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Record W759720639

Failure Accommodation Tested in Magnetic Suspension Systems for Rotating Machinery

2000· article· en· W759720639 on OpenAlexaboutno aff
Andy Provenza

Bibliographic record

VenueNASA Technical Reports Server (NASA) · 2000
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetic bearingFlywheelGas compressorEngineeringReliability (semiconductor)AerospaceMechanical engineeringSuspension (topology)Electromagnetic suspensionAutomotive engineeringAerospace engineeringRotor (electric)MagnetPhysics
DOInot available

Abstract

fetched live from OpenAlex

The NASA Glenn Research Center at Lewis Field and Texas A&M University are developing techniques for accommodating certain types of failures in magnetic suspension systems used in rotating machinery. In recent years, magnetic bearings have become a viable alternative to rolling element bearings for many applications. For example, industrial machinery such as machine tool spindles and turbomolecular pumps can today be bought off the shelf with magnetically supported rotating components. Nova Gas Transmission Ltd. has large gas compressors in Canada that have been running flawlessly for years on magnetic bearings. To help mature this technology and quiet concerns over the reliability of magnetic bearings, NASA researchers have been investigating ways of making the bearing system tolerant to faults. Since the potential benefits from an oil-free, actively controlled bearing system are so attractive, research that is focused on assuring system reliability and safety is justifiable. With support from the Fast Quiet Engine program, Glenn's Structural Mechanics and Dynamics Branch is working to demonstrate fault-tolerant magnetic suspension systems targeted for aerospace engine applications. The Flywheel Energy Storage Program is also helping to fund this research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.227
Teacher spread0.217 · 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.

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
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueNASA Technical Reports Server (NASA)Same topicMagnetic Bearings and Levitation DynamicsFrench-language works237,207