Uncertainty in Fluid Flow Measurement: A Case Study of Flow Measurement Comparison Through PVC and Steel Pipes at Varying Temperatures for Liquid With Small Air Bubbles
Bibliographic record
Abstract
Using fresh water with small amount of air bubbles of size as small as a pencil dot, the existing flow meter in the flow system (hydraulics) was proved using the Sierra Ultrasonic meter (Innova-SonicTM Model 210i) with measurement medium taken at ambient temperature of 30oC. The procedures for installation of the ultrasonic meter were followed and measurement taken with transducers mounted using V-method and N-method respectively, on a pipe size of 20mm. The V-method and N-method gave average flow rates of 0.02918cf/sec and 0.04402cf/sec respectively. The V-method resulted in a meter factor of 1.9610, while the N-method resulted in a meter factor of 3.2511. The V-method and N-method gave relative percentage error values of 49% and 69.2% respectively. The V-method proved to be suitable for the pipe size of 20mm used in the experiment, with standard deviation of 5.3339x10-4 and variance of 2.84505x10-7 compared to standard deviation of 6.8x10-3 and variance of 4.624x10-5 obtained from N-method. It is recommended that a meter factor of 1.9610 obtained from V-method be applied on the existing meter. The liquid flow rates at measurement medium temperatures of 50oC and 70oC were determined by the use of poly-vinyl chloride and steel pipes respectively. It was observed that fluid flow rate increased with increase in temperature, and flow rate determined with ultrasonic flowmeter depends on material type as flow through poly-vinyl chloride pipe was generally higher compared with steel pipe at the temperatures values considered.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".