Design Considerations for Plate and Frame Ultrafiltration Modules by Computational Fluid Dynamics Analysis
Bibliographic record
Abstract
Abstract Pressure and flow maldistributions were studied in a full‐scale industrial plate and frame ultrafiltration module, operating in a Z flow pattern, for the recovery of used motor oils. Solutions were obtained using (1) a three‐dimensional solution of the Navier‐Stokes equation using computational fluid dynamics and (2) Bernoulli's equation and a momentum balance in one dimension. Fluid decelerations and accelerations generated pressure increases and decreases in the distributor and collector, respectively, biasing the flow distribution to the last channel. Several modifications to the original design were evaluated; the most effective was larger distributor and collector diameters, which greatly improved the uniformity of the flow distribution and transmembrane pressure, and reduced the overall pressure drop in a bank. A variable diameter distributor and collector module was designed using the 1‐D model. Flow distribution was significantly improved but also yielded an undesirable overall higher pressure drop and a pressure maldistribution in the bank. The maldistribution of the main inlet manifold to the distributors in the first bank was strongly dependent on the module design. The flow distribution across the width of a channel became uniform within a short distance, essentially eliminating the need to consider this design aspect any further. Flows at the bank outlets, and hence inlets of the following bank, showed uniform lateral distribution in all cases, suggesting that future modelling work can be limited to a fraction of the module width, based on symmetry, in order to gain computational efficiency.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".