Automatic, Non-Intrusive, Flame Detection in Pipelines
Bibliographic record
Abstract
Many methods of flame detection are available. Unfortunately, few offer remote, non-line-of-sight, detection. In cases where flammable mixtures are transported within tubing (such as flare lines, storage tank vents, air drilling, and improperly designed purging operations) there is often no means by which combustion can be detected. This is a significant deficiency in some applications. If the mixture were to ignite, the results could be catastrophic. To address this problem, combustion noise is being investigated at the University of Calgary as a possible means of detecting flames within tubing. An experimental study has been completed that shows that combustion noise can be distinguished from other sources of noise by its inverse power law relationship with frequency. A robust algorithm has been developed that, when combined with high-speed pressure measurements, provides early detection of flames. When combined with other filters, the algorithm can automatically separate combustion noise from other sources of noise. In this paper, a stop band filter was used to remove the noise created by a fluttering check valve.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".