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Record W3195686327 · doi:10.1177/1045389x211041168

Dynamic modeling for soft dielectric elastomer actuator considering different input frequencies and external loads

2021· article· en· W3195686327 on OpenAlex
Peng Huang, Jundong Wu, Yue Zhang, Pan Zhang, Yawu Wang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2021
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsConcordia University
FundersHigher Education Discipline Innovation ProjectNatural Science Foundation of Hubei ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Nonlinear systemHysteresisSuperposition principlePhenomenological modelCreepActuatorConstraint (computer-aided design)GeneralizationComputer scienceEngineeringMaterials scienceMathematicsMathematical analysisPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

A dynamic model for the soft dielectric elastomer actuator (SDEA) is developed in this paper to describe its intricately nonlinear behaviors considering different input frequencies and external loads. Firstly, the characteristics of the SDEA are observed by several groups of experiments. A phenomenological model is proposed to describe the asymmetric hysteresis behavior of the SDEA, which consists of a Prandtl-Ishlinskii model with one-side play operator and a dead-zone model with one-side dead-zone operator. Meanwhile, a mathematical model is built to depict the creep behavior of the SDEA. The dynamic model including a module and a linear system is proposed to further handle the rate-dependent and the stress-dependent hysteresis behaviors of the SDEA, in which the module is the superposition of the asymmetric hysteresis model and the creep model. To ensure that the inverse solution of the module is existing, as well as the linear system is controllable and observable, the constraint conditions of parameter values of the dynamic model are constructed. Next, the parameter identification is divided into two steps, and the differential evolution algorithm is employed in each step. Finally, the generalization of the proposed dynamics model is demonstrated by comparing the model output with the experimental data.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.683

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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