Micromechanical Behavior of DNA‐1A Lunar Regolith Simulant in Comparison to Ottawa Sand
Bibliographic record
Abstract
Abstract In this study, the micromechanical interparticle contact behavior of “De NoArtri” (DNA‐1A) grains is investigated, which is a lunar regolith simulant, using a custom‐built micromechanical loading apparatus, and the results on the DNA‐1A are compared with Ottawa sand which is a standard quartz soil. Material characterization is performed through several techniques. Based on microhardness intender and surface profiler analyses, it was found that the DNA‐1A grains had lower values of hardness and higher values of surface roughness compared to Ottawa sand grains. In normal contact micromechanical tests, the results showed that the DNA‐1A had softer behavior compared with Ottawa sand grains and that cumulative plastic displacements were observed for the DNA‐1A simulant during cyclic compression, whereas for Ottawa sand grains elastic displacements were dominant in the cyclic sequences. In tangential contact micromechanical tests, it was shown that the interparticle friction values of DNA‐1A were much greater than that of Ottawa sand grains, which was attributed to the softer contact response and greater roughness of the DNA‐1A grains. Widely used theoretical models both in normal and tangential directions were fitted to the experimental data to obtain representative parameters, which can be useful as input in numerical analyses which use the discrete element method.
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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 | 0.000 |
| 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) | 0.001 | 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".