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Record W4293240853 · doi:10.18280/mmep.090210

Examination of the Airflow Uneven Distribution over the Combine Harvester Cleaning System

2022· article· en· W4293240853 on OpenAlexvenueno aff
Ildar D. Badretdinov, Салават Мударисов, Damir Khaliullin

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsAirflowDuct (anatomy)Combine harvesterProcess (computing)Mechanical engineeringEngineeringAcousticsComputer scienceAutomotive engineeringSimulationMarine engineeringPhysics

Abstract

fetched live from OpenAlex

The research aims to study the uneven distribution of airflow created by a fan over the cleaning system of a combine harvester, affecting the efficiency of the technological process of separating impurities from the original crop. The article presents a methodology for studying the actual operation process using a digital twin, reveals problem areas and studies even distribution of the airflow at the outlet of the fan discharge channel of the combine harvester cleaning system. During the research, several parameters were defined. Based on the digital twin development and study, the airflow rate at the outlet of the radial fan discharge duct of the combine harvester (CH) cleaning system at different rotation rates of the fan wheel (450-1050 min-1) was determined. Experimental measurements of the airflow distribution over the working part of the sieve shoe for the existing cleaning system and modern combine harvesters made up 3.75-10.2 m/s.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.193
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes1
Has abstractyes

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