Collaborative CFD Exercise for a Submarine in a Steady Turn
Bibliographic record
Abstract
The application of viscous-flow solvers to calculate the forces on ship hulls in oblique motion has been studied for a long time. However, only a few researchers have published work in which the flow around ships in steady turns was studied in detail. To predict ship manoeuvres, an accurate prediction of the loads due to rotational motion is also required. In a collaborative CFD exercise, the Submarine Hydrodynamics Working Group (SHWG) performed calculations on the bare hull DARPA SUBOFF submarine to investigate the capability of RANS viscous-flow solvers to predict the flow field around the hull and the forces and moments for several steady turns. In the study, different commercial as well as bespoke flow solvers were used, combined with different turbulence models and grid topologies. The work is part of a larger study aiming to improve the knowledge and understanding of underwater vehicle hydrodynamics. In this paper, the results of the exercise will be presented. For several cases, verification studies are done to estimate the uncertainties in the results. Flow fields predicted by the different members of the SHWG are compared and the influence of the turbulence model will be discussed. Additionally, the computed forces and moments as a function of the drift angle during the steady turns will be validated. It will be demonstrated that using sufficiently fine grids and advanced turbulence models without the use of wall functions will lead to accurate prediction of both the flow field and loads on the hull.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".