System Stability of the Open Draw Section and Paper Machine Runnability
Bibliographic record
Abstract
The present work is concerned with the system dynamics and stability of the open draw sections of paper machines where web breaks occur most frequently. We have applied a novel particle based system dynamics model that allows the investigation of complex interactions between web property fluctuations and system parameters, without any constraints of a particular geometrical web shape or boundary conditions assumed a priori. The result shows that, at a given machine draw and web property parameters, the open draw section maintains its steady-state until it reaches a certain machine speed limit. At this speed the system looses its stability and the web strain starts growing without any limit, and thus leading to a web break. A similar instability can also be triggered when web properties suddenly fluctuate during steady-state operation. The parametric sensitivity studies indicate that, among the web property parameters studied, the elastic modulus of the wet web has the largest impact on the critical machine speed as well as on the detachment point where the web is released from the first roll. Further analysis shows that the decrease in dryness has a (negative) synergistic effect causing an increased risk of system instability. It is, therefore, most important to control, not only average dryness, but also its variations in order to enhance paper machine runnability.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".