Maximizing Mass Transfer Using Highly Curved Helical Pipes: A CFD Investigation
Bibliographic record
Abstract
Membrane contactors are considered as one of the most promising intensification technologies for gas-liquid absorption processes in food, chemistry, energy and pharmaceutical industries. Two strategies can be applied in order to push process intensification: maximize mass transfer coefficients and maximize the specific membrane surface area (i.e. packing density) of the module. Dean vortices generation is one of the most efficient way for mass transfer improvement but requires helical shape membranes. Consequently, a combined packing geometry and mass transfer study is needed in order to evaluate the optimal performances of helical hollow fiber membrane modules. This work aimed to achieve that target through a systematic analysis of mass transfer performances in highly curved hollow membrane contactors. The impact of the nondimensional numbers * , * (respectively the helical radius and helical pitch both nondimensionalized by the pipe diameter ) on mass transfer was evaluated numerically by using CFD. Helical hollow fiber membranes with lengths ranging from 30 to 6000 , nondimensional helical diameters * between 0.05 and 10, and nondimensional helical pitches * between 1.25 and 15 were studied. In addition, mass transfer in packed membrane modules is analyzed thanks to a mass transfer enhancement factor , which is equal to the mass transfer ratio / (comparing the Sherwood number of helical and straight pipes) multiplied by the packing density ratio / (comparing the packing density of helical and straight pipes). It is shown that enhancement is maximal for highly curved helical pipes. For example, for a Schmidt number of 10, highly curved pipes allow reaching a value, which is about 4 times larger than that of a straight tube. For a Schmidt number 1000, the mass transfer efficiency can be enhanced by one order of magnitude.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".