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Record W2158290215 · doi:10.1177/1099636208098147

Micromechanical Thermoelastic Model for Sandwich Composite Shells made of Generally Orthotropic Materials

2009· article· en· W2158290215 on OpenAlexaff
Gobinda C. Saha, Alexander L. Kalamkarov

Bibliographic record

VenueJournal of Sandwich Structures & Materials · 2009
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsDalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsThermoelastic dampingOrthotropic materialHomogenization (climate)Asymptotic homogenizationMaterials scienceComposite numberShell (structure)Composite materialThermal expansionSandwich-structured compositeThermalStructural engineeringFinite element methodPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Analytical solutions based on the two-scale asymptotic homogenization method for thermoelastic problem pertinent to general thin composite shells are obtained. The model allows the determination of both local fields and effective elastic and thermal expansion coefficients of composite sandwich shells made of generally orthotropic materials. Orthotropy in the material characteristics leads to a significantly complex set of thermoelastic local problems and is considered for the first time in the present article. First, a 3D-to-2D general asymptotic homogenization composite shell model based on a set of four unit-cell problems is derived. Secondly, the expansion of the model is continued to the further derivation of formulae for the forces, moments, displacements, strains, stresses, and effective thermoelastic coefficients that are representatives of the sandwich shell. Finally, the theory is illustrated by examples pertaining to thin composite sandwich shells with hexagonal honeycomb, hexagonal-triangular, and star-hexagonal cellular cores of orthotropic materials.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
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 science0.0010.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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
GenreMethods

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
Published2009
Admission routes1
Has abstractyes

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