Aero-thermodynamic and chemical process interactions in an axial high-pressure turbine of aircraft engines
Bibliographic record
Abstract
Precise investigation of aero-thermodynamic and chemical processes relating to environmental precursor pollutants in an aircraft turbine is challenging because of the complexity of transformation processes at high temperature and high pressure. We present here, for the first time, new insights into the study of aero-thermodynamic processes, formation of nitrate and sulfate aerosol precursors, and investigate the influence of chemical processes on aero-thermodynamics. We also shed light on the effect of three-dimensional blade profile, radial spacing, and rotor speed on the performance of a high-pressure turbine. We highlight that flow vortex and the variation of chemical formation which appear in both rear stator blades and rear rotor blades. We found that the chemical processes were affected by the evolution of temperature (maximum of 16.9%) and flow velocity (maximum of 38.8%). Contrary to the conservative one-dimensional and two-dimensional modeling, which provide only the flow trends and flow evolution at cylindrical surface, respectively, our three-dimensional modeling approach offers the possibility of combining information on radial spacing and rotor-speed effect by providing three-dimensional images of spatial-geometry effect on aero-thermodynamic and chemical processes. Quantitatively, the magnitude of change in aero-thermodynamics and nitrogen oxidation may be expected to be up to 17% and 48%, respectively, over a stage of the high-pressure turbine.
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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 | 0.000 |
| 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) | 0.001 | 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".