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Record W2954296383 · doi:10.23977/jemm.2018.31002

Numerical Analysis of Coating Device based on Fluent

2018· article· en· W2954296383 on OpenAlexvenueno aff
Zhe Chen, Bai Ziyan, Chen Xinxuan

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2018
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCoatingFluentDie (integrated circuit)Materials scienceExtrusionCompression (physics)SlurryComposite materialFlow (mathematics)Mechanical engineeringEngineering drawingComputer simulationComputer scienceMechanicsSimulationEngineeringPhysics

Abstract

fetched live from OpenAlex

According to the applicable conditions of coating quality of two different coating models in the extrusion coating of lithium battery, the flow state of the slurry in the inner flow channel of the extrusion die is simulated by Fluent software. The simulation results show that the coating uniformity of the two models basically meets the requirements. The FLUID1 die is superior to the FLUID2 die in terms of speed uniformity and loss. From the analysis of the uniformity of the outlet pressure, the FLUID1 pressure uniformity at the outlet is excellent with FLUID2.However, when considering the overall compression of the die, FLUID2 can also be selected. The suitable coating style depends on the different craft and coating skill.

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 score0.940
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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
Published2018
Admission routes1
Has abstractyes

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