Work Description

Title: Emitter Model Inference from Electrospray Array Thruster Tests Dataset Open Access Deposited

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Methodology
  • Analysis has 3 major components. 1. the model; 2. the data; and 3. the inference. To the first, we leverage a reduced-fidelity single emitter model for porous conical type electrospray emitters we have published previously: C. B. Whittaker and B. A. Jorns, Journal of Applied Physics 134, 083301 (2023). To the second, we leverage the experimental data we reported characterizing the performance of the MEAT-1.2 system at the 38th IEPC: C. B. Whittaker, B. A. Jorns, S. M. Arestie, and C. M. Marrese-Reading, in 38th International Electric Propulsion Conference (Electric Rocket Propulsion Society, 2024) p. 730. Finally, we specify a Bayesian parameter estimation over the parameters of the model given the observation of the data. We adopt a formulation wherein we treat the parameters of the model prediction probabilistically, such that our state of knowledge in the parameters (i.e., what values best fit the data) is described as a probability distribution. To do so, we fully parameterize the model and describe our prior state of knowledge in each parameter (e.g., what our input uncertainty in the propellant temperature is, what the charge to mass ratio of the ion beam is, and so on). To specify a prior over the emitter geometry, we make measurements of 143 of the emitters in the array and infer the corresponding distribution over emitter geometry (i.e., manufacturing tolerances) hierarchically to feed into our main inference over the model parameters. This framework describes the parameters of the models probabilistically (i.e., accounting for uncertainty). Once the inference is performed and the model thus trained, we leverage it to make performance predictions of the system. Inspecting these predictions allows us to interpret what is going on in the array at the level of individual emitters.
Description
  • The object of our study was to train a reduced-fidelity model for individual emitter behavior within a porous conical type electrospray array thruster on data taken over the entire array, which is the sum over all the emitters. By leveraging surface profilometry to measure the variance in geometry in the array, we then gain insight into the individual emitter dynamics. By rigorously predicating uncertainty in the predictions made by the model on uncertainty over its inputs, we can then understand the major sources of uncertainty in the system. The raw experimental data which inform this inference and prediction study were acquired in April of 2024 at the Jet Propulsion Laboratory's MicroPropulsion Laboratory, with special thanks to Colleen Marrese-Reading and Steven Arestie. These and other results are reported in a separate manuscript: C. B. Whittaker, B. A. Jorns, S. M. Arestie, and C. M. Marrese-Reading, in 38th International Electric Propulsion Conference (Electric Rocket Propulsion Society, 2024) p. 730. The thruster used in these experiments was fabricated at the University of Michigan in March of 2024. The analysis underlying this work was conducted from September of 2024 to January of 2025. This work was supported by a NASA Space Technology Graduate Research Opportunity (80NSSC21K1247). This research was also supported in part through computational resources and services provided by Advanced Research Computing, a division of Information and Technology Services at the University of Michigan, Ann Arbor. Finally, this work was performed in part at the University of Michigan Lurie Nanofabrication Facility.
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Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
ORSP grant number
  • AWD018979
Keyword
Related items in Deep Blue Documents
Resource type
Last modified
  • 05/28/2025
Published
  • 05/28/2025
Language
DOI
  • https://doi.org/10.7302/92gy-3n37
License
To Cite this Work:
Whittaker, C. B. (2025). Emitter Model Inference from Electrospray Array Thruster Tests Dataset [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/92gy-3n37

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Files (Count: 4; Size: 297 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
Data.zip 2025-02-24 2025-04-10 297 MB Open Access
README.txt 2025-02-24 2025-04-10 20.8 KB Open Access
Python-spec-file.txt 2025-02-24 2025-04-10 1.74 KB Open Access
Code.zip 2025-03-19 2025-04-10 102 KB Open Access

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