Effect of Activation Energy on Detonation Cellular Dynamics and Reinitiation Behaviors
Bibliographic record
Abstract
Two-dimensional simulations of detonation propagation in a channel filled with stoichiometric hydrogen–air mixture with unity Lewis number using the chemical-diffusive model (CDM) coupled with compressible Navier–Stokes equations are presented. Specifically, the effect of four activation energies ([Formula: see text], and 10) with CDM on detonation cell structures, cellular dynamics, and reinitiation behaviors is discussed. As [Formula: see text] increases, detonation cell size increases and the cellular structure becomes more irregular. Spectral analysis by the auto-correlation function is performed to provide quantitative insights about detonation cell size and irregularity. Furthermore, detailed analysis on the detonation wavefront captures three distinct detonation propagation modes, including stable detonation ([Formula: see text]), weakly unstable detonation ([Formula: see text]), and highly unstable detonation ([Formula: see text]). The effect of activation energy in detonation attenuation is further studied through a detonation propagation over a semicylinder obstacle, where two distinct detonation attenuation regimes are captured, including unattenuated detonation transmission ([Formula: see text]) and critical detonation reinitiation ([Formula: see text]). The mechanism of the critical detonation reinitiation event is further examined. It is found that a strong transverse detonation wave forms at higher activation energies after the Mach shock reflection at the bottom wall, which eventually leads to a steady detonation propagation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".