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Record W2780353453 · doi:10.1139/cjp-2017-0167

A three-dimensional generalized shock plate problem with four thermoviscoelastic relaxations

2017· article· en· W2780353453 on OpenAlexvenueno aff
Ashraf M. Zenkour, Ahmed E. Abouelregal

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicThermoelastic and Magnetoelastic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsThermoelastic dampingRelaxation (psychology)Shock (circulatory)Thermal shockViscosityThermalMathematical analysisViscoelasticityMechanicsClassical mechanicsThermodynamicsMathematics

Abstract

fetched live from OpenAlex

The three-dimensional generalized thermoviscoelastic shock plate problem is presented. Four thermoviscoelastic relaxations are used during this study, three of them are due to the generalized thermoelasticity models and the fourth is due to the viscosity parameter. The plate is thermally isolated at its bottom surface while its upper one is under a thermal shock. The transient thermal shock plate problem is presented according to a unified theory of generalized thermoelasticity. The classical coupled thermoelasticity model is used and two of its generalizations, namely Green–Lindsay and Lord–Shulman models, are also used. Normal mode analysis is adopted to get an analytical general solution of the present plate problem. The distributions of all variables are investigated along the plate directions. Different thermoelasticity theories are compared to present suitable conclusions. Some special findings are pointed out to show the effects of viscosity and other thermal relaxation parameters on thermoelastic interactions. Numerical results are plotted and tabulated to serve as benchmark results for future comparisons.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.191
Teacher spread0.176 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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