A Numerical Investigation of the Effects of Compositional and Thermal Buoyancy on Transient Plumes in a Porous Layer
Bibliographic record
Abstract
We present a suite of high-resolution numerical model experiments conducted to investigate the effects of varying thermal and compositional buoyancy on the behavior and morphology of plumes in porous media. The calculations model the injection of fluid through a narrow opening into the base of a nonreactive, saturated, porous matrix with interstitial fluid of different temperature and/or solute concentration and are scaled to be comparable with previously published experimental results. Calculations are presented for the case of zero injection velocity (in which case heat and solute diffuse in from the boundary) and for small, nonzero injection velocity. Different combinations of thermal and compositional buoyancies result in various plume structures owing to the fact that solute and heat both diffuse and advect at different rates in porous media. Plumes with dominantly positive thermal buoyancy have large plume heads, while those with dominantly compositional buoyancy lack this feature and propagate more rapidly. When the injected fluid has positive compositional and negative thermal buoyancy, the initial flow spreads laterally along the base of the domain before a narrow straight-sided compositional plume emerges. For cases when the injected fluid has positive thermal buoyancy and negative compositional buoyancy, plumes initially rise upward before a dense solute cap forms, interrupting the flow. For sufficiently large positive thermal buoyancy, this cap breaks down and the flow becomes highly time dependent. The velocities and widths of the plumes are also presented in order to characterize the plumes formed in the different parameter regimes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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.
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