An inverse design method for viscous flow in turbomachinery blading using a wall virtual movement
Bibliographic record
Abstract
An inverse shape design method for turbomachinery blades based on a time-accurate solution of the viscous flow equations is presented. The design scheme is formulated such that either the blade pressure distributions on pressure and suction surfaces, or the blade pressure loading and its thickness distribution can be prescribed as design variables. The blade profile is modified using a virtual velocity distribution that would make the momentum flux on the blade surfaces equal to the design momentum flux. The flow is simulated by solving the Reynolds-averaged Navier–Stokes equations that are discretized using a cell vertex finite-volume method, where an arbitrary Lagrangian–Eulerian formulation is used to account for mesh movement. An algebraic Baldwin–Lomax model is used for turbulence closure. The inverse method is first validated for a transonic compressor cascade; it is then used to redesign a subsonic turbine and a transonic compressor. The results show that the design method is rather robust, flexible and useful in reshaping the blade geometry to achieve the prescribed design variables. They also indicate that by carefully tailoring the design target, significant improvement can be achieved in the blade aerodynamic performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".