Salinity Intrusion Modeling Using Boundary Conditions on a Laboratory Setup: Experimental Analysis and CFD Simulations
Bibliographic record
Abstract
Salinity intrusion is one of the most pressing threats to unconfined coastal aquifers, and its simulation is of great importance for groundwater research and management. This study compared the performances of two computational fluid dynamics (CFD) software applications, ANSYS Fluent 2022 R2 and COMSOL Multiphysics 5.6, in simulating the transport of saltwater in a pilot-scale experimental setup, which was built to recreate two boundary conditions of unconfined aquifers with homogeneous stratigraphy. The experiments were performed until the saline wedge reached a quasi-steady-state condition. Sequential photographs and image analysis were required to record the movement of the saline toe and the saline wedge location. The maximum toe length was achieved under the head-controlled boundary condition, with a toe length of 1.6 m after 7 h of the experiment, and 1.65 m and 1.79 m for the COMSOL and ANSYS Fluent simulations, respectively. The findings evidence that the flux-controlled condition produced a better representation of the saline wedge than the head-controlled condition, indicating good agreement between the CFD simulations and the experimental data. Recommendations for future research include CFD simulations of real coastal aquifers and coupling fluid dynamics with other processes such as land subsidence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".