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Record W3085851113 · doi:10.1177/0957650920957465

Multidisciplinary assessment of blade number and manufacturing parameters for the performance of centrifugal fans

2020· article· en· W3085851113 on OpenAlexaff
Jun Ni, Rui Liu, Yu Sun

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy · 2020
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCentrifugal fanBlade (archaeology)AerodynamicsParametric statisticsEngineeringParametric designMechanical engineeringAirflowPower (physics)Mechanical fanStructural engineeringAerospace engineeringMathematicsInletPhysics

Abstract

fetched live from OpenAlex

This work targets the development of the multidisciplinary design for centrifugal fans, based on the calculation of the fan performance following changes in the fan structure and manufacture. A parametric method that starts from the blade number and manufacturing parameters is first developed to control the shape of fan assembly and its fluid domain. The parametric code, and the aerodynamic and structure analysing codes are then integrated, from the solution of which algorithms are developed to calculate the fan indexes including the flow rate, pressure rise, fan efficiency, fan strength, and the motor power. Validation of the proposed method is performed by the comparison with an experimental test. An orthogonal sampling opens out relevant changes of the fan working performance with respect to the blade number and manufacturing parameters. Results suggest ranges for the blade number and manufacturing parameters that have a sound performance and structure safety when converting the motor power to airflow.

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 score0.329
Threshold uncertainty score0.236

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part A Journal of Power and EnergySame topicTurbomachinery Performance and OptimizationFrench-language works237,207