Acoustic Wave Testing System for Monitoring the Vapor Chamber in Vapor Extraction Process
Bibliographic record
Abstract
Abstract The acoustic wave detection system is considered a non-destructive monitoring system to estimate distances using measurement of 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 vapor extraction process. For this purpose, various stages of vapor chamber evolution in the Vapex process were experimentally simulated by changing the shape of air balloons buried in simulated porous media in a lab scale model. Then, an array of ultrasound transducers and receivers were used to measure time-of-flights at different stages of the vapor chamber growth. Finally, the collected data were fed into a signal processing program developed in this study to determine the shape of the vapor chamber. Conducted analysis in this study include: sound speed testing in different porous media, signal attenuation tests in different porous media, imaging of different simulated vapor chambers in different porous media, and the acquisition and analysis experiments. Results show that acoustic wave detection can be used for accurate mapping of the position and shape of the vapor chamber in the studied process. Monitoring the shape and growth of the vapor chamber provides valuable information for optimizing oil production in order to maximize oil recovery. This is the first attempt at using acoustic wave detection techniques in monitoring the phase movement in the Vapex process. Results of this study show that this technique can be potentially used for this purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 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".