Ultrasonic Cross-Correlation Flow Measurement: Theory, Noise Contamination Mechanisms, and a Noise Mitigation Technique
Bibliographic record
Abstract
Based on past experience with ultrasonic cross-correlation flow meters in power plant environments, the presence of spatially correlated noise due to pressure waves, vibration, or sources other than transport of turbulent eddies will cause a bias in the time delays measured by the meter. Several techniques were developed to detect the existence of such correlated noise and correct for its effect at plant conditions. An analytical and experimental investigation was performed to further understand the basic physics of the noise mechanisms. The dominant error mechanisms investigated in this work were speed of sound perturbations due to pressure fluctuations and beam path length changes due to wall vibration. An analytical model was formulated which estimates the signal level of the flow meter based on the turbulent velocity field. From this model, an estimate of the system noise which would cause contamination could be determined. A test at a water tunnel facility was performed in order to evaluate the noise mechanisms. During this test, measurements were taken with and without controlled noise sources. Pressure and acceleration measurements were used to evaluate a coherent noise removal technique developed to mitigate the impact of noise in the ultrasonic cross-correlation flow measurement. The coherent noise removal technique was shown to be effective in removing noise during the water tunnel test.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".