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Record W2964856588 · doi:10.1109/icorr.2019.8779498

Design of an Active Cooling System for Thermally Activated Soft Actuators

2019· article· en· W2964856588 on OpenAlexaff
Brandon P.R. Edmonds, Ana Luisa Trejos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsActuatorWater coolingComputer scienceMaterials scienceMechanical engineeringControl engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A new type of actuator made from twisting a silver-plated nylon thread presents new possibilities for the way wearable mechatronic rehabilitation devices are designed. The twisted coiled actuator (TCA) has been previously shown to provide a power density up to 100 times that of biological muscles, while also encompassing biomimetic characteristics. However, since TCAs require heat to contract, the main drawbacks preventing this type of actuator are its inherent low efficiency and slow reaction times. To combat both of these issues, a simple tube enclosure was designed to provide active cooling using forced air. The two main parameters affecting the efficiency and bandwidth are the cooling air pressure and tube diameter. This study presents a two-way repeated measures test to compare these parameters on the cooling and heating rates of the TCA system, as well as the thermal capacitance with three pressure levels (10, 15, and 20 psi) and three tube diameters (4, 4.5, and 5 mm). The results show that an increase in pressure significantly improves the rate of cooling, while a decrease in tube diameter has negative effects on the efficiency and cooling rate of the system. The mean values of the cooling time $(\tau_{\text {cool}})$ were 2.972, 2.210, and 2.682 seconds for 4, 4.5, and 5 mm diameters, respectively. These results indicate that a decrease in diameter improves the cooling rate up to the point at which the walls of the tube become so close to the TCA strand, that they prevent rapid heat transfer while cooling.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.583
Threshold uncertainty score0.319

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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