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Record W4311001933 · doi:10.3389/fresc.2022.1016355

Active cooling of twisted coiled actuators via fabric air channels

2022· article· en· W4311001933 on OpenAlexafffund
Alex Lizotte, Ana Luisa Trejos

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsActuatorAir coolingActive coolingMaterials scienceMechanical engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Twisted coiled actuators (TCAs) are promising artificial muscles for wearable soft robotic devices due to their biomimetic properties, inherent compliance, and slim profile. These artificial muscles are created by super-coiling nylon thread and are thermally actuated. Unfortunately, their slow natural cooling rate limits their feasibility when used in wearable devices for upper limb rehabilitation. Thus, a novel cooling apparatus for TCAs was specifically designed for implementation in soft robotic devices. The cooling apparatus consists of a flexible fabric channel made from nylon pack cloth. The fabric channel is lightweight and could be sewn onto other garments for assembly into a soft robotic device. The TCA is placed in the channel, and a miniature air pump is used to blow air through it to enable active cooling. The impact of channel size on TCA performance was assessed by testing nine fabric channel sizes—combinations of three widths (6, 8, and 10 mm) and three heights (4, 6, and 8 mm). Overall, the performance of the TCA improved as the channel dimensions increased, with the combination of a 10 mm width and an 8 mm height resulting in the best balance between cooling time, heating time, and stroke. This channel was utilized in a follow-up experiment to determine the impact of the cooling apparatus on TCA performance. In comparison to passive cooling without a channel, the channel and miniature air pump reduced the TCA cooling time by 42% ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM1"><mml:mn>21.71</mml:mn><mml:mo>±</mml:mo><mml:mn>1.24</mml:mn></mml:math> s to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM2"><mml:mn>12.54</mml:mn><mml:mo>±</mml:mo><mml:mn>2.31</mml:mn></mml:math> s, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM3"><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.001</mml:mn></mml:math> ). Unfortunately, there was also a 9% increase in the heating time ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM4"><mml:mn>3.46</mml:mn><mml:mo>±</mml:mo><mml:mn>0.71</mml:mn></mml:math> s to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM5"><mml:mn>3.76</mml:mn><mml:mo>±</mml:mo><mml:mn>0.71</mml:mn></mml:math> s, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM6"><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.001</mml:mn></mml:math> ) and a 28% decrease in the stroke ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM7"><mml:mn>5.40</mml:mn><mml:mo>±</mml:mo><mml:mn>0.44</mml:mn></mml:math> mm to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM8"><mml:mn>3.89</mml:mn><mml:mo>±</mml:mo><mml:mn>0.77</mml:mn></mml:math> mm, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM9"><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.001</mml:mn></mml:math> ). This work demonstrates that fabric cooling channels are a viable option for cooling thermally actuated artificial muscles within a soft wearable device. Future work can continue to improve the channel design by experimenting with other configurations and 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 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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes2
Has abstractyes

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