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Record W2800510843 · doi:10.1139/tcsme-2015-0040

MINIATURE FLUID DYNAMIC BEARING WITH IMPROVED LOAD CAPACITY

2015· article· en· W2800510843 on OpenAlexvenueno aff
Chien-Sheng Liu, Min-Kai Lee, Ying-Chi Chuo

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsLoad bearingBearing (navigation)Computer scienceMechanical engineeringEngineeringStructural engineering

Abstract

fetched live from OpenAlex

In this paper a novel design is proposed to improve the load capacity of fluid dynamic bearing (FDB) for miniature spindle motors and small-form-factor data storage applications. In contrast to conventional miniature FDB with two sets of herringbone grooves on its inner surface, the proposed miniature FDB comprises another one set of herringbone grooves on its outer surface. The proposed miniature FDB is verified numerically utilizing commercial software Advanced Rotating Machinery Dynamics (ARMD). The simulation results show that compared to the conventional miniature FDB, the proposed miniature FDB can obviously improve the load capacity of the bearing system. Overall, the results presented in this study show that the proposed miniature FDB provides another solution for miniature spindle motor applications.

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: none
Teacher disagreement score0.936
Threshold uncertainty score0.751

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 head0.010
GPT teacher head0.181
Teacher spread0.172 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

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