Large-Eddy Simulation and Conjugate Heat Transfer Around a Low-Mach Turbine Blade
Bibliographic record
Abstract
Determination of heat loads is a key issue in the design of gas turbines. In order to optimize the cooling, an exact knowledge of the heat flux and temperature distributions on the airfoils surface is necessary. Heat transfer is influenced by various factors, like pressure distribution, wakes, surface curvature, secondary flow effects, surface roughness, free stream turbulence, and separation. Each of these phenomenons is a challenge for numerical simulations. Among numerical methods, large eddy simulations (LES) offers new design paths to diminish development costs of turbines through important reductions of the number of experimental tests. In this study, LES is coupled with a thermal solver in order to investigate the flow field and heat transfer around a highly loaded low pressure water-cooled turbine vane at moderate Reynolds number (150,000). The meshing strategy (hybrid grid with layers of prisms at the wall and tetrahedra elsewhere) combined with a high fidelity LES solver gives accurate predictions of the wall heat transfer coefficient for isothermal computations. Mesh convergence underlines the known result that wall-resolved LES requires discretizations for which y+ is of the order of one. The analysis of the flow field gives a comprehensive view of the main flow features responsible for heat transfer, mainly the separation bubble on the suction side that triggers transition to a turbulent boundary layer and the massive separation region on the pressure side. Conjugate heat transfer computation gives access to the temperature distribution in the blade, which is in good agreement with experimental measurements. Finally, given the uncertainty on the coolant water temperature provided by experimentalists, uncertainty quantification allows apprehension of the effect of this parameter on the temperature distribution.
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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".