Computational simulation of ratcheting in dented pipes due to monotonic and cyclic axial loading
Bibliographic record
Abstract
This article presents a computational simulation framework developed to capture the ratcheting response of dented straight pipes. The results are compared against the results obtained from our previously conducted experiments. Ratcheting occurs when a structure is subjected to a primary axial load along with a secondary cyclic load, provided that the applied loads are sufficiently large in magnitude to cause the material to yield. The dented pipes investigated in this study were initially subjected to a monotonic axial compression load, which caused the initiation of small amplitude wrinkles, and were subsequently subjected to an axial cyclic loading regime. A nonlinear finite element analysis, using a combined hardening model, was adopted to model the response of the material during the entire loading regime. The parameters required by the material model were obtained from cyclic tests conducted on representative coupon specimens. The results of the numerical simulations were compared to experimentally obtained data. The results demonstrated that the ratcheting response of dented pipes could be numerically simulated with a reasonable accuracy. The results also revealed that the surface imperfections exerted a very pronounced effect on the ratcheting response of the dented pipes. The computational model was also used to investigate the influence of some key parameters, such as the initial strain level, stress amplitude, mean stress, loading regime, and material hardening properties, on the resulting ratcheting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".