Effect of annulus ratio on the residence time distribution and Péclet number in micro/milli‐scale reactors
Bibliographic record
Abstract
Abstract Micro/milli‐scale annular reactor with straight and helical forms has excellent heat and mass transfer performance due to the short molecular diffusion distance and dual‐wall surface transport. The annular gap spacing is scalable by adjusting the inner and outer tube diameter. The influence of diffusion and convection effects on axial dispersion as expanding the flow scale requires further elucidation with the help of residence time distribution (RTD) curves and Péclet (Pe) numbers. The correlation of RTD characteristics with annulus ratio γ = D h / D (ratio of annulus characteristic size to outer diameter) is investigated using computational fluid dynamics. Results show that with enlarging the straight annular gap from micro‐scale to milli‐scale, RTD characteristics exhibit opposing patterns. This can be attributed to the transition from diffusion‐dominated to convection‐dominated on momentum transfer, and the transition interval is 0.167 < γ < 0.250. Correlation equations of Pe number with Reynolds (Re) number and γ are established under diffusion‐dominated and convection‐dominated states. The symmetrically distributed secondary flow in the helical annular gap effectively elevates the Pe (Pe max > 100). Correlation equations of Pe with Re and γ are established in helical annular gaps with 0.083 < γ < 0.208 and 0.167 < γ < 0.500. The above results contribute to understanding the annular flow RTD characteristics for better applications of tube‐in‐tube reactors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".