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Record W2911800446 · doi:10.1039/c9sm00160c

Shape-programming of hyperelastic plates through differential growth: an analytical approach

2019· article· en· W2911800446 on OpenAlexaff
Jiong Wang, Qiongyu Wang, Hui–Hui Dai, Ping Du, Danxian Chen

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsToronto Metropolitan University
FundersSouth China University of TechnologyResearch Grants Council, University Grants CommitteeNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsHyperelastic materialDifferential (mechanical device)Materials scienceComputer scienceMathematicsPhysicsStructural engineeringEngineeringThermodynamicsFinite element method

Abstract

fetched live from OpenAlex

In this work, we study the plane-strain deformations of hyperelastic plates induced by differential growth, aiming to derive some analytical formulas for 2D shape-programming of hyperelastic plates. First, we present a plate equation system with the growth functions incorporated, which is derived from the 3D governing system through a series expansion and truncation approach. By proposing a novel analytical method, the plate equation system is solved explicitly. The obtained solutions can reveal the dependence of the current configurations of the hyperelastic plates on the differential growth fields. By solving an inverse problem, some analytical formulas are obtained, which can be used to identify the growth functions for generating arbitrary 2D geometrical shapes of the hyperelastic plates. To demonstrate the efficiency of these formulas, some representative examples are studied, which show good consistency with the numerical simulations. The obtained analytical formulas have wide potential applications in the design of intelligent soft devices.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.909

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)0.0010.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 designBench or experimental
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

Citations19
Published2019
Admission routes1
Has abstractyes

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