Effects of activation energy on the instability of oblique detonation surfaces with a one-step chemistry model
Bibliographic record
Abstract
A numerical study was performed to investigate the detailed effects of activation energy Ea on the oblique detonation wave surface instability. Numerical simulations were performed using an ideal reactive flow model given by the inviscid Euler equations with one-step irreversible Arrhenius reaction kinetics. The numerical results demonstrate two types of unstable structures following the initial smooth surface after detonation initiation. One exhibits by a “saw-tooth” reactive front and the other exhibits by a “keystone” feature. To quantify the destabilization processes, two characteristic length scales, L1 and L2, are defined statistically to be the length of the smooth detonation surface before the appearance of instabilities and the length of the unstable surface before the first cellular structure with the onset of right-running transverse waves, respectively. Their dependence on Ea was simulated and analyzed. In general, both lengths decrease with increasing Ea, making the surface more unstable. However, with increasing Ea, the high temperature sensitivity of the mixture causes an abrupt explosion in the initiation region, introducing a high overdriven surface and suppressing the instability. With the balance between the destabilizing effect of Ea and the stabilizing effect of increasing overdrive factor, both L1 and L2 are found to approach a near-constant value in the high Ea limit.
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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 | 0.001 |
| 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) | 0.002 | 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".