A comparative experimental and multiphysics computational fluid dynamics study of coupled surface–subsurface flow in bed forms
Bibliographic record
Abstract
The use of multiphysics computational fluid dynamics (CFD) approaches to simulate surface–subsurface flow processes is evaluated by comparison with flume experiments on current‐exposed permeable bed forms. The unique experimental data include measurements of the time‐averaged surface water flow velocities, the pressure distribution at the sediment–water interface, and pore water flow paths. The modeling approach first simulates the time‐averaged turbulent flow in the channel with CFD and then uses the predicted pressure distribution at the sediment–water interface to drive a flow and transport model for the sediment. The CFD‐modeled velocity and pressure distribution and transient particle tracks within the sediment agree reasonably well with observations. Differences that exist between observations and simulations mainly concern the eddies in the wake zone downstream of the ripple crests that are slightly shorter than those predicted by the model. This deviation propagates from the surface to the subsurface domain, appearing in the pressure distribution along the bed and, consequently, the subsurface flow patterns. The good representation of general patterns and rates makes multiphysics CFD modeling a powerful and sufficiently accurate tool that can replace measurements for many studies of surface–subsurface processes involving current‐exposed immobile bed forms. The approach can be used for predicting transport processes where they cannot easily be observed, such as in large rivers and coastal systems where boundary conditions such as mean currents and bed forms can be mapped.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".