Numerical Analysis of Newtonian Fluid Flow Through Multi-Hole Orifice Meter
Bibliographic record
Abstract
The influence of multi-hole orifice flow meter geometry parameters on the parameters of Newtonian fluid through multihole orifice meters was investigated using computational fluid dynamics as well as the effect of contamination in front of the MHO flow meter.The air flow was steady, three-dimensional, and turbulent.Analysed Newtonian fluid was air and physical properties that were considered were density and dynamic viscosity.The numerical method was finite volume method, and standard k-ε turbulence model was used for turbulence modelling.Multi-hole orifice meter with three different β parameters 0.55, 0.6 and 0.7, was observed and Reynold's number was 10 5 .The pressure drop and discharge coefficient were analysed.Numerical simulations were performed using commercial software the STAR-CCM+ 2019.2.It was found that increase in 𝛽𝛽 parameter results with the decrease in pressure drop and increase in discharge coefficient.Also, it was found that that the influence of 𝛽𝛽 parameter is much higher when analyzing pressure drop rather than discharge coefficient values.Numerical simulations were also performed to investigate the effect of contaminations in front of the MHO plate with 𝛽𝛽 = 0.5, on the discharge coefficients.It was found that as the contamination angle is increased the discharge coefficient tends to increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".