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Record W2140638370 · doi:10.5539/mer.v3n1p170

Design analysis of Hub, Rim and Drum in Brake Assembly

2013· article· en· W2140638370 on OpenAlexvenueno aff
V. Ramamurti, T Sukumar, S. Mithun, N. Prabhakar, P. Hudson

Bibliographic record

VenueMechanical Engineering Research · 2013
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsBrakeDrumAutomotive engineeringTorqueElectronic brakeforce distributionHydraulic brakeAxleTransient (computer programming)EngineeringBrake padPower (physics)Dynamic brakingStructural engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Stress analysis connected with the brake assembly of heavy vehicles is a complicated problem in view of the machine elements involved. The hub (on the rear axle), the rim (holding the wheel) and the drum (holding the brake shoe) experience severity of loads. While the vehicle is being driven the power is transmitted from the hub to the rim. When the brake is applied, the brake drum receives the braking torque and communicates it to the rim. Analysis associated with braking is actually transient since the braking torque varies with time in a short period of time whereas the one associated with driving is predominantly steady while the vehicle moves with uniform speed. None of them can be considered rotationally symmetric. Even though 3D brick element can be used for modelling all the three members, the computational effort needed to handle the problem of braking becomes extremely cumbersome. Hence a compromise solution is presented in this paper.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.054
GPT teacher head0.311
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2013
Admission routes1
Has abstractyes

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