Multi-Scale Modeling Of High Velocity Impact On Alumina Ceramic Tiles
Bibliographic record
Abstract
Multi-scale numerical modeling informed and validated by experiments is a powerful engineering tool for optimization and design of structures subject to complex loading (e.g., dynamic impact loads). The choice of the material model and the computational framework is important because it influences the accuracy of predictive results. In this study, we primarily focus on structural-scale modeling of ceramics under high-velocity ballistic impact by incorporating the user-defined Johnson-Holmquist-Beissel (JHB) material model within the framework of Smoothed Particle Hydrodynamics (SPH) in LS-DYNA modeling software. After ensuring that the implementation of the JHB material model was correct by comparing equivalent stress-pressure plots through a single element simulation test, we draw upon experimental results published in the literature to validate the model, where we focus on the damage patterns, cone size, residual mass, and residual velocity of the projectile. In addition, the effects of some numerical settings of SPH method through parametric studies were also investigated in this work, including particle spacing, artificial viscosity coefficient, numerical control, and contact treatment. The observations of parametric difference were then used to determine appropriate values for SPH model parameters of the ballistic impact problem. Comparing the results of the experiments and simulation, we observed a good agreement, and this indicates the present material modeling framework can realistically simulate the response of ceramic upon high-velocity impact, and SPH is well suited for impact problems. Overall, this study provides the guidance for development of future multi-scale numerical models and structural scale design of protection systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".