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Record W2750185263 · doi:10.1139/cjp-2017-0369

Sandwich structure panel subjected to thermal loading using fractional order equation of motion and moving heat source

2017· article· en· W2750185263 on OpenAlexvenueno aff
E. Bassiouny, Hamdy M. Youssef

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicThermoelastic and Magnetoelastic Phenomena
Canadian institutionsnot available
FundersDeanship of Scientific Research, Prince Sattam bin Abdulaziz UniversityPrince Sattam bin Abdulaziz University
KeywordsLaplace transformPhysicsThermoelastic dampingDiscontinuity (linguistics)Mathematical analysisInverse Laplace transformMechanicsHeat equationFourier transformThermal shockWork (physics)Equations of motionOrder theoryInverseThermalClassical mechanicsThermodynamicsGeometryMathematics

Abstract

fetched live from OpenAlex

The present work studies the thermoelastic behaviour of a model for a layered thin plate called sandwich structure subjected to a thermal shock wave in light of the generalized thermoelasticity theory using fractional order equation of motion in the presence of a moving heat source. The governing equations are solved using Laplace transform. To obtain the different inverse field functions numerically, we used a complex inversion formula of Laplace transform based on Fourier expansion. The effect of different parameters; namely, the speed, the strength of the heat source, fractional order and time on the thermodynamical temperature, stress, and strain distribution, are discussed and presented graphically. Comparison with previous work in the context of the theory of generalized thermoelasticity shows that the present model is more reliable than the previous. The present model removes the points of discontinuity present in the stress and temperature distributions in the previous model. In the present model we found that the middle layer was affected slightly by some of these parameters. The other new results are discussed.

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 score0.256
Threshold uncertainty score0.364

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 head0.021
GPT teacher head0.208
Teacher spread0.187 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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