A Method for Calculation of Final Film Thickness in Free Coating of Viscoelastic Fluids onto a Vertical Surface
Bibliographic record
Abstract
Abstract A method for the calculation of the final film thickness in free coating of a viscoelastic fluid onto a vertical surface withdrawn from its vessel is developed. The method is based on the definition of an objective function, the minimization of which guarantees that the kinematic as well as the dynamic conditions at the lower boundary of the dynamic meniscus region are simultaneously satisfied. A systematic approach is provided in order to localize the optimum value of the final film thickness within the optimization interval. It was observed that there was a clear relationship between the estimated final film thickness and the value of a parameter A. The closeness of this parameter to zero corresponds to the dynamic constraint being fulfilled at the lower boundary of the dynamic meniscus region. This relationship is used as an objective means of determining the direction to update the interval of optimization to obtain the final thickness of the film. The results of the proposed method are compared to the previous works on the free coating of viscoelastic fluids, which are based on a trial‐and‐error method. The performance of the previously applied rheological models to the formulation of the free coating process, mainly the modified Oldroyd models, is also compared by introducing the present method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".