Virtual soil calibration for wheel–soil interaction simulations using the discrete-element method
Bibliographic record
Abstract
Lunar mobility studies require a precise knowledge of the geotechnical properties of the lunar soil when it comes to design-adapted and efficient-traction systems. The remarkable progress of computers since the Apollo missions allows direct testing of the performance of new design prototypes through simulations of soil-structure interactions using the discrete-element method (DEM). Before simulating traction-system displacements on the soil, the virtual-soil parameters need to be calibrated. This study presents a systematic method for calibrating a granular soil through four steps: (1) measurement of three of the real-material properties through two experiments, (2) determination of the design variables defining the virtual soil, (3) construction of surrogate models for the virtual-material properties as a function of the design variables via simulated experiments, and (4) optimization of the design-variable values to fit the virtual-soil properties to the real-soil values. Two different experiments, a direct-shear test and an angle-of-repose measurement, were used to determine the following material properties: cohesion, internal angle of friction, and angle of repose. Optimum DEM parameters were computed to characterize two types of soil: silica sand, based on an experimental direct-shear test and angle-of-repose measurements, and lunar regolith, based on data from the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".