A numerical investigation of the transition from regular reflection to Mach reflection in air
Bibliographic record
Abstract
The transition from regular to Mach reflection for the reflection of a planar shock from a wedge in an air-filled shock tube is investigated numerically. Both inviscid and viscous high fidelity computational fluid dynamics methods are used to examine the effects of wall and surface boundary layers on the transition boundary. An innovative method is implemented to determine numerically the transition boundary by varying the shock strength and wedge angle. The numerically predicted boundaries, with and without boundary layers, are then compared not only to theoretical transition criteria based on standard two- and three-shock analyses that neglect the presence of boundary layers, but also to experimental transition boundary data that is inherently influenced by boundary layer effects. The objective of this numerical study is to help resolve the von Neumann paradox as to why experimental regular reflections persist beyond the analytically predicted transition boundaries into the Mach reflection domain. It is shown that the numerical results of the Navier-Stokes equations for viscous flows can well reproduce the persistence of regular reflection into the Mach reflection domain observed in physical experiments while the numerical results of the Euler equations for inviscid flows are in close agreement with the predictions given by the inviscid theory.
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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 | 0.002 |
| 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) | 0.001 | 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".