Fatigue damage from dynamic ice action - The FATICE project
Bibliographic record
Abstract
In the FATICE project we have addressed the fatigue damage on fixed offshore structures exposed to drifting ice. This is an important challenge in the development of energy production from offshore wind in the Baltic and involves at least five element: a) define ice statistics, b) predict the structural response (ice-structure interaction simulations), c) estimate the fatigue damage and d) carry out scale-model tests. We have used the Copernicus database and simple analytical equations to define the large-scale ice statistics and studied down-scaling to structural scale by comparing with ice load data on the Norströmsgrund lighthouse (LOLEIF and STRICE data). The VANILLA model allows for ice-structure interaction simulations and has been validated against the full-scale LOLEIF and STRICE data and against the model-scale ice in HSVA. The fully coupled and the traditional methods are compared. In the fatigue estimations studies the assumption of linear damage accumulation is challenged and load combinations from wave, wind and ice studied by assessing simulated time-series of the different loads. The main results is that sea ice cause the higher loads than wind and waves do, but the cumulative frequency of ice loads is much smaller than for wind and waves. The traditional model-scale ice tends to be too soft and/or too viscous so that a realistic breaking pattern combined with realistic force-time series is not been obtained for large aspect ratios. HVA has developed a crushing model ice (ICMI) in which the ice crystals are larger and the texture more uniform.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".