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Record W4385239717 · doi:10.1139/cjp-2022-0339

Mathematical modelling of micropolar thermoelastic problem with nonlocal and hyperbolic two-temperature based on Moore–Gibson–Thompson heat equation

2023· article· en· W4385239717 on OpenAlexvenueno aff
Rajneesh Kumar, Sachin Kaushal, Arun Kochar

Bibliographic record

VenueCanadian Journal of Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicThermoelastic and Magnetoelastic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsThermoelastic dampingPhysicsLaplace transformIsotropyHeat equationMathematical analysisBoundary value problemClassical mechanicsFourier transformPartial differential equationThermal conductionDimensionless quantityMechanicsThermalThermodynamicsMathematics

Abstract

fetched live from OpenAlex

The aim of this article is to study a problem of thermomechanical deformation in a homogeneous, isotropic, micropolar thermoelastic half-space based on the Moore–Gibson–Thompson heat equation under the influence of nonlocal and hyperbolic two-temperature (HTT) parameters. The problem is formulated for the considered model by reducing the governing equations into 2D and then converting to dimensionless form. Laplace transform and Fourier transform techniques are employed to obtain the system of differential equations. In the transformed domain, the physical quantities like displacement components, stresses, thermodynamic temperature, and conductive temperature are calculated under the specific types of normal force and thermal source at the boundary surface. A numerical inversion technique is used to recuperate the equations in the physical domain to exhibit the influence of nonlocal and HTT in the form of graphs. Particular cases of interest are also discussed in the present problem. The present study finds applications in a wide range of problems in engineering and sciences, control theory, vibration mechanics, and continuum mechanics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.393
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.180
Teacher spread0.166 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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