Experimental and Numerical Investigation of Spatial and Temporal Dispersion of Forced Fuel Oscillations
Bibliographic record
Abstract
In lean premixed combustors of gas turbine engines, fuel-air mixing is considered vital for controlling pollutant emissions as well as combustion instability. Enhancement in mixing may be obtained by modulating the fuel flow rate. Modulation of fuel flow is also a useful technique to actively control combustion instabilities arising from the pressure oscillations in the combustor and thrust augmenters. Effectiveness of the forced oscillations depends on the level of dispersion present in the system. Knowledge of dispersion levels is also important in determining the degree of mixing and therefore, the effectiveness of a premixer. This paper presents the experimental efforts undertaken to study the spatial and temporal dispersion of fuel flow rate oscillation introduced at the premixer inlet. Effects of oscillation amplitude and frequency are investigated at different bulk flow rates and at various locations in the premixer. Also presented is a review of the in-house numerical work done towards this end, using three computational methods. Results show that the degree of dispersion in fuel flow rate oscillations depends on modulation amplitude and frequency as well as advective velocity of the bulk flow.
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 | 0.001 |
| 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 | 0.001 |
| 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".