Development of closed-loop active control method for suppression of thermoacoustic instability using radial air micro-jets
Bibliographic record
Abstract
Thermoacoustic instabilities present in the combustor of power producing devices are having adverse effects on the performance. To avoid thermoacoustic instabilities, design of control method is very much essential. Design and development of a closed loop control method is a real challenge for combustor. Active control methods are advantageous than passive methods. The characterization of thermoacoustic instability is essential for effective design of control method. The selection of appropriate controller and it's design depends on characterization of thermoacoustic instabilities. In this method the feedback signal acquired from microphone is used to control the flow rate of radial micro-jets. The developed method is implemented effectively to suppress thermoacoustic instabilities in a one dimensional combustor (Rijke tube). The airflow to the radial micro-jets injector was controlled using a control unit which consist of a stepper motor coupled with a needle valve, and an airflow sensor. Radial micro-jets are used to break a coupling and act as an active closed-loop method. The control method used radial jets effectively to control the thermoacoustic instability and reduces sound pressure level to background level (100 dB to 44 dB) in short span of time (10 Second).•LabVIEW Interface for Arduino (LIFA), LabVIEW, and DAQ are very useful in developed closedloop active control method.•Developed closed loop active control method is very effective for suppression of thermoacoustic instability.•Developed closed loop active control method used air in the form micro jets to control thermoacoustic instabilities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".