Automated 3D propeller modeling for integration into a shape optimization workflow
Bibliographic record
Abstract
Marine propeller optimization is crucial for enhancingvehicle efficiency and minimizing environmental impact. This paperdemonstrates developing a robust and automated 3D propellermodeling method which can be easily integrated into a shapeoptimization workflow using automated meshing and Reynolds-AveragedNavier-Stokes (RANS) solvers. The goal is to designhigh-performance propellers with improved efficiency and reducedcavitation. By leveraging high-fidelity 3D Computational Fluid Dynamics(CFD) simulations over low-order methods, the approach canenable better wake resolution, account for hull effects, and optimizepropellers with retrofits. The paper demonstrates the automatedmodeling process, validates it with an existing propeller, and outlinesthe optimization workflow. Future efforts will focus on employing themethodology to modify existing propellers, perform multi-objectiveoptimizations, and emphasize the role of high-fidelity CFD-drivenoptimizations in advancing sustainable marine propulsion.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".