Numerical modelling of detonation initiation via shock interaction with multiple flame kernels
Bibliographic record
Abstract
In this study, the deflagration-to-detonation transition from the interaction of a shock wave with multiple laminar flame kernels is analyzed computationally. For comparison, both Euler equations and Navier-Stokes equations including the effects of viscosity, thermal conduction and molecular diffusion for an acetylene–air mixture model with a single-gas approximation are solved numerically to identify the dominant mechanism on the transition process. A finite-volume operating splitting scheme based on the 2nd order Godunov-type, Weighted Average Flux (WAF) method with an approximate HLLC Riemann Solver and second-order finite differences for the Navier-Stokes fluxes evaluation are used in the present computation. Adaptive mesh refinement (AMR) is employed to dynamically increase the resolution of a simulation in regions of interest around shocks, flame fronts and regions of large gradients in density using a hierarchical grid structure. The simulation results show that repeated shock–multiple flames and shock-boundary interactions lead to the acceleration of the original shock into unreacted material near the wall and subsequently the development of a hotspot explosion center. The Richtmyer-Meshkov instability caused by the interaction of the shock with subsequent flames also generates and maintains a highly turbulent flame brush. In the absence of physical diffusion in the Euler simulation, the enhanced burning rate of the turbulent flame brush is suppressed. Nevertheless, the intense flow fluctuations generated by the interactions of shocks, boundary and flames create the conditions under which deflagration-to-detonation can potentially occur at later times. A numerical study is also carried out to verify the effect of numerical grid resolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".