Prediction of PAN oxidation in a gas turbine bearing chamber using coupled chemical kinetics and CFD simulation of lubricant flow
Bibliographic record
Abstract
A Computational Fluid Dynamics (CFD) model, using COMSOL 6.1, was developed in this work to simulate the oil temperature distribution within a bearing housing, to provide a means of predicting the most probable zones for PAN oxidation. Three different zones in the radial direction and two distinct zones in the axial direction were specified with different temperature profiles. It was also found that the rotational speed of the rotor and oil outlet pressure can significantly influence the temperature distribution. Oil inlet temperature was the other factor that had a minor impact on the temperature profile. Furthermore, the highest temperatures were observed in the bulk oil in the area surrounding the rotor. A chemical reaction analysis, which was performed using MATLAB R2022a, was performed to estimate the rate of PAN oxidation. According to the final results; higher rotational speeds increase the rate of oxidation. Moreover, a reduction in revolution speed extends the time required to completely consume the original PAN content. These findings were also used to demonstrate where varnish deposits start to form. Multiple temperature zones were used instead of the average temperature to carefully check the mutual relation between the revolution speed and temperature, and accurately calculate the rate of PAN oxidation reaction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".