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Record W2118998693 · doi:10.1109/tmag.2008.920463

Nonlinear Bearing Force and Torque Model for a Toothless Self-Bearing Servomotor

2008· article· en· W2118998693 on OpenAlexaff
Thomas R. Grochmal, C.P. Forbrich, Alan F. Lynch

Bibliographic record

VenueIEEE Transactions on Magnetics · 2008
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTorqueBearing (navigation)Control theory (sociology)Nonlinear systemServomotorComputer scienceLevitationFinite element methodFriction torqueMagnetPhysicsMechanical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

We report developments in the modeling of bearing force and torque for a toothless self-bearing servomotor (TSBS), a permanent-magnet synchronous machine that utilizes the Lorentz force for levitation and torque simultaneously. The contactless nature and toothless construction of the device results in smooth torque production, making the TSBS ideal for precision pointing and slewing applications. We present, for the first time, nonlinear analytical expressions for force and torque based on first principle modeling. The parameters are identified from system data using linear least squares. The resulting nonlinear model aligns very well with finite-element analysis predictions. This approach has advantages over previous modeling efforts, which were purely analytical and always yielded linearized force and torque expressions.

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 score0.618
Threshold uncertainty score0.986

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 head0.019
GPT teacher head0.223
Teacher spread0.203 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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