A method to compute ship exhaust plumes with waves and wind
Bibliographic record
Abstract
Abstract Thermal and concentration transport models are implemented in CFDShip‐Iowa version 4.5, a semi‐coupled solver for air/water free surface flow (Int. J. Numer. Meth. Fluids 2008; 58(6):591–624), to investigate the exhaust plume around ship superstructures. An incompressible, variable density approximation is implemented where the density can change in all governing equations due to temperature variations only. The thermal and concentration models are tested for the cases of steady and unsteady flow with thermal and solution transport in a 2D square cavity, and for a 3D thermal plume in an open environment, showing good agreement between computational results and experimental data. To test the method in an extreme motions condition, the exhaust plume of the ONR Tumblehome model DTMB 5613 is studied, showing complicated vortical structures in air including a pair of counter‐rotating vortices downstream of the stack for cross‐flow, and bended bird‐plume shape in the symmetry plane and varying arc‐shape in axial sections both for temperature and NOx concentration fields. Effects of smoke exhaust speed and wind speed on the temperature and concentration distributions are studied. Finally, a smoke downwash computation is performed for a ship free to move in 6 degrees of freedom in a sea state 8 condition. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".