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Record W4406983467 · doi:10.11159/jffhmt.2025.003

Modeling Heat Transfer in Cylindrical Batteries: Spiral-Based Thermal Conductivity Tensor

2025· article· en· W4406983467 on OpenAlexvenueno aff
Jiří Hvožďa, Jan Boháček, Alexander Vakhrushev, Ebrahim Karimi‐Sibaki

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
Fundersnot available
KeywordsThermal conductivityHeat transferMaterials scienceSpiral (railway)MechanicsTensor (intrinsic definition)ThermalThermodynamicsComposite materialPhysicsMechanical engineeringGeometryMathematicsEngineering

Abstract

fetched live from OpenAlex

This study investigates the importance of considering the well-known spiral structure of cylindrical batteries in numerical models of heat transfer. Such models typically simplify the internal geometry by a concentric layout of electrodes and separators, resulting in an effective orthotropic thermal conductivity with radial, tangential, and axial components defined in a cylindrical coordinate system. However, the actual spiral structure suggests radius-dependent thermal conductivity. In this study, several thermal simulations were performed, comparing thermal fields obtained with the commonly used cylindrical orthotropy and a more realistic spiral structure. The results show that the spiral structure has a negligible effect on the overall temperature distribution for configurations with dense spirals and higher radial thermal conductivity (2 W·m1·K1). However, for lower radial thermal conductivity (0.2 W·m1·K1), considerable errors were observed even for dense spirals. These findings emphasize the need for studies to justify simplifications made in the thermal conductivity tensor.

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 score0.389
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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
Published2025
Admission routes1
Has abstractyes

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