Propagation of Excitation Pulses and Autocatalytic Fronts in Packed-Bed Reactors
Bibliographic record
Abstract
We experimentally and numerically studied the velocity of excitation pulses in packed bed flow reactors as a function of the fluid flow velocity and the diameter of the glass beads used as packing material. Differential transport was absent. The downstream and upstream propagating pulses were observed to behave in a manner that is strikingly different from the simple Galilean translation expected in the case of an ideal homogeneous plug-flow. Downstream propagating pulses travel faster than anticipated, by a constant factor that depends on the bead size. Upstream propagating pulses travel at a lower velocity and become stationary above a critical value of the fluid flow velocity. Both the width and the intensity of up- and downstream propagating pulses increase when the fluid flow velocity is increased. Model calculations show that the accelerated downstream propagation and the decelerated upstream propagation agree qualitatively with enhanced turbulent diffusion within the packed bed. The formation of stationary pulses can be explained by the existence of a stationary fluid phase of stagnant pockets within the packed bed. Once excited, a stagnant pocket acts as a permanent super-critical perturbation, which causes excitation of the flowing medium and locks the temporal response in space. The dramatic increase of the pulse-intensity remains unexplained by the mentioned models.
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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 | 0.001 |
| 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".