Steady three-dimensional flows past hollow fiber membrane arrays – cross flow arrangement
Bibliographic record
Abstract
Hollow fiber membrane (HFM) modules are among the most common separation devices employed in membrane separation applications. Three-dimensional steady-state computational fluid dynamics (CFD) simulations are carried out to study flow past hollow fiber membrane banks (HFMB). The current study investigates the effects of flow behavior on membrane performance during binary mixture separations. Carbon dioxide (CO2) removal from methane (CH4) is examined for various arrangements of HFMs in staggered and inline configurations. The common HFM module arrangement is the axial flow configuration. However, this work focuses on the radial cross-flow configuration. The HFM surface is a functional boundary where the suction rate and concentration of each species are coupled and are functions of the local partial pressures, the permeability, and the selectivity of the HFM. CFD simulations employed the turbulent k–ω shear stress transport (SST) model to study HFM performance for Reynolds numbers, 200 ≤ Re ≤ 1000. The efficiency of the inline and staggered arrangements in the separation module is evaluated by the coefficient of performance and the rate of mass flow per unit area of CO2 passing across the membrane surface. This work demonstrates that the module with staggered arrangement outperforms the module with the inline arrangement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".