How does porosity heterogeneity affect the transport properties of multibore filtration membranes?
Bibliographic record
Abstract
The prediction of pressure and flow distributions inside porous membranes is important if the geometry deviates from single- bore tubular geometries. This task remains challenging, especially when considering local porosity variations caused by lumen- and shell-side membrane skins and macro- and micro-void structures, all of them present in multibore membranes.This study analyzes pure water forward and reverse permeation and backwashing phenomena for a polymeric multibore membrane with spatially-varying porosity and permeability properties using computational fluid dynamics simulations. The heterogeneity of porosity distribution is experimentally characterized by scanning electron microscopy scans and reconstructed cuboids of X-ray micro-computed tomography scans. The reconstructed cuboids are used to determine porosity, pore size distribution, and intrinsic permeability in the membrane’s porous structure in all spatial directions. These position-dependent properties are then applied to porous media flow simulations of the whole membrane domain with different properties for separation layer, support structure, and outside skin layer. Various cases mimicking the pure water permeation, fouling, and backwashing behavior of the membrane are simulated and compared to previously obtained MRI measurements.This work reveals (a) anisotropic permeability values and isoporosity in all directions and (b) differing contributions of each lumen channel to the total membrane performance, depending on the membrane-skin’s properties. This study encourages to pertain the quest of understanding the interaction of spatially distributed membrane properties and the overall membrane module performance of multibore membranes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".