Comparison of the Constituents of Two Jet Engine Lubricating Oils and Their Volatile Pyrolytic Degradation Products
Bibliographic record
Abstract
Leaking oil seals in jet engines, at locations prior to the compressor stage, can be a cause of smoke in the cabins of BAe-146 aircraft. Compressed combustion air is bled off to pressurize the cabin and to provide a source of fresh air. Bleed air is diverted from a location just prior to the combustion chamber at a temperature around 500 degrees C. To prevent oil breakdown products from entering the cabin air, catalytic converters have been used to clean the air. During an oil seal failure this device becomes overloaded and smoke is observed in the cabin. Some aircraft companies have removed the catalytic converters and claim an improvement in air quality. During an oil seal failure, however, the flight crew is potentially exposed to the thermal breakdown products of the engine oils. Because very little is known regarding the thermal breakdown products of jet engine lubrication oils, two commercially available oils were investigated under laboratory conditions at 525 degrees C to measure the release of CO, CO2,NO2, and HCN as well as volatiles which were analyzed using GC-Mass spectrometry in an attempt to see if the neurotoxic agents tricresyl phosphates (TCPs) and trimethyl propane phosphate (TMPP) would be present or formed. TMPP was not found in these experiments. Some CO2 was generated along with CO which reached levels in excess of 100 ppm. HCN and NO2 were not detected. GC compositions of the two bulk oils and their breakdown products were almost identical. The presence of TCPs was confirmed in the bulk oils and in the volatiles. Localized condensation in the ventilation ducts and filters in the air conditioning packs are likely the reason why the presence of TCPs has not been demonstrated in cabin air. It was recommended that this needed to be verified in aircraft.
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 | 0.000 |
| 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) | 0.001 | 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".