Cars and sample probe measurementsin two pilot-scale natural gas flames
Bibliographic record
Abstract
The design of a reliable low-NO x burner requires a good understanding of the mixing between the fuel and air jets. This is true both for technologies using staging of the combustion air and for those using exhaust gas recirculation. Therefore, there is a need for detailed measurements in the near-burner region to validate the theories on which the burner technology is based and to better understand the processes involved in this region of the furnace. This article presents intrusive measurements of conventional combustion products (O 2 , CO 2 , CO, and NO x ) and mean and fluctuating components of temperature using a laser-based measurement system (CARS) in two natural gas flames produced by two commercially available burners. Based on these measurements, the burners are compared with respect to combustion performance. The low-NO x burner produced similar amounts of NO x compared to the other burner. The reason for this is found in the differences in the fluctuating component of gas temperature in the flames from these burners. The low-NO x burner is shown to have broad temperature profiles resulting in much larger regions where the temperature is above the 1800 K limit for NO x formation compared to the high-NO x burner.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".