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Record W7161950490 · doi:10.82308/7354

Investigating the ride properties of a particle filled wheel for planetary mobility

2014· dissertation· en· W7161950490 on OpenAlexaboutno aff
Daniel Oyama

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAccelerationStiffnessImpulse (physics)Centripetal forceDamperSuspension (topology)TerrainShock absorber

Abstract

fetched live from OpenAlex

Ride is the isolation of passenger and cargo from terrain inputs on a movingvehicle. While most lunar rover designs assign this duty to shock absorbers, DrPeter Radziszewski and Dr Sudarshan Martins propose it be supplied in largerpart by the wheels. Their invention, dubbed iRings, consists of a 24 inch diameterchainmail tire carcass filled with thousands of polypropylene spheres. When spunbeyond a critical speed, their centripetal acceleration compresses them against thechain-mail, which lacking any structure, adopts their bulk stiffness, damping andshape. In this thesis, measurements of iRings' free response to an impulse whilespinning are analysed to create a linear single degree of freedom contact model.The model's damping ratio drops from 0.8-0.9 at 0 rpm to 0.01 at 131 rpm as bothits stiffness and damping decrease with speed. The transition occurs close to theDavis critical speed of 54 rpm. Throughout, natural frequency remains constantat 3-4 Hz despite large fluctuations in stiffness. This is likely because iRingsoscillates as a result of plastic and not elastic deformation. This model is matchedin-silico to the Canadian Space Agency's (CSA) rovers Juno and Artemis and thewhole is tested on a sinusoidal lunar analogue terrain supplied by the CSA. TheiRings wheel is found to supply comparable, but slightly inferior isolation thana pneumatic tire, the Carlisle AT-489. Nevertheless, iRings proves itself to be apassively adaptive suspension component and with improvements to its stiffness,could surpass the pneumatic wheel.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.211
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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