A mesoscale study on explosively dispersed granular material using direct simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it