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Record W7131146757 · doi:10.1115/imece2025-166299

Study of Composite Materials for Cooling Systems With Variable Heat Generation

2025· article· W7131146757 on OpenAlexaff
Gerardo Carbajal, Ethan Trulson, Emily Geiger

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsNickel Institute
Fundersnot available
KeywordsThermocoupleComposite numberHeat generationAluminiumThermalPhase-change materialElectricity generationPorosityThermal energy storageDissipation

Abstract

fetched live from OpenAlex

Abstract In the present study, a composite material comprising aluminum foam with porosity levels of 10, 20, and 40 pores per inch (PPI) and eicosane (phase change material) is investigated under the effect of different heat inputs. The heating system consisted of a cylindrical 12V, 10W electrical heater placed in the middle of the sample. The sample is a rectangular aluminum foam block impregnated with eicosane, a new composite material with distinct thermal properties. Applying a power supply, we varied the power range from 4 to 8 W. A type T thermocouple was placed on one side of the composite material to record temperature variations over time. The test measured the effects of different heat inputs applied to the composite material, which was subjected to a forced convection air cooling system. The power was supplied to the composite material under two circumstances: the thermocouple reached the eicosane’s melting temperature, the eicosane fully melted, or there was no significant change in temperature over time. After the electric power was turned off, the solidification time was recorded, and then the sample was heated again, generating a cycle that was repeated two more consecutive times. The experimental results showed that the composite material can maintain a temperature below 39°C, making it a promising candidate for passive cooling of lithium-ion battery systems or low heat capacity dissipation systems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.058
GPT teacher head0.313
Teacher spread0.255 · 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.

Study designBench or experimental
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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