Evaluation of using a porous medium approach to simulate flat-sheet membrane bioreactors
Bibliographic record
Abstract
The porous medium approach has been applied by others to study the global hydrodynamics in hollow-fiber (HF) membrane bioreactors (MBR). This study explores the usability of the porous medium approach for flat-sheet (FS) MBR and introduces a CFD-based numerical calibration of the porous parameters. Two key modeling strategies are suggested to ensure the pressure prediction accuracy: (1) use SST k - ω turbulence model incorporated with an intermittency transition model (SSIT); (2) considerate two-phase flow during the calibration. The results showed that SSIT significantly improved the pressure prediction of the flow between two sheets. Also, unlike HF MBR, the gas phase was observed to be non-negligible during the calibration for FS MBR. The numerically calibrated porous parameters were then applied in a three-dimensional simplified model (SPM), where multiple membrane sheets were treated as one porous zone. The SPM was compared with a realistic model (RM), where all membrane sheets were simulated directly. A similar pressure distribution was observed in both models; however, local flow characteristics (i.e., the acceleration of the flow entering the module zone) were not reflected in the SPM. Hence, it is recommended to use the porous medium approach for global hydrodynamics investigation when the local flow characteristics are not of interest.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".