Bibliographic record
Abstract
A pneumatic test is often used to detect leaks in a pipe. However, current practices would become problematic for a visual inspection when the pipe is inaccessible or for a leak-based algorithm using the measured pressure reduction trend, which could be caused by decreased ambient temperature rather than by a leak. The paper develops a new method to resolve these issues using conservation of gas mass. The gas mass decreases with a leak. When the gas mass drops below its accuracy limit, the leak could be detected. The detectible leak mass of gas depends on the pressure of gas, the temperature of gas, the volume of gas, the molar mass of gas, and the accuracy of gas mass. The detectible leak hole size not only depends on the detectible leak mass of gas but also on the test duration, the discharge coefficient of the hole, and the flow velocity. A parametric study is to investigate the relationship between the detectible leak hole size and these parameters. The study finds that the detectible leak hole sizes can range from 0.1 to 1.2 mm, mainly dependent on the volume of gas, the accuracies of the pressure and temperature measurement devices, and the test duration become smaller when the test duration increases and/or when the accuracies are enhanced are larger if the volume of gas is increased and do not depend on the test pressure for an ideal gas. The test temperature and other parameters have limited effects on the detectible hole sizes. Although the above results are derived from nitrogen, they are applicable to other gases. The method can detect tiny leaks but cannot detect the leak locations.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".