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Record W3025746302 · doi:10.23977/jemm.2020.050103

Numerical Simulation of Internal Flow Field of Centrifugal Fan for Clearing and Selecting Grain Harvester

2020· article· en· W3025746302 on OpenAlexvenueno aff
Dejian Li

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2020
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAirflowCentrifugal fanDuct (anatomy)Mechanical fanInternal flowImpellerComputational fluid dynamicsEngineeringVortexMechanicsMarine engineeringFlow (mathematics)Mechanical engineeringInletPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Aiming at the structure of the single air duct centrifugal fan in the existing full-feed grain combine harvester air sieve cleaning device, the flow channel model of the single air outlet single air duct centrifugal fan was established, and the mesh was partitioned using the MESHING software. And using CFD (Computational Fluid Dynamics) software to carry out three-dimensional numerical simulation of the internal flow field of the single-outlet single-channel centrifugal fan to determine the structure size of the fan. The improved internal flow field simulation results of the fan show that the airflow speed at the upper air outlet and the outer edge of the impeller of the single air outlet single air channel centrifugal fan is larger, and the air velocity attenuation distance is increased, which is beneficial to the airflow covering the entire screen surface; It is basically distributed in layers, and the lateral air flow at the air outlet is symmetrically distributed on the middle high side and low side, with obvious boundary effects. The influence of the fan speed, the distribution of the fan's internal flow field, the wind speed of the air outlet, and the air volume is analyzed: the wind speed and air volume of the air outlet gradually increase with the increase of the fan speed.

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 score0.722
Threshold uncertainty score0.369

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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
Published2020
Admission routes1
Has abstractyes

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