Assessment and Characterization of Volcanic Ash Threat to Gas Turbine Engine Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiple volcanoes erupt yearly propelling volcanic ash into the atmosphere and creating an aviation hazard. The plinian eruption type is most likely to create a significant aviation hazard. Plinian eruptions can eject large quantities of fine ash up to an altitude of 50,000 m (164,000 ft). While large airborne particles rapidly fall, smaller particles at reduced concentrations drift for days to weeks as they gradually descend and deposit on the ground. Very small particles, less than 1 μm, can remain aloft for years. An average of three aircraft encounters with volcanic ash was reported every year between 1973 and 2003. Of these, eight resulted in some loss of engine power, including a complete shutdown of all four engines on a Boeing 747. However, no crashes have been attributed to volcanic ash. The major forms of engine damage caused by volcanic ash are: (1) deposition of ash on turbine nozzles and blades due to glassification (2) erosion of compressor and turbine blades (3) carbon deposits on fuel nozzles. The combination of these effects can push the engine to surge and flame out. If a flame out occurs, engine restart may be possible. Less serious engine damage can also occur. In most cases the major damage will require an engine overhaul long before the minor damage becomes an operational issue, but under some conditions no sign of volcanic ash is evident and the turbine cooling system blockage could go unnoticed until an engine inspection is performed. Several organizations provide aircrew procedures to respond to encounters with a volcanic ash cloud. If a volcanic ash encounter is suspected, then an engine inspection, including borescope, should be performed with particular attention given to the turbine cooling system.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it