Virtual soil calibration for wheel–soil interaction simulations using the discrete-element method
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it