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Record W4408646148 · doi:10.5539/mer.v13n1p1

Disruptive ROVA Electropneumatic Valve Technology for Satellite-Delivering Rocket Applications

2025· article· en· W4408646148 on OpenAlexvenueno aff
Luis Teia

Bibliographic record

VenueMechanical Engineering Research · 2025
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsRocket (weapon)SatelliteAerospace engineeringEngineeringAeronauticsMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

This article presents a novel design for a Rotational Valve (ROVA) electro-pneumatic (pilot+main) valve combo developed for our rocket RFA ONE, where rotation governs the internal piston movement (in commercial valves, translation is the traditional movement). The need for such a new design comes from two facts: (1) commercial valve design operational pressure at maximum 10bar limits some rocket applications, where higher pressures are used to drive larger purelypneumatic both actuators and valves; (2) dents and scratches cause pistons to get stuck, leading to maintenance downtime and costs. It is the thesis of this study that a valve design employing rotation may alleviate this jamming problem. The 10bar limit problem (emerging from a seal being lift-off due to excessive pneumatic pressure) is resolved by making that seal insensitive to driving pressure. The ROVA valve combo internal operation is explained in detail, being visualized using Computer-Aided Design (CAD) and free to download at an open-source repository. Physical calculations show that the pressurization of internal paths plus the piston’s rotation is achieved with times comparable to commercial valves. This new design pushes the envelop on the electro-pneumatic valve capability to handle larger piloting pressures (that commercial options), and potentially extends valve life by using rotation as a preferred means of operation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.345
Teacher spread0.318 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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