Machine Learning Prediction of the Morison Equation Coefficients
Bibliographic record
Abstract
Abstract Prediction of forces on cylindrical structures in marine environments such as jacket platforms and monopiles typically relies on the Morison equation. The Morison equation relies on two coefficients to accurately predict the magnitude of the inertial and drag forces experienced by the structure. These coefficients are sensitive to the diameter and the external shape of the cylinders. Both of these factors change in the presence of biocolonization through an increase in diameter and in macro-roughness. This article details how machine learning can be used to estimate these coefficients in a number of scenarios and demonstrates the applicability through experiments in a wave basin. Several machine learning methods are compared. The approach allows for improved accuracy of force estimates when assessing loads on marine structures, especially for scaled testing in wave basins, where progression of technological readiness is typically done. The benefit of this approach, as opposed to the current binary approach of rough or smooth structures, is that we can examine the evolution of force over time allowing for improved estimates of fatigue lifetimes or improved maintenance cycles.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".