Numerical Prediction of Model Podded Propeller-Ice Interaction Loads
Bibliographic record
Abstract
As the interest in arctic shipping and arctic exploration of oil and gas is increasing in recent years, the number of ice class vessels is increasing rapidly. Also the choices for propulsion devices are getting wider and these include podded propulsion systems. This study is a framework for the numerical prediction of the ice interaction loads acting on a podded propeller blade. The results of this study will help us to understand the propeller-ice interaction problem more comprehensively. Several studies for propeller-ice interaction have been carried out in the past few decades. Propeller-ice interaction, however, is a complicated process with a high level of uncertainties due to ice properties, ship operating conditions, and environmental conditions. Full-scale measurements involve high costs. In order to overcome these difficulties, model tests were carried out with model ice in an ice tank. The model tests provide well-controlled ice properties and interaction conditions to reduce the uncertainties. The tests were carried out in the ice tank with scaled down model ice at the National Research Council of Canada’s Institute for Ocean Technology. The ice loads acting on the propeller blade were measured with a six-component force and moment load cell fitted to the shaft and one of the propeller blades. Based on the experimental results, a numerical prediction model was developed to estimate the ice loads on the propeller blade. The numerical prediction is composed of three parts: the hydrodynamic calculations including separable and inseparable hydrodynamic loads, and the ice milling loads calculation. The separable and inseparable hydrodynamic loads can be obtained from clear water and blocked flow respectively. The hydrodynamic calculations were done by a low order panel method. The subroutines for calculating the ice milling loads are implemented into the panel method. The numerical prediction model for ice milling loads is described and the results are compared with those of experiments.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".