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Record W4214914001 · doi:10.5267/j.esm.2022.1.001

An investigation on dynamic behavior of rotating shafts using a pipe elbow finite element formulation

2022· article· en· W4214914001 on OpenAlexvenueno aff
M. Sajjadpour, S.A. Hosseini Kordkheili

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsFinite element methodRotor (electric)Degrees of freedom (physics and chemistry)Structural engineeringEngineeringGas compressorMechanical engineeringRotordynamicsGyroscopeComputer sciencePhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Rotating shafts have a vast application in various industries especially in the aerospace industry such as engines, compressors and turbines. The researchers have performed considerable efforts on the rotating shafts’ dynamic behavior because of their sensitivity to the rotor specifications and different parameters such as supports. In this paper by employing a pipe elbow element, an especial finite element formulation is derived to investigate dynamic behavior of rotating shaft in the presence of support clearance. The proposed element consists of four nodes with twenty-four degrees of freedom, which also accounts for the shear and gyroscopic effects. Within a finite element analysis framework, the focus of the paper is proposing a formulation to account for the dynamic behavior of a rotating shaft with much less number of elements. The element is implemented in a finite element code and then is used to model and analyze some rotating shaft examples. In order to verify the developed formulation, results are compared with those obtained from other schemes reported in the literature.

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: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.928

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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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
Published2022
Admission routes1
Has abstractyes

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