Bibliographic record
Abstract
The main objectives in this thesis was to investigate the effect of oblique trawl board crossings, increased trawl board added mass due to seabed proximity and the effect of a more rectangular trawl board geometry. In addition a new hydrodynamic load model which handles the seabed proximity and forward speed in a more consistent way was examined.\n\nAll simulations in this thesis are performed by means of the computer software SIMLA. A brief description of methods applied in SIMLA and nonlinear finite element analysis is therefore included. The thesis contains also a chapter which describes trawling concepts and trawl boards used in Norwegian waters.\n\nDesign loads from trawl gears on subsea pipelines are nowadays based on recommendations from the DNV-RPF111 code. Simulation models with a 4500 kg polyvalent trawl board were established to verify the DNV recommendations for free spans of height 0 m and 1 m.\n\nThe simulations demonstrated that increasing trawl board added mass due to seabed proximity did not have any influence on neither pull-over loading nor pipeline response.\n\nThe effect of a rectangular trawl board geometry was most pronounced for a span height of 0 m because the duration increased by 0.5 s and the horizontal pull-over force was kept constant throughout the pull-over. A slighty larger pull-over loading compared to the polyvalent board was observed for a span height of 1 m.\n\nOblique trawl board crossings were examined for 6 different hit angles. The major finding was that a perpendicular crossing did not predict the largest pull-over load. On a general basis the simulations for a span height of 1 m underpredicted maximum pull-over force, duration and pipeline displacement compared to the DNV-RP-F111 recommendations. The 0 m span height simulations indicate that DNV predicts a different shape of the load time history and is slightly nonconservative in terms of maximum pull-over load.\n\nThe new hydrodynamic load model which includes the effect of forward speed and seabed proximity was used to simulate a perpendicular trawl board crossing. Here the span height of 0 m indicated that the DNV-RP-F111 code is nonconservative in terms of the pull-over load. The simulation for a span height of 1 m was however in very good agreement with the DNV-RP-F111 code in terms of duration and horizontal pull-over load. Therefore it is recommended that future simulations are based on the new hydrodynamic load model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".