RANS simulation and validation of self propulsion test of a bulkcarrier
Bibliographic record
Abstract
This paper presents numerical simulations of bare-hull resistance, open-water propeller performance, and self-propulsion of a PANAMAX bulk carrier model with a four-bladed fixed-pitch propeller using the RANS solver in the open-source Computational Fluid Dynamics (CFD) package OpenFOAM.The computational domains were generated using Star CCM+ and converted to OpenFOAM format using the ccm26ToFoam utility, except for the self-propulsion study, which was generated using the snappyHexMesh utility in OpenFOAM.Then the numerical results were compared with the available experimental data.The unsteady simulations were run in OpenFOAM and Star CCM+ for the bare hull at F n = 0.178 in order to compare the trim, sinkage, and free surface elevation.Secondly, the open-water hydrodynamic characteristics of the propeller were simulated with the Moving Reference Frame (MRF) and compared with the simulation results using Star CCM+.The OpenFOAM results for the resistance and open-water curve agree quite well with the StarCCM+ solutions and the experimental data.In addition, the predicted trim angles by OpenFOAM and StarCCM+ are very close; however, large discrepancies were found in trim and sinkage in comparison with experimental data.As a result, uncertainty analysis must be performed to understand the source of these discrepancies in the next stage of the study.Finally, a steady-state self-propulsion simulation was conducted for one rotational speed.It was observed that the predicted thrust and total resistance were very close, indicating the self-propulsion point.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".