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Record W4393147277 · doi:10.1139/tcsme-2023-0147

Structural optimization design of connection between centrifugal impeller and shaft end based on finite element method

2024· article· en· W4393147277 on OpenAlexvenueno aff
Yanjun Li

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerFinite element methodConnection (principal bundle)Structural engineeringMechanical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

A centrifugal compressor is the core equipment of the chemical industry. To solve the problems of assembly difficulties and poor mechanical properties caused by the connection between impeller and shaft end in existing compressor, this paper takes a small geared air compressor designed by Shenyang Borui Turbine Technical Service Co., Ltd. as an example and designs its first-stage impeller and shaft end into three structural forms of tie rod–pin connection, tie rod–end key connection, and flange connection, respectively, to find an optimal connection mode between the centrifugal impeller and shaft end. Firstly, the calculation method is verified. Then, the stress, strain, deformation, and critical rotor speed of these three structures are compared and analyzed based on multi-parameter genetic optimization method. The results show that when the impeller and shaft end are connected by the tie rod–end key structure, compared to the tie rod–pin connection structure and flange connection structure, the maximum stress is reduced by 43.8% and 61.2%, the strain is reduced by 43.8% and 61.8%, the deformation is reduced by 3.64 times and 6.27 times, respectively, and the resonance margin will also be greatly improved. The research results in this paper provide a theoretical basis for the reliable design of the connection between the centrifugal impeller and shaft end.

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: Methods · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.530

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMechanical Engineering and Vibrations ResearchFrench-language works237,207