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Record W2595641253 · doi:10.3390/lubricants5010007

Rotordynamic and Friction Loss Measurements on a High Speed Laval Rotor Supported by Floating Ring Bearings

2017· article· en· W2595641253 on OpenAlexaboutno aff
Rob Eling, Mathys te Wierik, Ron A.J. van Ostayen, Daniel J. Rixen

Bibliographic record

VenueLubricants · 2017
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsTurbochargerBearing (navigation)Rotor (electric)RepeatabilityAutomotive engineeringTest benchEngineeringMechanical engineeringAutomotive industryOil pressureGas compressorComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Floating ring bearings are the commonly used type of bearing for automotive turbochargers. The automotive industry continuously investigates how to reduce the bearing friction losses and how to create silent turbochargers. Many of these studies involve creating a numerical model of the rotor-bearing system and performing validation on a test bench on which a turbocharger is driven by hot gases. This approach, however, involves many uncertainties which diminish the validity of the measurement results. In this study, we present a test setup in which these uncertainties are minimized. The measurement results show the behavior of the floating ring bearing as a function of oil feed pressure, oil feed temperature, rotor unbalance and bearing clearances. Next to an increased validity, the test setup provides measurement data with good repeatability and can therefore represent a case study which can be used for validation of rotor-bearing models.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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