Experimental investigations of the effect of surface roughness on high-acceleration film splitting between two substrates
Bibliographic record
Abstract
Thin liquid film separation is an important part of many industrial processes and is relevant to “rewet” that can occur in the press section of a papermachine. The separation ratio, the mass of the remaining liquid on one (“the moving”) substrate after separation to the total mass of the initial liquid film between two substrates, is of particular interest in this thesis because reducing rewet reduces the energy consumption in the dryer section of a paper machine significantly. The focus of this study is the experimental measurement of high-acceleration separation of Newtonian liquid films trapped between two substrates. The behavior of the liquid bridges between smooth separating substrates has been a subject of past studies at low separation rates. A distinction of this study is the investigation of high-acceleration separation of the thin liquid bridging the gap between rough as well as smooth substrates. An experimental apparatus has been designed and manufactured that can produce average separation accelerations of up to 325 m/s2, initial bridge heights starting from 10 µm, and average surface roughness values of up to 86 µm. When the distance between substrates increases, a viscous fingering region is observed along the perimeter of the wetted area where air fingers grow radially inward, while in the center region, cavitation bubbles can emerge and grow until the two substrates are sufficiently separated such that the liquid bridges between them break. The separation ratio is meaningfully affected by the surface roughness, viscosity, and acceleration, creating variations of up to 20% in the separation ratio. It is hypothesized that the separation ratio is affected by the relative amounts of the flow field that are subject to viscous fingering and liquid cavitation. Two distinct separation processes, residual layer formation and fibrillation, corresponding to the two flow regimes have been suggested to explain the difference in the measured separation ratios of different cases. A laser-induced fluorescence (LIF) measurement system has been developed to measure the thickness distribution of the liquid bridges during the separation process, and the results of the LIF measurements are consistent with the suggested hypothesis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".