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Record W7132444264

Commissioning of a high speed rolling-element bearing rig: preliminary results

2018· article· en· W7132444264 on OpenAlexvenueno aff
A. Dadouche, R. Kerrouche, S. Boukraa

Bibliographic record

VenueNPARC · 2018
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBearing (navigation)Ball (mathematics)Ball bearingRotational speedMain bearingLubricationThrottle
DOInot available

Abstract

fetched live from OpenAlex

Rolling element bearings represent critical components in rotating machinery especially aircraft engines and accessory gearboxes. They are required to operate reliably under high temperature and speed conditions well over 3 million DN (bearing bore in millimeter x shaft speed in rpm) where hybrid technology is being investigated by numerous engine manufacturers. As a result, there has been an enormous amount of work done in the field over the last five decades where different test rigs have been built for that purpose. For instance, Holmes tested ball bearings with a 125 mm bore at speeds up to 24 krpm (3.0 MDN) to determine skidding characteristics. Similarly, in their experimental work, Bamberger et al. studied the effect of speed and load on the performance operation of a 120 mm bore angular contact ball bearing. They found that significant skidding occurred at the highest speed of 25 krpm. This paper describes the operation of a high-speed bearing rig instrumented with state-of-the-art sensors to study performance characteristics of rolling-element bearings operating at extreme conditions. A typical aircraft engine roller bearing was used during the commissioning phase of the rig. Preliminary results of the main bearing characteristics under controlled operating conditions are presented.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.397

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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
Published2018
Admission routes1
Has abstractyes

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