AUTOMATED MARINE PROPELLER GEOMETRY GENERATION OF ARBITRARY CONFIGURATIONS AND A WAKE MODEL FOR FAR FIELD MOMENTUM PREDICTION
Bibliographic record
Abstract
This paper first describes procedures and methodologies to automatically producemarine propeller geometry with optional auxiliary bodies such as nozzles, blockagesand rudders. This process is designed and implemented for a general boundaryelement method (the panel method) to deal with both lifting body and non-lifting bodyflows.The generated geometry is represented by quadrilateral and triangular panels thatcan be used by other mesh generation codes to produce 3D volumetric mesh for CFDwork. The vertices of these generated panels are set so that the normal of the surfacespoints inside the body. The order of the panels and their side indices are aligned fornumerical procedures such as differentiation of the perturbation doublet potential forsurface tangential velocities and Kutta condition at the trailing edge. A DXF fileformat was also implemented as one of the output files that can be used for propellermanufacturing via CNC and for commercial CFD codes that use geometry datainput.Based on the near field wake modeling studies performed by the authors and previousexperimental investigations on far wake turbulent jet measurements, a far wakemodel for a propeller panel method is implemented to enhance the capability ofpredicting the velocities and momentum impact on the risers under a floatingproduction storage off-loading (FPSO) system during operation. This far wake modelconsists of contraction wake (within one propeller diameter downstream), transitionwake (one to two diameters downstream), and inflation wake (two diameters beyond).Near field velocity prediction of this far wake model is validated using previous LDVmeasurement. Further experimental studies are scheduled for LDV/PIV measurementup to 20-diameter downstream.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".