Investigation of Viscor and Methanol Spray Dynamics Using Proper Orthogonal Decomposition in Siemens Energy Industrial Atomisers
Bibliographic record
Abstract
Abstract The demand to reduce carbon emissions has prompted research into alternative fuels that can replace conventional fuels like diesel in industrial gas turbines. Among different potential biofuels and e-fuels, methanol emerges as a sustainable and high-performance alternative to diesel for gas turbine applications. It is well established that the fuel physical properties, spray dynamics and degree of atomisation are strongly correlated and affect the engine performance. In this study, Proper Orthogonal Decomposition (POD) was applied on spatio-temporally resolved images to characterise Viscor, here used as diesel surrogate, and methanol sprays of pressure-swirl atomisers employed in Siemens Energy industrial gas turbine (SGT-400) combustors. The methanol experimental results were then compared against Viscor results at analogous operating conditions, including density adjusted atomiser pressure drop and ambient pressures. Results confirmed that the methanol spray cone angle is slightly wider than Viscor at corresponding operating conditions. The POD analysis allowed to identify dominant spatial oscillation modes and characterise them in terms of oscillation amplitude, wavelength and onset distance from the atomiser edge for both fuels. Oscillation wavelengths and maximum amplitudes were found to correlate with Weber number, average SMD and axial jet velocities.
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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) | 0.001 | 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".