Characterization of Ottawa Sand and Application to Blast Simulations
Bibliographic record
Abstract
The present work aims at presenting consistent data both from laboratory characterization and from blast tests to see how a computer model performs when only data from mechanical tests are being fed to the constitutive model. A sand (Ottawa 20-30) that meets ASTM C778 requirements, i.e., well characterized microscopically, was tested in triaxial compression under confinement pressures ranging from 50 to 300 MPa and moisture contents of 0 to 15% as well as high strain-rates. These tests provided both the experimental equation of state (pressure vs. volume) and compaction curves as well as the strength vs. pressure properties to build a constitutive model both in LS-DYNA and CTH. Blast tests were subsequently performed by burying explosive at three different depths inside a sand pot with a rigid steel plate on top. During flight, the height of the steel plate was tracked with Phantom high-speed cameras to determine the impulse transmitted to the plate as well as the maximum jump height. Simulations were performed with both an Eulerian code (CTH) and a Lagrangian/ALE code (LS-DYNA) using the constitutive model determined during the material characterization. The predictions of both codes are as close as 7% and as far as 22%, depending on the test configuration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".