Modelling of Self‐Induced Oscillations in the Mixing Head of a RIM Machine
Bibliographic record
Abstract
Abstract In this paper, the self‐induced oscillations in the mixing head of a RIM machine were modelled. An analytical and simple correlation was derived between the Strouhal number and the averaged pressure drop along the amplitude of the oscillation in the mixing chamber. This correlation theoretically identified that the frequency of the oscillations could be well correlated by the jet Reynolds number, Red, and the dimensionless distance between the two jets as proposed by Denshchikov et al., Fluid Dyn. 3, 460‐462 (1983). The flow field dynamics in the mixing head was simulated successfully by Fluent and the computed pressure fluctuations were used to calculate the frequencies of the oscillations in the mixing head. The calculated Strouhal numbers are in good agreement with the dominant frequency from the power spectra of the measured velocity component ux (Santos, 2003). Finally, the effect of Red and Froude number, Fr, on the Strouhal number was investigated in the impingement region. The average Strouhal number showed a decrease with the Reynolds numbers, due to the increasing randomness of flow field in the impingement region. It was also found that the operations at lower values of Fr presented an increasing stability up to the point where the system is unable to present dynamic evolution. The model in this paper provides a theoretical starting point towards understanding of the quantities of the oscillatory flow in the mixing head, as well as a numerical approach to evaluate the dominant frequency in the mixing chamber.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".