Characteristics of Volcanic Ash in a Gas Turbine Combustor and Nozzle Guide Vanes
Bibliographic record
Abstract
To understand the physics of volcanic ash impact on gas turbine hot-components and develop much-needed tools for engine design and fleet management, the behaviors of volcanic ash in a gas turbine combustor and nozzle guide vanes (NGV) have been numerically investigated. High-fidelity numerical models are generated, and volcanic ash sample, physical, and thermal properties are identified. A simple critical particle viscosity—critical wall temperature model is proposed and implemented in all simulations to account for ash particles bouncing off or sticking on metal walls. The results indicate that due to the particle inertia and combustor geometry, the volcanic ash concentration in the NGV cooling passage generally increases with ash size and density, and is less sensitive to inlet velocity. It can reach three times as high as that at the air inlet for the engine conditions and ash properties investigated. More importantly, a large number of the ash particles entering the NGV cooling chamber are trapped in the cooling flow passage for all four turbine inlet temperature conditions. This may reveal another volcanic ash damage mechanism originated from engine cooling flow passage. Finally, some suggestions are recommended for further research and development in this challenging field. To the best of our knowledge, it is the first study on detailed ash behaviors inside practical gas turbine hot-components in the open literature.
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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 | 0.000 | 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".