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Record W2482304645 · doi:10.4271/2016-01-1920

Carbon Silicon Carbide Brakes for a Premium Saloon - an NVH Perspective

2016· article· en· W2482304645 on OpenAlexaff
Deaglan O'Meachair, Stamatis Angelinas, Matthew Crumpton, Antonio Rubio Flores, Juan José García Pabón, Pablo Barles

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2016
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsSilicon carbideNoise, vibration, and harshnessPerspective (graphical)Materials scienceAutomotive engineeringEngineeringComputer scienceComposite materialAcousticsPhysicsVibrationArtificial intelligence

Abstract

fetched live from OpenAlex

Bentley Motors Ltd. has developed a Carbon Silicon Carbide (CSiC) brake system for its Mulsanne product, introduced at 17MY. The CSiC brake system is conceived as a performance brake system, and as such offers notable improvements in brake performance. In developing the brake system, particular focus was placed on meeting the refinement levels required for a premium product, and indeed as the flagship model for Bentley Motors, NVH refinement of the brake system was of particular concern. This paper intends to discuss the technical performance of the brake system and review the NVH performance of the brakes. Particular attention is given to the methodology employed by Bentley Motors Ltd. and IDIADA Automotive Technology S.A. in identifying NVH concerns, and proposing and validating solutions in the field, through extensive NVH endurance runs. The performance of the system is benchmarked against similar systems offered by Bentley Motors.

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 score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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