Ultrasonic Spike Signal Analysis for Subcooled Boiling Condensation Measurement
Bibliographic record
Abstract
Condensation is a key factor in subcooled boiling, significantly affecting heat and mass transfer, which are critical in many industrial processes.In a previous study, we successfully obtained the condensation rate -the collapsing speed of vapor bubbles during subcooled boiling using the ultrasonic velocity profile method -UVP method, employing two primary ultrasonic frequencies.The ultrasonic sensor was driven by sinusoidal electrical burst signals, also called tone-bursts, with a specified center frequency.This study examines the application of spike excitation signals within the same method.Spike signals, commonly produced by pulsers, i.e. pulse generators, in the ultrasonic testing field, offer the potential to enhance the accuracy of the UVP method while can reduce the cost of the measurement system.Our investigation involved measuring and analyzing ultrasonic echoes in water under two conditions: ambient temperature and boiling.Subsequently, these signals were applied to measure air-water adiabatic bubbly flow, and the measurement accuracy was validated.Finally, subcooled boiling flow measurements were performed, and the associated uncertainties were thoroughly analyzed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".