Numerical Investigations of the Aerothermal Performance of Modern Turbine Blade Tip Geometries at Design and Off‐Design Conditions and Under Stationary and Moving Shroud
Bibliographic record
Abstract
ABSTRACT High‐pressure turbine blade tips operate under extreme thermal stress, generating significant aerodynamic losses that must be continually optimized to improve engine efficiency and durability. This study uses computational fluid dynamics (CFD) to investigate the aerodynamic and thermal behavior of advanced turbine blade tip configurations, specifically GE's vertical and inclined shelf tips, under both design and off‐design transonic conditions. Conventional designs, such as flat and squealer tips, were also analyzed for comparison. Shroud motion effects were included to simulate turbine stage rotation. The simulations are performed by solving the three‐dimensional, steady and turbulent form of the Reynolds‐Averaged Navier‐Stokes (RANS) equations using the Ansys‐CFX. A two‐equation turbulence model, Shear stress transport (SST) with Gamma‐Theta transition formulation is used. CFD results showed strong alignment with experimental data, validated through isentropic Mach number and heat flux measurements. The results revealed that cavity‐based designs (squealer and shelf tips) exhibited complex secondary flow structures within the tip cavity, including the formation of suction‐side and pressure‐side cavity vortices (SSCV and PSCV), which contribute to the tip leakage vortex (TLV) and associated aerodynamic losses. The vertical shelf tip demonstrated the lowest leakage rate in both stationary and moving conditions, attributed to its narrow cavity width and reduced PSCV size, while the inclined shelf exhibited the highest heat transfer coefficient (HTC), beneficial for cooling applications but paired with higher leakage and mixing losses. Notably, these findings differ from previous results on GE's shelf tip, where the inclined shelf had the lowest leakage rate.
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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".