Computational implementation of interfacial kinetic transport theory for water vapour transport in porous media
Bibliographic record
Abstract
A computational framework is developed for applying interfacial kinetic transport theory to predict water vapour permeability of porous media. Modified conservation equations furnish spatially periodic disturbances from which the average flux and, thus, the effective diffusivity is obtained. The equations are solved exactly for a model porous medium comprising parallel layers of gas and solid with arbitrary solid volume fraction. From the microscale effective diffusivity, a two-point boundary-value problem is solved at the macroscale to furnish the water vapour transport rate in membranes subjected to a finite RH differential. Then, the microscale model is implemented using a computational framework (extended finite-element method) to examine the role of particle size, aspect ratio and positioning for periodic arrays of aligned super-ellipses (model particles that pack with high density). We show that the transverse water vapour permeability can be reduced by an order of magnitude only when fibres with a high-aspect ratio cross section are packed in a periodic staggered configuration. Maximum permeability is achieved at intermediate micro-structural length scales, where gas-phase diffusion is enhanced by surface diffusion, but not limited by interfacial-exchange kinetics. The two-dimensional computations demonstrated here are intended to motivate further efforts to develop efficient computational solutions for realistic three-dimensional microstructures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".