Mutual Information Functions of Differential Pressure Fluctuations in Spouted Beds
Bibliographic record
Abstract
The objective of this paper is to determine whether the flow regimes in spouted beds can be recognized by the mutual information function of pressure fluctuation signals. Differential pressure fluctuation time series were obtained in a gas spouted bed of 0.12-m i.d. at different axial and radial positions, with glass beads and silica gel particles. It was found that the packed bed has a low level and fast decay rate of mutual information, suggesting a quite random signal. Unstable spouting exhibits a high level of mutual information and strong periodic motion. While for a stable spouting regime, the level of mutual information and the decay rate around time zero are between those for the packed bed and unstable spouting regimes. The mutual information function for signals measured on the wall has higher predictabilities compared to those measured inside the bed. The determination of optimum time delay by the aid of the first minimum of the mutual information function was discussed. It has been found that the first minimum of the mutual information function can be used as the optimum time delay if it does exist. In the case that no first minimum exists in the mutual information function, the suggestion of Daw and Halow (Daw, C. S.; Halow, J. S. AIChE Symp. Ser. 1993, 89, 103−122) that 10−50% of the maximum value of the mutual information resulted in too small a time delay. An appropriate time delay can be determined by comparing the similarity between phase space trajectories and pseudo phase space trajectories.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".