Time-Resolved Data-Driven Surrogates of Hall-effect Thrusters
The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic high-frequency oscillations of these thruster enables additional...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
12.08.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | The treatment of Hall-effect thrusters as nonlinear, dynamical systems has
emerged as a new perspective to understand and analyze data acquired from the
thrusters. The acquisition of high-speed data that can resolve the
characteristic high-frequency oscillations of these thruster enables additional
levels of classification in these thrusters. Notably, these signals may serve
as unique indicators for the full state of the system that can aid digital
representations of thrusters and predictions of thruster dynamics. In this
work, a Reservoir Computing framework is explored to build surrogate models
from experimental time-series measurements of a Hall-effect thruster. Such a
framework has shown immense promise for predicting the behavior of
low-dimensional yet chaotic dynamical systems. In particular, the surrogates
created by the Reservoir Computing framework are capable of both predicting the
observed behavior of the thruster and estimating the values of certain
measurements from others, known as inference. |
---|---|
DOI: | 10.48550/arxiv.2408.06499 |