Determination of the effective gas diffusivity of a porous composite medium from the three-dimensional reconstruction of its microstructure
Bibliographic record
Abstract
The present work describes a procedure to calculate the effective diffusivity of a porous composite medium from the three-dimensional reconstruction of its microstructure. We perform Monte Carlo simulations based on the mean-square displacement method on numerical models of composite materials microstructures. First, computations of the effective diffusivity in the bulk diffusion regime account for the effect of the tortuosity of the geometry on gas diffusion. The Bruggeman equation, which is often used in the literature to relate the effective diffusivity to the porosity of the structure, appears to be inaccurate for porosities ε<0.40. A more accurate correlation for this range of porosities is provided based on the results of our simulations. Second, the Bosanquet equation, which accounts for the effect of pore confinement on gas diffusion, is validated provided that the definition of the Knudsen number is based on the appropriate characteristic length. The procedure to calculate this characteristic length is demonstrated for analytical geometries. However, in practice, geometries obtained from experimental measurements are discrete. For discrete geometries, we show the effect of the resolution of the geometry on the accuracy of the calculation of the effective diffusivity and other properties of the porous material. In addition, the tesselation of solid surfaces affects the calculation of the chord-length distribution regardless of the resolution. This hinders the accurate estimation of the characteristic length necessary to compute the Knudsen number and the effective diffusivity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".