Effective mass diffusion and dispersion in random porous media
Bibliographic record
Abstract
In this work, the effect of the stochastic nature of porous medium on porosity, effective diffusion and mass dispersion was investigated. To this end, a methodology to build idealized media composed by solid particles with random size has been introduced. Gaussian and uniform probability distribution functions were employed to design the size and orientation of rectangular cylinders and cubes. Predictions of effective parameters were performed by using numerical procedures based on the volume averaging method. From the porosity and effective diffusion analysis, it was found that a minimum number of particles minimizes fluctuations of predictions. Thus, a minimum sampling size to measure properties was inferred. The minimum number of particles depends on the probability distribution functions and the dimensionality of particles used (cylinders or cubes). The methodology to build porous media also allows the creation of anisotropic media, and its effect is marked over the longitudinal and transverse components of effective diffusivity tensor. But this is not the case for the mass dispersion tensor, where the flow direction is the main cause for anisotropy. In fact, the flow taking place at pore‐scale increases noticeable the randomness of predictions of mass dispersion, specifically when the number of particles or the particle Péclet number are increased. This effect is more significant on the transverse mass dispersion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".