Three students in the Ann and H.J. Smead Department of Aerospace Engineering Sciences are being recognized with 2023 NASA Space Technology Graduate Research Opportunities (NSTGRO) fellowships.
The annual program sponsors 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 space technology research at their university 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.
The 2023 Honorees
Grace Calkins
1st Year PhD Student
Advisor: Jay McMahon
Lab: Orbital Research Cluster for Celestial Applications (ORCCA) Laboratory
Calkins' research focuses on Linear Covariance Analysis (LCA), a computationally efficient method for uncertainty quantification in nonlinear trajectories. LCA provides direct statistical information about uncertainty and is significantly faster than the current method, Monte Carlo Analysis (MCA). However, LCA has limitations, including its applicability to only sufficiently linear systems and Gaussian noise modeling for a single reference trajectory. The study aims to expand LCA's capabilities for intelligent autonomous systems, guidance algorithms, and onboard navigation. To achieve this, the research addresses the limitations by integrating automatic trajectory branching, developing new uncertainty representation methods using Gaussian mixture models (GMMs), applying Unscented Kalman filter theory for autonomous branch point prediction, and leveraging the Koopman operator and state transition tensors to model nonlinear systems as high-dimensional linear systems. These tasks will be conducted in parallel while also enhancing LCA's computational efficiency using autodifferentiation and computer algebra. The ultimate goal is to enable LCA to handle complex nonlinear multi-branched trajectories, making it applicable to various NASA missions, such as docking operations, lunar landings, and planetary aircraft missions.
J. Flores Govea
2nd Year PhD Student
Advisor: Hisham Ali
Lab: Magnetoaerodynamics and Aerospace Plasmas Laboratory
My research aims to study the performance of a magnetohydrodynamic (MHD) control mechanism for enhancing planetary entry systems, specifically flight control and thermal protection systems. Unlike previous studies, I aim to utilize an electromagnetic device to induce the magnetic field which will allow for the active control of the MHD-induced capabilities. The experimental aspect of this research will be performed in the new state-of-the-art inductively coupled plasma (ICP) wind tunnel facility within the Magnetoaerodynamics and Aerospace Plasmas Laboratory (CU-MAPLAB). This facility has the unique capability to simulate a high-altitude planetary entry environment where MHD forces are expected to be the most beneficial. I plan to simulate various planetary atmospheres where a MHD control mechanism has been identified as an enabling and enhancing technology, such as the Ice-Giants. The experimental results will be implemented into system performance and trajectory models to evaluate the impact of a MHD device on system and mission design. Specifically, I will investigate the impact of a MHD device on aerocapture missions. The goal of the research is to increase the technology readiness level (TRL) of a MHD control mechanism for an eventual flight demonstration.
Mark Stephenson
1st Year PhD Student
Advisor: Hanspeter Schaub
Lab: Autonomous Vehicle Systems (AVS) Laboratory
Mark's research studies decentralized satellite autonomy for search-and-image tasking in satellite constellations. Advances in spacecraft technology have enabled larger observational satellite constellations, allowing for more complex planetary and climate science missions. While traditional methods for constellation operations have limitations with respect to responsiveness and scalability, Mark uses reinforcement learning to teach satellites to autonomously and independently collaborate, improving the science output of constellations for tasks ranging from environmental monitoring on Earth to deep-space planetary science.