Bibliographic record
Abstract
Development of low-pollution gas turbine engines has been calling for the development on new technologies. Lean premixed combustion is one of them but tends to be accompanied by combustion instabilities. Some instabilities are caused by coupling between the combustion zone and upstream fuel/air mixing chamber. Acoustic oscillations in the mixing chamber lead to variation of mixture distribution in the combustion zone. On the other hand, the instability itself may improve the turbulent mixing that mitigates these equivalence ratio fluctuations. The goal of this study is to gain knowledge of the fundamental mechanisms of harmonically perturbed jet mixing in air. A jet is considered as a system on which a harmonic analysis is performed. The input parameter is a modulated velocity to induce perturbation. The output parameter is the whole flow field, particularly the statistics of mixture fraction distribution. The tool is a high-order compressible direct numerical simulation code. It is demonstrated that the system can be qualified as a band-pass filter. The efficiency of mixing reaches a maximum for a modulation frequency comparable to the natural mode of a laminar jet. This study suggests that the characteristic frequency of the system to improve mixing can be inferred from the investigation of the natural mode of this system and vice versa.
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 | 0.000 |
| 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) | 0.002 | 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".