Evaluation of Hydrodynamic Loads on a Concrete Gravity-Based Offshore Structure in Extreme Waves
Bibliographic record
Abstract
Abstract The oil and gas companies extended their activities in the offshore area of the North Seas. The presence of extreme environmental waves imparts extra risks to the operations of the offshore fixed and floating structures in these areas. The operation and safety of these offshore structures are considerably disturbed during the propagation of large waves over the region. The Newfoundland coast experienced very high waves during an intense ever storm in 2018 that brought waves with very large wave heights at various locations. This paper reports the loadings of such high waves on the shaft of a Gravity-Based Structure (GBS). A 3D non-linear dispersive mass, momentum, and energy model (MME) with second-order in wave amplitudes, and OrcaFlex™, a commercial numerical tool are used for the simulation of loads on the structure. The 3D numerical model describes the characteristics of the wavefield in terms of mass, momentum, and energy flux conservation equations. OrcaFlex™ is a 3D non-linear time-domain finite element implicit and explicit software that uses lumped mass elements to simplify equations, has diffraction capability, and makes the computation efficient. In the simulations, parameters for incident wave conditions are varied systematically to study various cases and data comparisons between the two numerical simulators are made. The geometry of the GBS was kept constant. The water depth is assumed to be 80m and the shaft length is 95m with a diameter of 30m. The simulation is carried out for 3 hours in each case. New data and information that would be produced from this work are important for possible use in the design method of a GBS and thus increase structural and operational safety exclusively in the harsh environment or in the existence of freak waves.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".