Determination of Maneuvering Force Coefficients for a Destroyer Model with OpenFOAM
Bibliographic record
Abstract
_ Ship maneuvering performance can be predicted using various methods, including physical model tests, rapid simulations using coefficient-based forces, and high-fidelity computational fluid dynamics. This article considers the application of computational fluid dynamics for the prediction of hull force coefficients to be used as inputs to rapid maneuvering simulations. The open-source software OpenFOAM was used to simulate forces on the destroyer model DTMB 5415 for steady drift, oscillatory pure sway motion, and oscillatory yaw motion. Recommendations are provided regarding best practices in a number of areas, including domain size, mesh refinement, time step size, and turbulent modeling. Predicted forces for steady drift angles up to 12 degrees are typically within 10%of experimental values. For oscillatory sway and yaw motions, predicted forces are typically within 25%of experimental values. Keywords maneuvering; OpenFOAM; best modeling practices
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".