Search Constraints
Filtering by:
Keyword
Bayesian inference
Remove constraint Keyword: Bayesian inference
Discipline
Engineering
Remove constraint Discipline: Engineering
1 - 4 of 4
Number of results to display per page
View results as:
Search Results
-
- Creator:
- Whittaker, Collin B.
- Description:
- This study follows after work conducted first for my dissertation and is presently being prepared for journal submission. The goal of our analysis was to analyze a small design space for an electrospray array thruster---varying the geometry of its emitters, the size of its extractor apertures, and its operating voltage---to determine designs robust to uncertainty. That is, we use a model for array performance whose input parameters we treat as uncertain (stemming from approximations to higher-order physics, manufacturing tolerances in fabricating a thruster, and so on). Making these predictions as a function of design, then, we can identify configurations that are performant robust to this uncertainty (i.e., still meet required performance specifications with high confidence). The data which inform this trade study are taken pricipally from our pending manuscript "Emitter Model Inference from Electrospray Array Thruster Tests", and from my thesis, "Designing Porous Electrospray Array Thrusters Under Uncertainty" (linked to the dataset as published). The analysis was conducted in January and February 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.
- Keyword:
- Electrospray, Electric propulsion, Robust optimization, Bayesian inference, and Ionic liquid ion source
- Discipline:
- Engineering
-
- Creator:
- Whittaker, Collin B
- 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.
- Keyword:
- Electrospray, Electric propulsion, Ionic liquid ion source, Bayesian inference, and Profilometry
- Discipline:
- Engineering
-
- Creator:
- Dong, Jiayuan, Liao, Jiankan, Huan, Xun, and Cooper, Daniel R.
- Description:
- We apply expert elicitation to assign informative prior to material flow analysis and conduct Bayesian inference for parameter and data noise learning.
- Keyword:
- Bayesian inference, Bayes factor, data noise, prior elicitation and aggregation, and uncertainty quantification
- Citation to related publication:
- Dong, Jiayuan, Jiankan Liao, Xun Huan, and Daniel Cooper. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference." Journal of Industrial Ecology 27, no. 4 (2023): 1105-1122.
- Discipline:
- Engineering
-
- Creator:
- Liao, Jiankan, Deng, Sidi, Huan, Xun, and Cooper, Daniel R.
- Description:
- We apply Bayesian inference to reduce network structure uncertainty on material flow analysis (MFA) and demonstrate the methodology through a case study on U.S. steel flow. In addition, we derive an input/output-based analysis to conduct decision-making based on the uncertainty results from MFA
- Keyword:
- Bayesian inference, Network structure uncertainty, Bayesian model selection, and Input/output analysis
- Citation to related publication:
- Liao, Jiankan, Deng, Sidi, Xun Huan, and Daniel Cooper. "Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis." arXiv preprint arXiv:2501.05556 (2025).
- Discipline:
- Engineering