Detecting and characterizing thermoacoustic oscillations in an industrial combustor with the spectral proper orthogonal decomposition
Bibliographic record
Abstract
This study demonstrates how thermoacoustic oscillations occurring at multiple frequencies in a combustor with mechanical vibrations can be detected and characterized using high-speed chemiluminescence imaging and the time-domain Spectral Proper Orthogonal Decomposition (SPOD). Three turbulent premixed methane-air flames with different morphologies are presented: an M-flame, a Bunsen flame, and a third case featuring a bistable flame that intermittently transitions between these two flame shapes. The analysis of these cases demonstrates how the SPOD can be used to (1) separate the influence of mechanical vibrations on the chemiluminescence measurements from thermoacoustic phenomena taking place far from the natural frequencies of a burner, (2) characterize large amplitude oscillations in space and time with modes that describe periodic phenomena with both wide- and narrow-band spectral signatures, and (3) reveal deterministic dynamics with low signal-to-noise ratios that were not easily detectable in the power spectral densities (PSDs) of either the spatially averaged OH ∗ chemiluminescence intensity or the pressure signal recorded upstream of the flame. In addition, this article also discusses how the SPOD enabled the identification of a correlation between the decay of a thermoacoustic mode and the instantaneous shape of a bistable flame.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".