Inverse Design of Turbine and Compressor Stages Using a Commercial CFD Program
Bibliographic record
Abstract
An aerodynamic inverse method that is developed for viscous flow over airfoils, is implemented into ANSYS-CFX as a User-Defined Function (UDF). The implementation is validated, it is then assessed in the redesign of a compressor and a turbine stage in two-dimensional visocus flow. In the inverse method, one of the design choices is to prescribe a target pressure distribution on the airfoil surfaces. The airfoil walls are assumed to be moving with a virtual velocity that would asymptotically drive the airfoil to the shape that would correspond to the specified target pressure distribution. This virtual velocity distribution is computed from the difference between the current and the target pressure distributions. The inverse design approach is fully consistent with the viscous flow assumption and is independent of the CFD approach taken. The Arbitrary Lagrangian-Eulerian formulation of the unsteady Reynolds-Averaged Navier Stokes equations is solved in a time accurate fashion with the airfoil motion being the source of unsteadiness. At each time step, the airfoil shape is modified and dynamic meshing is used to remesh the fluid flow domain. An axial compressor stage and turbine stage are redesigned using ANSYS-CFX running in inverse mode so as to demonstrate the ability of this approach to improve the aerodynamic performance of both compressor and turbine stages.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".