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Record W4231563647 · doi:10.1115/gt2018-75880

Thermomechanical Model Reduction for Efficient Simulations of Rotor-Stator Contact Interaction

2018· preprint· en· W4231563647 on OpenAlexaff
Nicolas Guérin, Anders Thorin, Fabrice Thouverez, Mathias Legrand

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsMcGill UniversitySafran Electronics (Canada)
Fundersnot available
KeywordsStatorReduction (mathematics)Rotor (electric)Krylov subspaceTurbomachineryComputer scienceWork (physics)Model order reductionThermalMechanical engineeringEngineeringMathematicsAlgorithmIterative methodPhysicsProjection (relational algebra)

Abstract

fetched live from OpenAlex

Turbomachinery rotor-stator unilateral contact induced interactions play a growing role in lifecycle analysis and thus motivate the use of accurate numerical prediction tools. Recent literature confirmed by ongoing in-house experiments have shown the importance of thermomechanical coupling effects in such interactions. However, most available (possibly reduced-order) models are restricted to the sole mechanical aspects. This work describes a reduction technique of thermomechanical models involving unilateral contact and frictional contact occurrences between rotor and stator components. The proposed methodology is grounded on Guyan and Craig–Bampton methods for the reduction of the structural dynamics in conjunction with Krylov subspace techniques, and specifically the Craig–Hale approach, for the reduction of the thermal equations. The method has the capability to drastically reduce the size of the model while preserving accuracy. It stands as a reliable strategy to perform simulations of thermomechanical models with localized mechanical and thermal loads.

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 score0.759
Threshold uncertainty score0.684

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 head0.029
GPT teacher head0.285
Teacher spread0.256 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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