Effects of Hydrogen and Nitrogen Concentration on Laminar Burning Velocities and NO, CO Formation of Propane-Air Mixtures
Bibliographic record
Abstract
With the development of the uses of hydrocarbon compounds in many industrial fields, especially in the field of energy liberation through oxidation of gases, as previous research specialized in improving the combustion processes of gases by adding particles of other gases that contribute to improving the combustion of these compounds in terms of temperatures and speed of flame combustion. The previous study aimed to improve the flame speed by adding different gases to several compounds. In this research paper, propane gas C3H8 is used as the main gas to improve its combustion properties. N2 and H2 gas were added in different proportions, through which a clear effect on the combustion properties can be seen in terms of combustion speed. Flames and exhaust as CO and NOx. As the auxiliary gases were added in this improvement at rates of 20%, 30% and 40%, and then these tests were analyzed, where it was concluded that H2 gas clearly contributed to improving the combustion speed of the flame, as the flame combustion velocity reached 58.3 cm/s by 40%.
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 | 0.000 | 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".