Fatigue damage from dynamic ice action - The FATICE project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it