Non-pneumatic Tire-Mars Soil Interaction Using Advanced Computational Techniques
Bibliographic record
Abstract
The physical characteristics of Mars's soil have an impact on how easily a spacecraft can land and navigate the planet's surface. On the surface of Mars, wheeled robots known as "rovers" were planted to carry out scientific investigations on the planet's historical temperature, surface geology, and possibilities for past or current life. The challenges of guiding mobile robots across terrain that is sloping, rocky, and deformable have brought to light the significance of creating precise simulation models of the tire and mars soil interaction. In this paper, current efforts to create a terramechanics-based model of rover movement using a Non-Pneumatic (NP) tire on planetary surfaces are discussed. Since no rocks or soils have been brought back to Earth from Mars, Martian simulants are frequently used for testing rovers and other devices for Mars terrain research. Using a Finite Element Analysis-based NP tire model that is modeled and tested, in addition to a dry loose Martian soil that is modeled using Smoothed-Particle Hydrodynamics (SPH) technique and calibrated using pressure-sinkage and direct shear test. The rolling resistance coefficient on a Mars simulant can be investigated and analyzed. Results obtained from this research will validate the sustainability of NP tires for future Exploration of Mars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".