Research on Material Model of Brick Block Based on Ballistic Penetration
Bibliographic record
Abstract
Abstract In the face of the need for soldiers to strike targets behind walls in military activities and counter-terrorism operations, while preventing projectiles from penetrating walls and harming other non military targets, this paper conducts research on brick blocks material models based on Autodyn software to analyze the penetration of pointed oval projectiles into 24cm thick brick walls. This article first studied the material models and obtained two sets of material models suitable for brick blocks, namely the Riedel-Hiermaier-Thomamodel material model and the Drucker Prager material model. Based on material models of different brick blocks, the finite element simulation of the penetration process of a pointed oval shaped projectile into a 24cm thick brick wall was carried out using Autodyn software, and the remaining velocity and offset angle of the projectile after penetrating the wall were obtained. By comparing and analyzing the simulated data with experimental data, it was found that the residual velocity of the brick block calculated using the RHT model had an error of 8.9% compared to the experimental results, and the error between the offset distance and the experimental results was 25%. This can accurately calculate the penetration ability of the projectile into the brick wall, providing guidance for predicting the trajectory of the projectile after penetrating the wall to a certain extent.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".