A mesoscale study on explosively dispersed granular material using direct simulation
Bibliographic record
Abstract
Explosively dispersed granular materials frequently exhibit coherent particle clustering and jetting structures. Influencing the mass concentration and related particle reaction and energy release, this phenomenon is of significant interest to the study of flow instability and mixing in heterogeneous detonation and explosion. Largely inhibited by the complex mesoscale multiphase interactions involved in the dispersal process, the underlying mechanism remains unclear. In this study, mesoscale direct simulations that capture coupled multiphase interactions and deterministic granular dynamics are conducted to investigate particle clustering and jetting formation in explosively dispersed granular payloads consisting of inert particles. Employing a mesoscale simulation framework that models particles as discrete entities and resolves the interfaces and collisions of individual particles in stochastically generated payloads with randomly distributed particle positions and sizes, numerical cases that cover a set of stochastic payloads, burster states, and coefficients of restitution are solved and analyzed. A valid statistical dissipative property of the mesoscale discrete modeling with respect to Gurney velocity is demonstrated. The predicted surface expansion velocities can extend the time range of the velocity scaling law with regard to Gurney energy in the Gurney theory from the steady-state termination phase to the unsteady evolution phase. Dissipation analysis based on the mesoscale discrete modeling of granular payloads suggests that incorporating the effects of porosity can enhance the prediction of Gurney velocity for explosively dispersed granular payloads. On the basis of direct simulations, an explanation for particle clustering and jetting formation is proposed to increase the understanding of established experimental observations in the literature.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".