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Record W3162900414 · doi:10.1177/09544070211015525

Hysteresis loss of ultra-large off-the-road tire rubber compounds based on operating conditions at mine sites

2021· article· en· W3162900414 on OpenAlexafffund
Shaosen Ma, Guangping Huang, Khaled Obaia, Soon Won Moon, Wei Victor Liu

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsSyncrude (Canada)University of Alberta
FundersSyncrude
KeywordsTreadNatural rubberHysteresisMaterials scienceStrain (injury)Composite materialUltimate tensile strengthTensile strain

Abstract

fetched live from OpenAlex

The objective of this study is to investigate the hysteresis loss of ultra-large off-the-road (OTR) tire rubber compounds based on typical operating conditions at mine sites. Cyclic tensile tests were conducted on tread and sidewall compounds at six strain levels ranging from 10% to 100%, eight strain rates from 10% to 500% s −1 and 14 rubber temperatures from −30°C to 100°C. The test results showed that a large strain level (e.g. 100%) increased the hysteresis loss of tire rubber compounds considerably. Hysteresis loss of tire rubber compounds increased with a rise of strain rates, and the increasing rates became greater at large strain levels (e.g. 100%). Moreover, a rise of rubber temperatures caused a decrease in hysteresis loss; however, the decrease became less significant when the rubber temperatures were above 10°C. Compared with tread compounds, sidewall compounds showed greater hysteresis loss values and more rapid increases in hysteresis loss with the rising strain rate.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.115
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

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