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Record W4296097671 · doi:10.1002/cjce.24641

Multiple physical field coupling simulation of high current lithium electrolyzer in industry

2022· article· en· W4296097671 on OpenAlexvenueno aff
Lili Wu, Wenying Li, Guimin Lu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMechanicsCurrent densityElectric fieldTurbulenceMaterials scienceChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract The electric field, thermal field, and velocity field in a 60 kA diaphragmless lithium electrolyzer are studied by multiple physical field coupling simulations. The current density distribution, the balance of heat generation, heat dissipation, and the flow field distribution of the lithium electrolyzer are discussed. The velocity distribution is solved by the Euler–Euler two‐phase flow model, and the turbulent flow of electrolyte is solved by the k–ε method. The results show that in an electric‐heat field, there is a constant ratio of current loss, and the current density distribution is not uniform. The electrolyte temperature is evenly distributed, wherein thermal radiation plays a dominant role. In addition, it is found that the velocity distribution of the end‐side interpolar space and the inside interpolar space is different. The influence of electrode distance in the range of 30~60 mm on the flow field is investigated, and the dominance of the electrode distance effect and the eddy current effect is analyzed. The optimal range of electrode distance is 40~50 mm.

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 score0.068
Threshold uncertainty score0.835

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 integrity0.0000.002
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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