Validation of the CFD code Flow-3D for the free surface flow around the ships’ hulls
Bibliographic record
Abstract
This paper describes the Computational Fluid Dynamics (CFD) calculations that were completed to model the free surface flow around the ships' hulls. Published experimental data for the DTRC 5415 combatant model is commonly used for validation of numerical codes, as in this study. Simulations were performed using the software Flow-3D, a Reynold's Averaged Navier-Stokes (RANS) solver with structured orthogonal mesh. The verification was based on the examination of the flow around the hull for range of speeds and by comparison of the results for resistance obtained by CFD simulations and by experiments. Additional analysis has been conducted to investigate mesh sensitivity and the implementation of different advection schemes. The second order advection scheme with monotonicity preserving was optimal for the qualitative analysis of the problem under consideration. This study shows that CFD code Flow-3D has a limited capability to resolve the physics of the flow around the hull. The shape of the free surface and wave distributions around the hull corresponds approximately to the experimental observations. For quantitative analysis of ship total resistance, Flow-3D shows a lack of accuracy. It appears that the code does not have the capability to properly resolve boundary layer on the hull and properly predict frictional resistance. The total resistance prediction can be improved by using only dynamic pressure results from Flow-3D and by using some established empirical/experimental approach for estimating frictional resistance. The multi-block grids and the different turbulent models are being used to obtain valid numerical results that are crucial for making sound design decisions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".