Instrumentation of an offshore platform model for set-down operation
Bibliographic record
Abstract
The paper describes the instrumentation used for the measurements of various parameters of interest for a gravity based structure type offshore platform during its set-down model tests. The tests were conducted at the wave basin facility of National Research Council of Canada (NRC). The parameters of interest included motions of the model in six degrees of freedom, parameters describing the environmental conditions such as wave elevations on selected locations in the wave basin, inflow rate of the ballast water and the water levels in the ballast tanks as a function of time, all synchronized. The complexity of the instrumentation arose due to the need to model both external dynamics, i.e. motions, and internal dynamics, i.e. the dynamics of the ballasting operation. The set-down operation was simulated in different wave conditions. During the tests the motions of the model as it was lowered to the sea floor by the ballasting operation were measured by two different systems. The first one is a motion capture system, which uses cameras and associated software to determine the motions of the model. The second system consisted of an array of accelerometers and digital inclinometers installed inside the model. During the set-down tests the flow into the central compartment inside the model for ballasting operation was controlled by a peristaltic pump. It allowed start/stop/pause on command and ease of change of flow rate. Two associated parameters were recorded by the data acquisition system (DAS): pump-on-off and flow rate. The water collected in the central compartment was eventually distributed into the surrounding ballast compartments during operation. Each compartment was instrumented to record the water levels as a function of time. Synchronization of the data enabled locating and investigating specific events recorded by the DAS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".