Mixing in a soft‐elastic reactor (SER): A simulation study
Bibliographic record
Abstract
ABSTRACT Inspired by the animal upper digestion tract, a strategy of developing soft‐elastic reactors (SERs) for potential industrial usage has evolved, and is under the framework of bio‐inspired chemical engineering. In our laboratory, two basic reactor categories have been experimented upon to explore the parameters influencing SER performance: vertical and horizontal cylindrical reactors. Different from the traditional rigid reactors equipped with solid agitators, SERs mainly triggers mixing through the movement of its elastic wall. The mechanisms of SERs are, however, not yet fully understood due to the limitations in the laboratory work in scale and in the diversity of the working conditions. For the first time we present a numerical study on the mixing behaviour of an SER to reveal its special characteristics as an alternative mixing method difficult to show in laboratory. Two‐dimensional situations are analyzed numerically with a successful implementation of a moving mesh method in ANSYS Fluent. The approach allows versatile movement of the reactor wall to be effectively simulated. Systematic investigations were then carried out to investigate the effects of two chosen wall motion modes. The effects of such motion modes have been demonstrated through tracking the evolutions of the flow fields. This work shows room for potential improvements of SERs by devising different wall movements.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".