Peculiarity of moving weak shock computations: Entropy generation analysis of numerically expressed shock waves
Bibliographic record
Abstract
Shock-capturing schemes using a finite volume method represent shock waves using a specific number of cells. Although understanding the behavior of these “numerical shock waves” is crucial owing to their distinct characteristics, which differ from those of theoretical and physical shock waves, moving weak and moderate shock waves have not been thoroughly investigated. In this study, a numerical test and an analysis were conducted to elucidate the properties of numerical moving shock waves. Our numerical test revealed that the outcome is influenced by the shock strength and numerical flux functions. The final state of a moving shock wave can be classified into three regimes (dissipated, transitional, and thinly captured regimes) depending on the shock strength. Peculiarly, weak shocks faced the dissipated regime, and the dissipation was serious at a shock Mach number of 1.01. Computational results were examined with respect to the entropy generation resulting from the propagation of the numerical moving shock wave. The analysis clarified that the thickness of numerical moving shock waves was determined to ensure physically accurate entropy generation. Furthermore, it was clarified that a moving weak shock wave cannot be accurately represented with a thin profile owing to excessive entropy production. This difficulty in handling a small amount of entropy production resulted in the peculiarity of the weak shock wave computations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".