Vertical Penetration of Offshore Pipelines: A Comparative Study Between Finite Element and Finite Volume Methods
Bibliographic record
Abstract
Deepwater surface laid pipelines generally penetrate a fraction of their diameter into the seabed. The near surface penetration behaviour of steel catenary risers (SCRs) is equally important in offshore oil and gas developments. Theoretical, physical and numerical investigations have been performed to understand pipeline–soil interaction during vertical penetration. The large deformation finite element (LDFE) modeling is a recent and advanced tool among different numerical modeling techniques. The authors of this study simulated the penetration of pipeline using Abaqus CEL Finite Element (FE) software [1]. They also developed a numerical modeling technique based on finite volume approach using ANSYS CFX [2] and showed some of its advantages. However, in that study an ideal soil (i.e. no softening or strain rate effects on undrained shear strength) was used. Strain rate and softening have significant effects on penetration behaviour and therefore in this study a numerical technique has been developed to incorporate these effects in ANSYS CFX. Comparison of the results shows that ANSYS CFX can also model the penetration behaviour. Moreover, ANSYS CFX has some advantages including low computational time, modeling of suction and pipeline–soil–water interaction. A parametric study is also presented to provide more insights into the pipeline–soil–water interaction.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".