Prediction of PAN oxidation in a gas turbine bearing chamber using coupled chemical kinetics and CFD simulation of lubricant flow
Why this work is in the frame
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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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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