Simulation Strategies for Shock-Turbulence Interactions
Bibliographic record
Abstract
The computational challenge of predicting shock-turbulence interactions stems from the fundamentally different physics at play. Shock waves are microscopically thin regions wherein flow properties change rapidly over a distance roughly equal to the molecular mean free path; hence, they are essentially strong discontinuities in the flow field. Turbulence, on the other hand, is a chaotic phenomenon with broadband spatial and temporal scales of motion. Most shock-capturing methods rely on strong numerical dissipation to artificially smooth the discontinuity, such that it can be resolved on the computational grid. Unfortunately, the artificial dissipation necessary for capturing shocks has a deleterious effect on turbulence. An additional problem is the fact that shock-capturing schemes are typically based on one-dimensional Riemann solutions that are not strictly valid in multiple dimensions. This can lead to anisotropy errors and grid-seeded perturbations. Other complications arising from upwinding, flux limiting, operator splitting etc., can seriously degrade performance and generate significant errors, especially in multiple dimensions. The purpose of this work is to design improved algorithms, capable of capturing both shocks and turbulence, which also scale to tens of thousands of processors. We have evaluated two new hydrodynamic algorithms, in relation to the widely used WENO method, on a suite of test cases. The new methods, referred to as the 'Compact' and 'Hybrid' schemes, show very promising results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".