CFD Results for Shock-Boundary Layer Flow Control with Micro-ramps at Various Grid Densities
Bibliographic record
Abstract
CFD was used to simulate the effect of micro-ramps on a shock-boundary layer interaction. A single ramp geometry and flow condition were simulated with fixed grids of various mesh densities, and also with an adaptive grid which locally refined the mesh to resolve flow features as they developed. Within the grid study was an effort to assess the effect of various techniques for approaching a converged solution. The intent was to determine if the converged solution depended upon the order in which the boundary layer, incident shock, and flow control ramp were introduced into the CFD solution. The Splitflow CFD code was used for the simulations because of its automatic self-generated grids and adaptive capability. Splitflow refines and focuses grid cells near features in the solution and/or geometry, and it also allows the addition of geometry features (e.g., flow control ramp) during the convergence of the solution. The result was that for coarse grids, the separation location and size remained fairly unaffected by the technique used to approach convergence. However, for moderate and fine grids, the location of the separation was strongly influenced by the order in which the incident shock and flow control ramp were introduced. The size of the separation is influenced by the grid resolution surrounding the incident shock as well as the grid surrounding the separation itself. This indicates that the numerical solution is not unique. Experimental test data favored the CFD solution with the separation location downstream and outboard of the ramp (attached flow directly behind the ramp). This corresponded to the CFD results when the incident shock was added to the solution before the ramp was introduced. However, the non-uniqueness of the numerical solution could be indicative of non-uniqueness in the physical solution, depending upon the actual testing conditions. I.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".