Numerical study on removal of offshore wind turbine monopile foundations using hydraulic pressure
Bibliographic record
Abstract
Many offshore wind farms installed in the beginnings of the 90 s and 2000s are approaching the end of their design lifetime, and the need of decommissioning these structures has become imminent. This study introduces a numerical approach for the complete removal of monopiles using hydraulic extraction. The numerical modeling of this study mimics the process of water injection into a pile with a sealed joint at its top, which results in increased internal pressure that ultimately moves the pile upward. A Coupled Eulerian-Lagrangian (CEL) approach within ABAQUS/Explicit is employed in the analysis. Using this approach, water injection process is simulated, and deformations of soil are captured. Moreover, to examine the movement of the pile during the removal process in fully saturated dense sand, modified Mohr-Coulomb (MMC) model is utilized. The MMC model, chosen for its ability to capture the non-linear pre-peak hardening and post-peak softening of dense sand, covers limitations in the conventional Mohr-Coulomb (MC) model. These two tools have been used to analyze pile-soil-water interaction. A parametric study is carried out on water injection rate to assess its effects on extraction rate. Breakout pressure which is required to trigger pile movement is determined for dense sand ( I D = 0.7 ) soil. The trend of the extraction rate based on the water injection rate seen in this numerical study for dense sand is in good agreement with the published experimental results.
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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 | 0.000 |
| 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".