Multi-Objective and Multi-Point Aerodynamic Optimization of Transonic Fan Blades
Bibliographic record
Abstract
A multi-objective and multi-point optimization methodology is developed for aerodynamic design of transonic fan blades. The optimization method aims to increase design efficiency, near stall efficiency and stall margin while maintaining the required design pressure ratio and high speed choke margin. Numerical analyses are performed by solving three-dimensional Reynolds-Averaged Navier-Stokes equations combined with shear stress turbulence model. A multi-level blade parameterization is employed to modify the blade geometry. The proposed method is applied to redesign NASA rotor 67. First, an optimization case with considering two operating conditions at peak efficiency and near stall is performed to demonstrate the relation between near stall efficiency and stall margin. An investigation on Pareto optimal solutions of this optimization shows that the stall margin is increased with improving near stall efficiency. Then, in order to maintain the required choke margin, an operating point at high speed choked flow is added to the optimization process. A final optimized design is selected by considering the interaction of design requirements at all three operating points. The new design presents higher efficiency and stall margin without any reduction in the chocking mass flow 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".