Data · dataset · 2021
Thruster Particle-in-Cell Simulation
Listed in Teesside University Research Data Repository
Python3 particle-in-cell simulation of thruster model in paper titled "Analysis and Particle-in-Cell Simulation of Gridded ICRH Plasma Thruster"
Description
Abstract
Large-payload deep space missions are impractical with current rocket propulsion technologies in use. Chemical thrusters yield a high thrust but low efficiency while ion thrusters are efficient but provide too little thrust for large satellites and manned spacecraft. Plasma propulsion is a viable alternative with a higher thrust than electric ion thrusters and specific impulse far exceeding those of chemical rocket engines.
Read the rest (2 more)
In this paper, a hybrid thruster is explored which affords the high mass flow rate of plasma thrusters while maximizing the specific impulse. The two primary processes of this system are the ion cyclotron resonance heating of plasma and subsequent electrostatic acceleration of ions with gridded electrodes. Through a particle-in-cell simulation of these two components, the exhaust velocities of Xenon, Argon, and Helium are compared.
It has been found that while the combination of systems results in a far greater exhaust velocity, the acceleration is largely from the gridded electrodes, and thus Xenon is the most suitable propellant with a specific impulse upward of 4200 s. Advancements in nuclear fusion and fission technologies will facilitate the deployment of high-power plasma thrusters that will enable spacecraft to travel farther and faster in the solar system.
Links
Where it is published
- DOI doi.org/10.17632/2r88w25743.3 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Physics
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/2r88w25743.3 | 14 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:physics | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |