Active cooling of twisted coiled actuators via fabric air channels
Bibliographic record
Abstract
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% ( 21.71±1.24 s to 12.54±2.31 s, p<0.001 ). Unfortunately, there was also a 9% increase in the heating time ( 3.46±0.71 s to 3.76±0.71 s, p<0.001 ) and a 28% decrease in the stroke ( 5.40±0.44 mm to 3.89±0.77 mm, p<0.001 ). 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".