Suppression of thermo-acoustic instabilities in horizontal Rijke tube using pulsating radial jets
Bibliographic record
Abstract
Thermo-acoustic instability has been observed in gas turbines, rocket engines, and aero-engines. Acoustic perturbations grow and change the characteristics of the flow due to instability. The present work describes the use of pulsating air jets to suppress the thermo-acoustic instabilities. In present study pulsatile micro-jets are placed downstream of the burner radially which breaks the coupling between acoustic waves and unsteady heat release. A microphone connected to LIFA (LabVIEW Interface for Arduino) was used to detect the sound pressure levels. By controlling the airflow rate of the pulsatile jets, the sound pressure levels were suppressed down to the background noise level using minimum energy and time. A closed-loop control system is developed for this purpose, which works on the feedback signal acquired from microphone. To simulate the one dimensional combustion phenomenon, an experimental setup called Rijke tube was used. The suppression was most effective for the pulsatile jets of 27-33 Hz pulsation frequency range and at a flow rate of 6.8 LPM. This control strategy effectively controlled the combustion instability of around 35-42 dB.•The closed loop control method is built on DAQ and Arduino using the LabVIEW interface for Arduino (LIFA).•Developed closed loop active control method was observed to be effective for suppression of thermo-acoustic instability.•Optimum position of the radial planes of micro-jets with respect to the burner was decided to improve the efficacy of the pulsatile jets towards suppression of thermo-acoustic instability.
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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 | 0.000 |
| 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) | 0.000 | 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".