Published: July 29, 2022 By

NASA LogoFour students in the Ann and H.J. Smead Department of Aerospace Engineering Sciences are being recognized with 2022 NASA Space Technology Graduate Research Opportunities (NSTGRO) fellowships.

The annual program sponsors U.S. citizen and permanent resident graduate students who show significant potential to contribute to NASA’s goal of creating innovative new space technologies for our nation’s science, exploration and economic future.

Program recipients perform innovative space technology research at their respective campuses and at NASA Centers.

NSTGRO honorees receive research funding and are matched with a technically relevant NASA subject matter expert, who serves as a research collaborator.

The research collaborator functions as a conduit into the larger technical community corresponding to the student’s space technology research area.

Read below for more information about each honoree and their research.

2022 Honorees

Kaylee Champion

2nd Year PhD Student

Advisor: Hanspeter Schaub
Lab: Autonomous Vehicles Systems (AVS) lab

My research is on touchless potential sensing in the cislunar plasma environment. To sense the potential of a nearby target, a servicing spacecraft aims an electron beam or ultraviolet laser at the target, exciting secondary electrons, photoelectrons, and x-rays. These emissions are then measured and used to determine the potential of the target with respect to the potential of the servicer. This information can be used to account for electrostatic perturbations during close-proximity operations, minimize the risk of electrostatic discharge during rendezvous, and detumble or orbit space debris. Previous work on this technology has been conducted in the Geosynchronous region, but the cislunar plasma environment presents novel challenges, such as shorter electrostatic interactions and spacecraft wakes. I'm working to account for these challenges through the use of computational spacecraft-plasma interaction models, such as NASCAP-2k and SIMION, and physical experiments in the ECLIPS vacuum chamber.

Grant Kirchhoff

2nd Year PhD Student

Advisor: Jeffrey Thayer
Lab: Active Remote SENsing Lab (ARSENL).

Water vapor’s (WV) impact on driving the Earth’s near-surface processes is paramount. The scientific community has identified a need to acquire higher spatially and temporally resolved global thermodynamic profiles of the region in the lower atmosphere that has a strong influence on climate models and weather forecasts. To directly measure WV concentrations, the DIfferential Absorption Lidar (DIAL) technique is an advanced solution that generates high-resolution WV measurements. However, implementing a spaceborne WV DIAL instrument is challenging because of fundamental limitations inherent to single-photon avalanche diode detectors, which are currently used on several NASA lidar instruments. Nonlinearities - such as deadtime and afterpulsing - induce biases in retrievals that result in limited signal dynamic range and corrupted WV profile estimates. Motivated by the demand for improved WV measurements through technological advancement, this research seeks to investigate advanced photon-counting lidar hardware characterization and advanced signal processing techniques that can mitigate the effects of photon detector biases and thus enable high-accuracy WV measurements of the lower atmosphere from a DIAL satellite.

Robyn Natherson

2nd Year PhD Student

Advisor: Daniel Scheeres
Lab: Celestial and Spaceflight Mechanics Lab (CSML)

My research focuses on how to optimize spacecraft trajectories for robustness to missed thrust events. The extended thrust arcs required by low-thrust propulsion methods leave a large timeframe for anomalies to arise which prevent the spacecraft from following its nominal thrust profile. If a trajectory is not designed to account for such losses, missed thrust events may result in the mission being unable to achieve its objectives. My project will study the design of robust, optimal preflight trajectories which leverage statistical models for thruster failure. The goal is to develop techniques for robust initial trajectory design, as well as improve the process for responsive redesign of trajectories in the event of a failure.

Jens Rataczak

3rd Year PhD Student

Advisors: Iain Boyd and Jay McMahon
Labs: Nonequilibrium Gas and Plasma Dynamics Laboratory (NGPDL) and Orbital Research Cluster for Celestial Applications Laboratory (ORCCA)

My research aims to develop novel guidance algorithms using machine learning models trained by research-grade computational fluid dynamics (CFD) simulations to enable more robust delivery of high-mass, low-ballistic-coefficient space systems to the outer planets via aerocapture. Specifically, I will investigate vehicles with hypersonic inflatable aerodynamic decelerators (HIADs). Uncertainty in the planet's atmospheric model, the chemical kinetic models for that atmosphere, and the vehicle's state and model parameters will be considered. The neural net-based models will capture key aerodynamic and aerothermodynamic phenomena that occur during aerocapture. I plan to improve the models typically used in the state-of-the-art fully-numeric, predictor-corrector guidance scheme by replacing its iterative process with a neural-net-based model to incorporate high levels of uncertainty and reduce on-board computation time. The uncertainty in the CFD chemical kinetic models will be addressed, and potential improvements to the chemical kinetics will be investigated.