On Scaling of Hall-Effect Thrusters Using Neural Nets

Plyashkov, Yegor V.; Shagayda, Andrey A.; Kravchenko, D. A.; Lovtsov, A. S.; Ratnikov, F.

doi:10.2514/1.b38592

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Let's compare predictions of semi empirical approach [7], approach in paper [1], and finally ours. Worth to mention that current approach is easiest to redesign from scratch.…”

Section: Results Discussionmentioning

confidence: 99%

“…Method in this paper and firstly used in Plyashkov, Shagayda, Kravchenko, Ratnikov, and Lovtsov [1] has advantages over SEM one in: ability to preidct performance more precisely on given domain, account for experimental data. I believe with more input data the ML method of deisgning thrusters would be more widely used.…”

Section: Discussionmentioning

confidence: 99%

“…Though there are a lot of ways these systems are designed in industry with great efficiencies, but in cost of multi-million research budgets and time. This problem might be solved using neural network design approach and few hardware iteration tweaks [1].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the neural network design approach has accuracy advantage only on domain of the dataset [1], this limitations is easily compensated by ability to create relations between multiple discharge and geometry parameters at once. Hence this novel approach and scaling relations together could be an ultimate endgame design tool for HET.…”

Section: Introductionmentioning

confidence: 99%

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Hall effect thruster design via deep neural network for additive manufacturing

Korolev¹

2023

Preprint

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Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computing power than design from scratch and way more flexible than usual scaling approach.

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“…Let's compare predictions of semi empirical approach [7], approach in paper [1], and finally ours. Worth to mention that current approach is easiest to redesign from scratch.…”

Section: Results Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hall effect thruster design via deep neural network for additive manufacturing

Korolev¹

2023

Preprint

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“…We found following recent research articles where researcher tried to implement ML and other artificial intelligence (AI)-based methodologies for the PPT or close cousin of the PPT systems like Hall effect thrusters (Plyashkov et al, 2022) data processing. However, there is a lot of scope to introduce a complete data processing architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Science and Machine Learning Processes for IoT-Based Pulsed Plasma Thruster Research

Hossain,

Sun,

Islam

2023

JDSIS

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Pulsed Plasma Thrusters (PPT) are very high specific impulse but low efficient electric propulsion engine which are being used as primary or secondary propulsion mechanism for spacecrafts (cube, micro, nano or pico satellites). PPTs are used in academic and/or industrial research from decades, but its experimental data collection methods are normally offline, analysis methods are conventional, and data interpretation lies in electromagnetic, physical, and chemical domain. It is inadequate to explain PPT generated data in few specific domains only and it may prone to inadequate explanations which can hide vast insights of data. Actually, PPT generated data are usually big data. It is essential to analyze and explain PPT data in data science and machine learning domain to get keen insights from the data. To this date no such complete solution exists for PPT either in industrial arena or academia to use all these three technologies. To meet the gap, we propose and implement an Internet of Things (IoT) based architecture for PPT experimental facility to collect, accumulate, and process PPT data intelligently. This architecture yields some machine learning (ML) models with prediction accuracy above 93%. This architecture is a complete IoT and big data based Artificial Intelligence (AI) implementation on PPT data through data science and ML processes. This architecture is capable to contribute in both academic as well as industrial research, development and deployment.

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Supervised machine learning-based Hall thruster scaling

Marianacci,

Mazouffre

2024

J Electr Propuls

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The scaling methodology described in this paper to find the geometry and working parameters of Hall Thrusters is based on algorithms of supervised Machine Learning. The approach considers the determination of the geometrical sizes, propellant mass flow rate and discharge voltage taking thrust and specific impulse as requirements. The magnetic field is also considered. The Gradient Boosting Regression is found as the most suitable algorithm for our purpose. Scaling relies on a specific database of 54 thrusters for the determination of all parameters. The database includes measurements carried out with xenon, krypton and argon as propellant. A unique analytical approach based on the GBR algorithm has been developed and validated to determine the suitable design for a Hall thruster according to space mission requirements.

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On Scaling of Hall-Effect Thrusters Using Neural Nets

Cited by 5 publications

References 13 publications

Hall effect thruster design via deep neural network for additive manufacturing

Hall effect thruster design via deep neural network for additive manufacturing

Data Science and Machine Learning Processes for IoT-Based Pulsed Plasma Thruster Research

Supervised machine learning-based Hall thruster scaling

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