Scaling Approach for Sub-Kilowatt Hall-Effect Thrusters

Lee, Eun Kwang; Kim, Youn-Ho; Lee, Hodong; Kim, Holak; Doh, Guentae; Lee, Dong‐Ho; Choe, Wonho

doi:10.2514/1.b37424

Cited by 19 publications

(6 citation statements)

References 29 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%

“…For working with unscaled data PINN's were introduced, they are using equations 2-7 to generate 𝐶 𝑥 coefficients. Yes, it was said earlier that this method lacks ability to generate better performing HETs, but as we have generated larger dataset on same domain as Lee et al [7] it is important to control that our dataset is still the same quality as original. Using above mentioned PINN's it was possible to fit coefficients and they showed only slight divergence in values of few % which is acceptable.…”

Section: B Physics Informed Neural Networkmentioning

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: B Physics Informed Neural Networkmentioning

confidence: 99%

Hall effect thruster design via deep neural network for additive manufacturing

Korolev¹

2023

Preprint

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“…This is experimentally proven to be efficient estimate since SEM predictions of thrust are always higher than real performance. Lee et al [7] Initial condition Value…”

Section: G Use Of Pretrained Model In Additive Manufacturing Of Hall ...mentioning

confidence: 99%

Hall effect thruster design via deep neural network for additive manufacturing

Korolev¹

2023

Preprint

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“…Most models for HET scaling [6][7][8][9][10][11][12][13][14][15][16][17][18][19] use various theoretical or semi-empirical approaches. In this work, regular machine learning models were built with the information in the database.…”

Section: Approach To Scalingmentioning

confidence: 99%

“…Simulating this physics with the accuracy necessary for engineering applications requires a non-stationary three-dimensional fully kinetic numerical model, which would consume enormous computational resources [5]. Therefore, when designing a new HET, it is common to use a scaling approach, i.e., to predict the output parameters of a new HET based on the experimentally obtained output parameters of some prototype thruster [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

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confidence: 99%

On Scaling of Hall-Effect Thrusters Using Neural Nets

Plyashkov

Shagayda

Kravchenko

et al. 2022

Journal of Propulsion and Power

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Hall-effect thrusters (HETs) are widely used for modern near-earth spacecraft propulsion and are vital for future deep-space missions. Methods of modeling HETs are developing rapidly. However, such methods are not yet precise enough and cannot reliably predict the parameters of a newly designed thruster, mostly due to the enormous computational cost of a HET plasma simulation. Another approach is to use scaling techniques based on available experimental

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Scaling Approach for Sub-Kilowatt Hall-Effect Thrusters

Cited by 19 publications

References 29 publications

Hall effect thruster design via deep neural network for additive manufacturing

Hall effect thruster design via deep neural network for additive manufacturing

Hall effect thruster design via deep neural network for additive manufacturing

On Scaling of Hall-Effect Thrusters Using Neural Nets

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