Aerodynamic Inverse Design for Viscous Flow in Turbomachinery Blading
Bibliographic record
Abstract
An inverse design method for turbomachinery blading based on a time-accurate solution of the compressible viscous flow equations on a time-varying geometry is presented. The blade pressure loading and thickness distributions are the prescribed design parameters. The blade profile is modified as it moves at a virtual velocity distribution that would make the momentum flux on the blade surfaces equal to the flux corresponding to the prescribed loading distribution. The unsteady flow due to the blade motion is simulated by solving the Reynolds-averaged Navier-Stokes equations that are discretized using a cell-vertex finite volume method in which an arbitrary Lagrangian-Eulerian formulation is used to account for the mesh movement and deformation during the design procedure. The method is first verified by inversely designing an existing blade using its loading distribution as the design target and starting from a profile that has a different camberline. The robustness, flexibility, and usefulness of this design method are demonstrated by redesigning a subsonic turbine and a transonic compressor blade for which, for the latter case, the conventional quasi-steady approach failed. The redesign cases demonstrate that the blade aerodynamic performance can be improved by carefully tailoring the target loading 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".