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Record W4391405539 · doi:10.23977/acss.2024.080104

Dynamic Analysis of Turbocharger Rotor System

2024· article· en· W4391405539 on OpenAlexvenueno aff
Kami Mou

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsTurbochargerRotor (electric)Computer scienceAutomotive engineeringEngineeringAerospace engineeringMechanical engineeringGas compressor

Abstract

fetched live from OpenAlex

The Turbocharge Rotor System (TRS) is a key component for enhancing engine performance. However, grasping its intricate dynamic response poses a formidable challenge for modeling and analysis. This study explores the optimal parameters for the dynamic operation of a TRS. To achieve this, we employ the Jeffcott Rotor System Model and Concentrated Mass Model for dynamic modeling of TRS, and the Capone model for calculating the nonlinear oil lubrication forces. The dynamic model of the TRS was solved using in-house built MATLAB codes, followed by a parametric study to reveal the influence of specific parameters on the dynamic performance of the TRS. This study ultimately provides us with the optimal values of these parameters, which can be valuable for guiding the design and optimization of TRS in practical scenarios.Top of Form

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 score0.910
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.225
Teacher spread0.221 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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