An improved correlation for dry pressure losses across static mixers at high Reynolds numbers
Bibliographic record
Abstract
Abstract Corrugated (SMV‐style) static mixers are industrially important for process intensification and can promote gas–liquid mass transfer in processes such as sour gas sweetening. Current correlations for pressure loss are limited to Reynolds numbers (Re) below 40 000, far below the ranges encountered in natural gas systems (10 5 < Re < 10 7 ). An experimental and numerical study of pressure drop across multiple corrugated mixers in the range of 10 4 < Re <2 × 10 5 encompassing different configurations (aligned, rotated), pipe diameters (1–4 in.), and sand grain surface roughness values (10–5000 μm) is reported here. Our previous correlation for pressure loss across a single corrugated element is shown to be extendable to multiple corrugated mixing elements. Through the inclusion of mixer tortuosity ( τ ), porosity ( ε ), and macro‐scale (geometric) wall roughness ( e ), the correlation also matches historical pressure drop data (at different τ , ε ) reported in literature, thereby demonstrating the utility of these variables as parameters that can help optimize mixer performance. Experiments and computational fluid dynamics (CFD) modelling revealed that the rotated configuration increased the residence time by up to 13% in comparison to the aligned configuration. This may have implications on the selective absorption of sour gas components that are based on fast kinetics. In addition, the role of wall roughness (both pipe housing and mixer) was demonstrated to be significant in this study (accounting for 55% of the pressure losses) and must be accurately accounted for when scaling laboratory measurements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".