Rotational Translational Movement Imposition for Macroelements
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".