Numerical suite for cathodeless plasma thrusters

Magarotto, Mirko; Fede, Simone Di; Souhair, Nabil; Andrews, Shaun; Ponti, Fabrizio De

doi:10.1016/j.actaastro.2022.05.018

Cited by 14 publications

(24 citation statements)

References 58 publications

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“…g exit (13) where M det is the magnetic Mach number at the detachment location, [14] T g is assumed to be the room temperature. S e exit and S g exit equal to the cross section of the source.…”

Section: Validation Of the Data Generation Modelmentioning

confidence: 99%

“…[ 3,28 ] Ways to deal with

{\Gamma}_{\mathrm{wall}}^{\mathrm{e}}

{\Gamma}_{\mathrm{e}\mathrm{xit}}^{\mathrm{e}}

{R}_{\mathrm{wall}}^{\mathrm{g}}

, and

{R}_{\mathrm{exit}}^{\mathrm{g}}

are in accordance with ref. [14]. m 0 is the gas feed rate, m g is the particle mass of neutral gas.…”

Section: Data Generation Model and The Distribution Property Of The R...mentioning

confidence: 99%

“…To perform the task through numerical methods, Jiménez et al established a two‐dimensional (2D), frequency‐domain, electromagnetic model to study the propagation and absorption of waves; [ 11 ] Zhou et al developed a hybrid model, in which a 2D axisymmetric fluid model for electrons and a particle‐in‐cell (PIC) model for heavy species are built; [ 12 ] Magarotto et al built a three‐dimensional (3D) advanced fluid drift diffusion plasma solver numerical tool to study the equilibrium conditions of helicon plasma sources; [ 13 ] In another work, Magarotto et al proposed a numerical suite to calculate properties of the inductive plasma thruster, in which a global model and a fluid model are developed. [ 14 ] These works show that, to build a practical model analysing the discharge process, multi‐dimensional fluid, PIC or hybrid method and appropriate assumptions are needed. Therefore, it is attractive to develop a fast and reliable way to predict the energy deposition.…”

Section: Introductionmentioning

confidence: 99%

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Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Cheng

Xia

Xiong

2022

Contributions to Plasma Physics

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To find a fast and reliable way predicting the energy deposition of helicon plasmas, this work focuses on machine learning algorithms. Data generation model and the distribution property of the source data are studied, then the classical algorithms and deep neural network (DNN) are built, and these algorithms are studied to test the performance on the energy deposition datasets. Both decision tree classifier (DTC) and support vector machine (SVM) find the electron temperature is the noise feature, and when it is included in the feature vector, the performance will degrade. Therefore feature selection needs be done to obtain high accuracy. For DNN, by directly changing the numbers of the hidden layers and units, test accuracy exceeds 0.95 when hidden layer is greater than three. The feature selection is automatically performed and the learning process is simpler in DNN. When the splitting ratio is varied, the generalization performance of DTC and SVM fluctuates, whereas DNN exhibits no evident change. Compared with the classical algorithms, DNN shows better stability when the source data are changed. The calculations suggest machine learning technique is a promising choice to predict the energy deposition of helicon plasmas.

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Section: Validation Of the Data Generation Modelmentioning

confidence: 99%

“…[ 3,28 ] Ways to deal with

{\Gamma}_{\mathrm{wall}}^{\mathrm{e}}

{\Gamma}_{\mathrm{e}\mathrm{xit}}^{\mathrm{e}}

{R}_{\mathrm{wall}}^{\mathrm{g}}

, and

{R}_{\mathrm{exit}}^{\mathrm{g}}

are in accordance with ref. [14]. m 0 is the gas feed rate, m g is the particle mass of neutral gas.…”

Section: Data Generation Model and The Distribution Property Of The R...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study of the energy deposition of helicon plasmas driven by machine learning algorithms

Cheng

Xia

Xiong

2022

Contributions to Plasma Physics

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“…First, the Drift Diffusion approximation has been considered for the heavy species without solving the momentum equation. The Drift Diffusion flux reads as in Equation (15):…”

Section: Numerical Strategiesmentioning

confidence: 99%

“…[7] This method is the less demanding in terms of computational resources and thus is widely used. [13] Recently, a promising numerical tool was developed at the University of Padova and the University of Bologna, namely 3D-VIRTUS [14,15] for the simulation of the plasma dynamics in an HPT. This tool consists of two modules coupled together: the first solves the Electro Magnetic (EM) wave propagation and thus the power coupled into the plasma by the antenna; the second is a fluid solver, based on the finite volume library OpenFoam, that handles the plasma dynamics.…”

Section: Introductionmentioning

confidence: 99%

Different fluid strategies for the simulation of a Helicon Plasma Thruster

Souhair

Ponti

Magarotto

et al. 2022

Contributions to Plasma Physics

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Different fluid numerical strategies have been considered for the analysis of the plasma transport in a Helicon Plasma Thruster, namely the Drift Diffusion solution, the explicitly segregated solution, and the adoption of the Simple algorithm [Patankar & Spalding] for the coupled solution of the continuity and momentum balances. These strategies have been compared in terms of plasma profiles such as electron density and temperature, and their computational efficiency has been assessed. Finally, a sensitivity analysis over the neutrals' pressure has been performed in order to assess the validity of each proposed strategy for different collisional regimes.

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