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Record W4413391840 · doi:10.1115/omae2025-157086

Rotational Translational Movement Imposition for Macroelements

2025· article· en· W4413391840 on OpenAlexaff
Rodrigo Provasi, Clóvis de Arruda Martins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMovement (music)Computer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Finite Macroelements for flexible pipe modeling is a research line under development by the authors. They profit from the geometrical characteristics of the pipes in the formulation, leading to well-behaved contact models with fewer degrees of freedom. Their use makes it possible to simulate the layer interaction. Previous works presented the macroelements that have already been developed: a helical metallic element, an orthotropic Fourier element, and elements to represent the contact between layers, including or not friction. Boundary conditions play a vital role in the structure’s response and must, thus, be correctly modeled to achieve the desired results. They can generally be expressed in applied loads, namely force and moments, or imposed movements, such as displacements and rotations. In numerical models, applying the boundary condition as movements is more manageable and tends to lead to models with an easier convergence. The present paper shows the complementary work of a previously presented one that imposes translational movements in the macroelement model by creating a rigid section. The model for section rotational movements is detailed in the current paper, highlighting the used hypothesis. It also shows the results for the isolated elements and a simplified combination of helix and cylinder using bonded contact, with agreement when compared to commercial software.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.156

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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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