Conceptual Demonstration of Hydrogen Peroxide Based Electrochemical Propulsion with Rotating Gliding Arc
Bibliographic record
Abstract
The development of high-performance green monopropellant-based small thrusters is essential for successful multipurpose space missions. This study experimentally demonstrates a novel electrochemical propulsion system that leverages rotating gliding arc (RGA) plasma to enhance the performance of 88 wt% hydrogen peroxide. This is the first electrochemical propulsion system that utilizes catalytic decomposition gases of hydrogen peroxide directly as plasma-forming gases and an alternating current (AC)-based RGA. The system employs an AC power supply operating at a 20 kHz frequency, combined with a swirler and an anchoring component that ensures geometric stabilization of the arc column. This configuration enabled stable plasma discharge under high-temperature ([Formula: see text]) and high-pressure ([Formula: see text]) conditions. The propulsion performance of the plasma reactor was significantly improved by rapidly heating the discharge gases, which were then accelerated to supersonic speeds through a De-Laval nozzle. In the electrochemical operation mode, this process led to consistent enhancements in both chamber pressure and characteristic velocity, achieving approximately 1.4- and 2-fold increases, respectively, compared to the chemical mode (without plasma discharge). The novel propulsion concept can provide a universally applicable solution capable of enhancing the performance of chemical propulsion systems with plasma discharge.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".