Numerical Investigation of Vertical Penetration of Steel Catenary Riser Near the Touch Down Zone
Bibliographic record
Abstract
Steel catenary risers (SCR) are widely used in offshore to transport hydrocarbon from the seabed to floating or fixed platforms. The fatigue life of SCR near the touchdown zone (TDZ) is one of the main design concerns because the risers are often subjected to cyclic loading (vertical penetration/uplift, lateral and axial displacements) from various sources of environmental loadings, such as sea waves and currents. Numerical modeling of the penetration and uplift behaviour of an SCR is a challenging task. Most of the models available in the literature for uplift resistance are empirical, which have been developed mainly from the results of physical experiments. In this study, numerical simulation of vertical resistance is presented. Analysis is performed using ANSYS CFX software. Strain-softening and strain-rate dependent undrained shear strength behavior of soft clay sediment has been reported by many researchers. Unfortunately, these models were not available in CFX. Numerical simulations presented in this paper are performed implementing this behavior in CFX. Numerical results are compared with available empirical models. The present CFX modeling explains some mechanisms involved in trench formation and suction development during uplift. Factors affecting uplift resistance such as the size and shape of the trench are also discussed from a parametric study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".