Acoustic-Wave-Testing System for Monitoring the Vapour Chamber in Vapour-Extraction Process
Bibliographic record
Abstract
Summary The acoustic-wave-detection system is considered a nondestructive monitoring system to estimate distances by measuring the time-of-flight of an ultrasonic wave. In this paper, a comprehensive experimental study was conducted to investigate the feasibility of the acoustic-wave-detection system in monitoring the shape and position of the gas phase in the vapour-extraction (VAPEX) process. For this purpose, various stages of vapour-chamber evolution in the VAPEX process were simulated experimentally by changing the shape of air balloons buried in simulated porous media in a laboratory-scale model. Then, an array of ultrasound transducers and receivers was used to measure time of flight at different stages of the vapour-chamber growth. Finally, the collected data were fed into a signal-processing program developed in this study to determine the shape of the vapour chamber. Conducted analysis in this study includes sound-speed testing in different porous media, signal-attenuation tests in different porous media, imaging of different simulated vapour chambers in different porous media, and acquisition and analysis experiments. Results show that acoustic-wave detection can be used for accurate mapping of the position and shape of the vapour chamber in the studied process. Monitoring the shape and growth of the vapour chamber provides valuable information for optimizing oil production in order to maximize oil recovery. The proposed methodology is able to identify acoustic anomalies in a porous medium in the laboratory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".