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Record W4406661005 · doi:10.1016/j.csite.2025.105806

Nonlocal fracture analysis of fiber reinforced composites under heat flux loading

2025· article· en· W4406661005 on OpenAlexaff
Ke Cao, Ruchao Gao, Wenzhi Yang, Zhijun Liu, Zengtao Chen

Bibliographic record

VenueCase Studies in Thermal Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesNatural Science Foundation of Gansu ProvinceNational Natural Science Foundation of ChinaLanzhou University
KeywordsMaterials scienceComposite materialHeat fluxFracture (geology)Fiber-reinforced compositeFiberFlux (metallurgy)MechanicsHeat transferPhysics

Abstract

fetched live from OpenAlex

This work aims to explore the fracture characteristics of fiber reinforced composites under heat flux loadings in the framework of Eringen's nonlocal elasticity. Epoxy-based composites reinforced by T300 graphite fiber , AS graphite fiber and S-Glass fiber are examined to study the temperature and nonlocal thermal stress conditions around the crack tips. Both the horizontal and vertical fibers are considered to make the comparisons. By means of the Fourier transform method , the thermal and elastic problems are converted to the singular integral equations and dual integral equations, respectively. After evaluating the integral equations numerically, the temperature and nonlocal stresses around the crack tips are illustrated graphically. The effects of the fiber volume fractions , fiber orientations , and the nonlocal characteristic lengths are investigated in detail. The application of nonlocal theory is proved to be capable of taking the composite's size effect into account as well as removing the singular stress field near the crack tips, which contributes to the development of the fibrous composite's application in various engineering industries.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.015
GPT teacher head0.297
Teacher spread0.281 · 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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