritz method. The mode shapes at various values of aerodynamic pressure have also been evaluated. It can be concluded that the type of pattern, boundary conditions, relative cell density, neutral axis shift effect and gradient index plays a crucial role in the prediction of flutter instability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.029
GPT teacher head0.311
Teacher spread0.283 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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