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Record W4240725839 · doi:10.1115/1.862ama_ch6

Dynamic Modeling and Analysis of Nonlinear Flexible Rotors Supported by Viscoelastic Bearings

2021· book-chapter· en· W4240725839 on OpenAlexaff
Mohammed Khair Al‐Solihat, Kamran Behdinan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsViscoelasticityNonlinear systemMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Rotors constitute a primary part of turbomachines such as pumps, compressors, and jet engines [1–3]. To analyze the dynamic behavior of rotor systems, simple rigid body dynamic models of rotors are first developed. Rigid body models of rotors, often referred to as Jeffcott rotor models, consider the rotor system as a rigid disk or cylinder rotating around its axis and supported on viscoelastic elements representing the combined shaft and bearing stiffness and damping [4, 5]. However, flexible models of rotors consider the shaft elasticity and corresponding geometric nonlinearities. Thus, these models are more efficient to predict the nonlinear behavior of the system arising from the nonlinear elastic motion of the shaft [3, 6–8].

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.208
Teacher spread0.200 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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