Ice Loads Acting on a Model Podded Propeller Blade (OMAE2005-67416)
Bibliographic record
Abstract
Abstract With the increase in popularity of podded propulsors and arctic navigation, understanding the interaction between a podded propulsor and ice has become more important. Propeller-ice interaction itself is a complicated process with a high level of uncertainty resulting from the uncertainties associated with the properties of the ice and with the propeller-ice interaction conditions. Model tests provide relatively well-controlled ice properties and interaction conditions to reduce the uncertainties. In order to improve the understanding of this interaction and to develop numerical models of it, a model podded propulsor was used in “Puller” mode, and ice loads were measured on its instrumented blade and propeller shaft. The results of the experiments conducted to simulate the interactions (milling) of the instrumented blade with ice in different operating conditions are reported in this paper. Loads measured during the milling consist of ice milling loads, “inseparable” hydrodynamic loads, and “separable” hydrodynamic loads. The sample results presented here include ice milling and inseparable hydrodynamic loads for various advance coefficients and depths of cut (amount of blade penetration into ice). Some results are compared with existing ice load models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".