Optimization study of obstacles in <scp>T–T</scp> mixing channel at low Reynolds numbers
Bibliographic record
Abstract
Abstract In this study, numerical simulations were conducted to optimize obstacle geometry for improving mixing in T–T mixers at low Reynolds numbers (2 < Re < 100). The study considered obstacles of cylindrical and prismatic shapes and optimized their pitch and geometrical parameters for enhanced mixing. For cylindrical obstacles, the optimized configuration resulted in symmetrical recirculation zones at Re > 30, which led to larger mixing qualities of 80% and 85% for Re values above 30 and 50, respectively. However, the pressure drop increased in the optimized T–T mixer due to the larger size of the obstacles. On the other hand, in the case of prismatic obstacles, the mixing qualities of 80% and 85% were achieved only at relatively higher Re values of 70 and 90, respectively. The recirculation zone formed behind the obstacle was asymmetric due to the asymmetrical shape of the optimized prism. At higher Re, the optimized cylindrical obstacle configuration resulted in better mixing than the prismatic configuration. However, the choking effect in the former increased the pressure drop.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".