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Record W4415898163 · doi:10.1016/j.csite.2025.107355

Thermoelastic fracture analysis of porous metal foams under cold shock using the dual-phase-lag model

2025· article· en· W4415898163 on OpenAlexaff
Wenzhi Yang, Xueyang Zhang, Zhijun Liu, Chuanbin Yu, Zengtao Chen

Bibliographic record

VenueCase Studies in Thermal Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicThermoelastic and Magnetoelastic Phenomena
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Gansu ProvinceNational Natural Science Foundation of ChinaLanzhou University
KeywordsThermoelastic dampingThermal shockLaplace transformPorosityThermal conductionThermalPorous mediumParametric statisticsCopper

Abstract

fetched live from OpenAlex

Some experimental evidence has proved that the Fourier heat conduction theory cannot precisely describe the transient thermal transport in porous media. This article employs the more generalized non-Fourier dual-phase-lag theory to investigate the thermoelastic and fracture responses of a porous copper foam plate containing an edge crack when the material is quenched by a cold thermal shock. The Laplace transform methodology and the numerical inversion scheme are adopted to deal with the equation’s time dependency. Parametric investigations are performed to reveal the impacts of the non-Fourier thermal lags, copper foam’s relative density, and the crack length, which would be beneficial to the thorough understanding and the development of the porous metal foams in thermal environments.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.279
Teacher spread0.256 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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