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Record W1977026660 · doi:10.1117/12.848726

Thermodynamic model using experimental loss factors for dielectric elastomer actuator design

2010· article· en· W1977026660 on OpenAlexafffund
Jean-Philippe Bigue, Patrick Chouinard, Marc Denninger, Sylvain Proulx, Jean‐Sébastien Plante

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversité de Sherbrooke
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsActuatorMaterials scienceCapacitanceVoltageDielectricElastomerDielectric elastomersBrakeMechanical engineeringMechanical energyAutomotive engineeringOptoelectronicsComposite materialEngineeringElectrical engineeringElectrodePower (physics)Physics

Abstract

fetched live from OpenAlex

Dielectric Elastomer Actuators (DEAs) are a promising actuation technology for mobile robotics due to their high forceto- weight ratio, their potential for high efficiencies, and their low cost. The preliminary design of such actuators requires a quick and precise assessment of actuator energy conversion performance. To do so, this paper proposes a simple thermodynamic model using experimentally acquired loss factors that predict actuator mechanical work, energy consumption, and efficiency when operating under constant voltage and constant charge modes. Mechanical and electrical loss factors for both VHB 4905 (acrylic) and Nusil's CF19-2186 (silicone) are obtained by mapping the performances of cone-shaped DEAs over a broad range of actuator speeds, capacitance ratios, and applied voltages. Extensive experimental results reveal the main performance trends to follow for preliminary actuator design, which are explained by the proposed model. For the tested conditions, the maximum experimental brake efficiencies are ~35% and ~25% for VHB and CF19-2186 respectively.

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 categoriesMeta-epidemiology (narrow)
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 score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.230
Teacher spread0.216 · 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.

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

Citations3
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicDielectric materials and actuatorsFrench-language works237,207