Compositional Variation Considering Diffusion and Convection for a Binary Mixture in a Porous Medium
Bibliographic record
Abstract
In this article we study the compositional variation in a porous cavity having different aspect ratios, accounting for natural convection and for thermal, pressure, and molecular diffusion for a binary mixture. The momentum equation is represented by Darcy's law and is solved numerically together with the energy equation and the species conservation equation using the control-volume scheme. The binary mixture's density and viscosity, as well as molecular, thermal, and pressure diffusion coefficients, vary with temperature, composition, and, pressure. Various thermal boundary conditions are investigated. In the lateral heating case the Soret effect is found to be weak, whereas in the bottom heating condition the Soret effect is more pronounced. Such findings are also evident when both bottom and lateral heating are combined, as in the third case studied in this article. In the presence of pressure diffusion, the competing effect of the thermal and pressure diffusions affects the compositional variation in the cavity. Darcy number variation also plays an important role in the mixture variation in the cavity, because of the formation of convective cells. It is important to note that the Soret effect is dominant when bottom heating is present and therefore should not be neglected.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".