Off-Road Soft Terrain Modeling Using Smoothed Particle Hydrodynamics Technique
Bibliographic record
Abstract
Soil modeling and calibration are the preliminary steps towards tire-soil interaction prediction. This paper presents soil calibration methods using Smoothed Particle Hydrodynamics technique (SPH). Calibration of soil comprises several models including dry sand, dense sand, and clayey soil. Furthermore, snow terrain properties provided in terramechanics literature are investigated and modeled using SPH technique. First, the soil material properties are collected from terramechanics published data. Then, two soil validation tests are performed to predict soil characteristics, namely the pressure-sinkage test and the direct shear-strength test. Both tests are repeated while varying targeted parameters, and compared to results provided from terramechanics physical measurements. Finally, optimal soil results are presented and discussed. The objective of this paper is to accurately model and validate SPH terrain materials. Additionally, the sensitivity of several SPH parameters and their effect on soil behavior is investigated. The results obtained from this study will be beneficial for modeling and simulation of tire-terrain interaction research.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".