Experimental study on energy consumption characteristics of rotational–perforated static mixers
Bibliographic record
Abstract
Abstract An ideal static mixer can achieve efficient mixing at low pressure drops. Owing to the excellent performance of the tridimensional rotational flow sieve tray (TRST) in a gas–liquid two‐phase system, the TRST structure was modified into a rotational–perforated static mixer (RPSM) to enhance mixing in multicomponent liquid systems. The energy consumption characteristics of the RPSM were experimentally studied based on Reynolds numbers in the range of 986–7892, gap L = 0–80 mm, and relative angle γ = 0–45°. The effects of the element installation method, number, gap, relative angle, fluid Reynolds number, fluid properties, and other parameters on the RPSM pressure drop were also investigated. An interaction analysis of each factor was performed using the factorial design method and an empirical model of the RPSM Z ‐factor was established. Additionally, pressure drop in the RPSM was compared with those of other commonly used static mixers. Results show that, when the element is backward‐installed, the pressure drop is higher than that in the forward direction because the fluid is constantly twisted. Moreover, the pressure drop increases with increasing element gap, and the average increase is 43.64% and 19.28% for the forward and backward installations, respectively. The influence of the relative angle on the pressure drop is mainly reflected when the gap L = 0. Subsequently, the degree of influence of each factor was determined, and the Z ‐factor was calculated and found to be consistent with the experimental values (relative error of less than 15%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".