The effect of a plate installation upstream of an orifice on the pressurized flushing efficiency
Bibliographic record
Abstract
The effect of a plate installation upstream of an orifice was investigated as a new method for improving the pressurized flushing efficiency. A plate with three different widths (B T ) was installed at four different distances (L T ) upstream of the orifice. According to the results, the plate installation significantly increased the pressurized flushing efficiency due to the wake vortices formed upstream of the orifice. The flushing efficiency increased up to a maximum value of 11.5 times relative to what was calculated for the experiment without plate for the same conditions. For the best configuration of the plate width and its distance from the outlet (i.e., L T /D O = 0.7 and B T /D O = 0.5, D O is the orifice diameter), the length, width, and depth of the flushing cone increased by approximately 160, 90, and 240% relative to the control experiment under the same conditions, respectively. Four empirical equations based on the effective and dimensionless variables were developed, enabling the users to make predictions based on the flushing cone volume and dimensions. Finally, the importance of each effective parameter in the derived equations were analyzed using sensitivity analysis method. The sensitivity analysis results showed the greater impact of outlet discharge of the orifice and the plate distance than its width on the flushing cone volume and dimensions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".