Effect of Turbine Inlet Temperature on Blade Tip Leakage Flow and Heat Transfer
Bibliographic record
Abstract
One of the most critical gas turbine engine components, rotor blade tip and casing, are exposed to high thermal load. It becomes a significant design challenge to protect the turbine materials from this severe situation. As a result of geometric complexity and experimental limitations, Computational Fluid Dynamics (CFD) tools have been used to predict blade tip leakage flow aerodynamics and heat transfer at typical engine operating conditions. In this paper, the effect of turbine inlet temperature on the tip leakage flow structure and heat transfer has been studied numerically. Uniform low (LTIT: 444 K) and high (HTIT: 800 K) turbine inlet temperature have been considered. The results showed the higher turbine inlet temperature yields the higher velocity and temperature variations in the leakage flow aerodynamics and heat transfer. For a given turbine geometry and on-design operating conditions, the turbine power output can be increased by 1.48 times, when the turbine inlet temperature increases 1.80 times. Whereas the averaged heat fluxes on the casing and the blade tip become 2.71 and 2.82 times larger, respectively. Therefore, about 2.8 times larger cooling capacity is required to keep the same turbine material temperature. Furthermore, the maximum heat flux on the blade tip of high turbine inlet temperature case reaches up to 3.348 times larger than that of LTIT case. The effect of the interaction of stator and rotor on heat transfer features is also explored using unsteady simulations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".