Drying of porous materials at pore scale using lattice Boltzmann and pore network models
Bibliographic record
Abstract
Abstract Drying at macroscale shows a first drying period with constant drying rate followed by second drying period showing a receding moisture front, phenomena that can be tailored upon need. In order to study the drying of materials, we present a new hybrid computational method, where the dynamics of the liquid-vapor interfaces is modelled by lattice Boltzmann modelling (LBM) in the two-phase pores, while the single-phase flow in the pores filled solely by vapor or liquid is solved by pore network model (PNM). This hybrid method is validated by comparison with reference full LBM simulations. The hybrid method combines the advantages of both methods, i.e., accuracy and computational efficiency. LBM and the hybrid LBM-PNM method are used to study the drying of porous media at pore scale. We analyse two different pore structures and consider how capillary pumping effect can maximize the drying rate. Finally, we indicate how optimized drying rates are relevant when designing facade or pavement solutions that can mitigate higher surface temperatures in urban environments by evaporative cooling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".