On the Potential of Hydrogen-Powered Hydraulic Pumps for Soft Robotics
Bibliographic record
Abstract
To perform untethered operations, soft robots require mesoscale power units (10-1000 W) with high energy densities. In this perspective, air-breathing combustion offers an interesting alternative to battery-powered systems, provided sufficient overall energy conversion efficiency can be reached. Implementing efficient air-breathing combustion in mesoscale soft robots is notoriously difficult, however, as it requires optimization of very small combustion actuators and simultaneous minimization of fluidic (e.g., hydraulic) losses, which are both inversely impacted by actuations speeds. To overcome such challenges, this article proposes and evaluates the potential of hydrogen-powered, hydraulic free-piston pump architecture. Experimental data, taken from two combustion-driven prototypes, reveal (1) the fundamental role of using hydrogen as the source of fuel to reduce heat losses, (2) the significant impact of compression ratio, equivalence ratio, and surface-to-volume ratio on energy conversion efficiency, and (3) the importance of load matching between combustion and fluidic transmission. In this work, a small-bore combustion actuator demonstrated a 20% efficiency and a net mean output power of 26 W, while a big-bore combustion actuator reached a substantially higher efficiency of 35% and a net mean output power of 197 W. Using the small-bore combustion actuator, the hydrogen-powered, hydraulic free-piston pump provided a 4.6% overall efficiency for a 2.34 W net mean output power, thus underlying the potential of the approach for mesoscale soft robotic applications.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".