A semi-implicit method for modeling salinity and temperature in variable-density flow
Bibliographic record
Abstract
In this study, we propose semi-implicit techniques for solving the coupled variable-density model of shallow water flows and transport processes, taking into account temperature and salinity. In the proposed numerical approach, we first compute the water depth and the conservative variables related to temperature and salinity using their governing continuity equations. The obtained values are used in the mixture density source terms which allows us to design semi-implicit discretization techniques for solving the momentum equations. To discretize the flux term, a well-balanced Godunov-type finite volume scheme is used with a suitable linear reconstruction of the system’s variables. The discretization of the mixture density and friction source terms can be used as a stand-alone approach within any other finite volume solver. The performance of the proposed coupled numerical model is tested through a series of numerical examples for modeling flows and transport processes. The developed numerical scheme is positivity-preserving for the computed water depth and salinity. Furthermore, the consistency between the discretizations of the continuity equation and the transport equations of temperature and salinity is satisfied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".