Multi-objective design optimization of compliant lunar wheels
Bibliographic record
Abstract
The development of the wire-mesh wheel of the Apollo Lunar Roving Vehicle was realized through a time consuming trial and error design process, primarily driven by manufacturability and physical testing. Recent wheel development, motivated by renewed interest in lunar surface exploration, utilizes more sophisticated numerical simulation tools. However, many researchers still employ trial and error or parametric approaches to designing the wheels. This thesis proposes a systematic approach to the design optimization of compliant lunar wheels. The problem is decomposed into system and component level analyses. The system level analysis investigates the effect of elastic wheel behaviour on rover and mission performance metrics. This is realized by optimizing concept independent wheel design variables using multi-disciplinary models coupled with optimization algorithms. Wheel concepts are explored by prototyping and physical testing, as well as numerical modelling. The mobility performance metrics of cellular, segmented and iRings wheels are compared to a baseline rubber wheel. In the component level analysis, a multi-objective optimization algorithm is coupled with numerical simulations of wheel-ground interaction to find optimal cellular wheel designs. The effectiveness of the methodology to optimize cellular wheel concepts is verified, and the limitations of the approach examined. Finally, a discussion to extend the proposed methodology to alternative wheel concepts is provided.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".