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Record W2901318228 · doi:10.25071/10315/35260

Heat Conduction In A Layered Structure With An Interface Crack Using The Dual Phase Lag Model

2018· article· en· W2901318228 on OpenAlexaff
Zengtao Chen, Keqiang Hu

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhase lagDual (grammatical number)Thermal conductionLagInterface (matter)Materials sciencePhase (matter)Composite materialComputer sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

In this paper, the transient heat conduction in a layered composite with an insulated interface crack parallel to the boundaries is investigated by using the dual phase lag (DPL) model.Fourier and Laplace transforms are applied and the mixed boundary value problem for the cracked structure under temperature impact is reduced to solving a singular integral equation.The temperature field in time domain is obtained and the intensity factor of temperature gradient is defined.Numerical studies show that overshoot phenomenon may occur due to the combined effect of the insulated crack and application of the DPL heat conduction model.The thermal conductivity and the phase lag parameters have strong influence on the dynamic intensity factor of temperature gradients.The results obtained by the dual phase lag model can be reduced to that by the hyperbolic model and that by the parabolic model.

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)0.001
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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