Molecular Dynamics Simulations of Shock Propagation and Spallation in Amorphous Polymers
Bibliographic record
Abstract
Abstract We conducted large-scale molecular dynamics (MD) simulations of shock wave propagation and spallation in amorphous polyurethane and polyurea. First, we computed the shock Hugoniot of the polymers using the multiscale shock technique and compared them with available experimental data to establish the upper limit of the shock pressure that can be accurately modeled using a non-reactive interatomic force field. Subsequently, we simulated shock wave propagation in the polymers, varying the shock particle velocity from 0.125 km/s to 2 km/s. A remarkable similarity in the shock behavior of polyurethane and polyurea was observed. The spall strength of each sample was computed by two methods: (a) the indirect method (based on the free surface velocity history)—accessible in experiments and (b) a direct method (based on the atomic stresses in the region of spallation)—accessible only through MD. The results reveal that the tensile strength computed from the indirect method is consistently smaller than the value obtained from the direct method. Moreover, the strength computed from the indirect method shows a noticeable agreement with the fracture nucleation stress. Our results provide novel molecular-level insights into the spallation mechanisms of amorphous polymers, which could facilitate the design of polymers for structural barrier applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".