Study of combustion noise reduction in a kerosene burner: investigation of nozzle and pre-heating chamber flow-field
Bibliographic record
Abstract
The combustion noise generated in a small burner was successfully reduced by installing a short tube inside the pre-heating chamber of the burner. To understand the noise reduction mechanism, the flow-field of the nozzle and pre-heating chamber with and without the inner tube was numerically studied in conjunction with some experimental measurements. The fuel spray characteristics obtained from Phase Doppler Particle Analyser (PDPA) measurements were used to define initial conditions of the spray discrete phase. Couplings between the continuous and discrete phases, as well as the turbulent stochastic effect were modelled. It is found that the installation of an inner tube in the pre-heating chamber modifies the flow-field, fuel spray trajectories, and reduces local velocities and turbulent strength. These contribute to the reduction in combustion noise. Most of all, the considerable modification of the fuel spray distribution in the pre-heating chamber plays a major role in the combustion noise reduction of the burner. In addition, the numerical results also show that the flow-field in the vicinity of the nozzle is very complicated and practically identical for both cases. A strong toroidal vortex is formed in the centre region of the nozzle, and a high velocity swirling airflow is observed outside the core region.
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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) | 0.001 | 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 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".