Published: May 30, 2023 By

Department of Defense logo.Five University of Colorado Boulder aerospace PhD students have earned prestigious 2023 National Defense Science and Engineering Graduate (NDSEG) Fellowships.

Saikiran Chikine, Conor Rowan, Kian Shakerin, Michael Sola, and James Walker are each receiving the Department of Defense award, which provides three year fellowships to promising young scientists and engineers.

The program, established by Congress in 1989, awards fellowships to up to 500 people across the United States annually and is designed to promote education in science and engineering disciplines relevant to the Department of Defense.

Find out more about our honorees and their research below.

The 2023 NDSEG Honorees

Saikiran Chikine

Advisor: Natasha Bosanac
Lab: Bosanac Group

As cislunar space grows more crowded, spacecraft operating in the region face increased challenges from collision risks, achieving rendezvous goals, and debris hazards. Noncooperative pursuit and evasion describes scenarios where two spacecraft are chasing/evading and cannot communicate, which can encompass a wide array of other situations, including the ones mentioned above. Since neither spacecraft knows the capabilities of the other, and given the inherent uncertainty already present in spacecraft state knowledge and maneuver execution, these scenarios present a large degree of uncertainty. My work will address this pursuit and evasion problem in the presence of uncertainty by applying uncertainty-aware reinforcement learning. The goal of my research is to ultimately develop a new approach to autonomous, uncertainty-aware maneuver planning for cislunar spacecraft that will allow them to perform in a wide range of scenarios, from collision avoidance to actively evading a surveilling spacecraft.

Conor Rowan

Advisors: Alireza Doostan and Kurt Maute

Solid mechanics is the field of engineering focused on the analysis/design of structures and is a core discipline within the aerospace industry. Comprising a wide range of physical principles and mathematical techniques used to predict deformations and stresses under the action of mechanical, inertial, and thermal loading, solid mechanics has successfully solved an impressive array of problems. Yet, due to assumptions inherent in the theory, certain phenomena have remained difficult to model. The effect of small-scale spatial variations in the material properties of a solid can strongly influence its mechanical response, but can be difficult to incorporate into analysis. Similarly, precise and robust models for the initiation and propagation of cracks in structures continue to elude the solid mechanics community. These two ongoing problems–multiscale effects and damage/fracture modeling–represent gaps in our understanding of the mechanics of materials, and forces designers to incorporate overly-conservative safety factors into many structures. The focus of my research will be augmenting traditional solid mechanics with more contemporary techniques to combat multiscale and fracture problems. Machine learning models promise a flexible approach to learn patterns in data which are not easily explained from first principles, and once trained, can be used to expedite expensive computations. A number of different approaches to model fracture exist, but have not been used in data-driven or multiscale settings. I plan to synthesize the methods of computational solid mechanics, data-science, and fracture mechanics to further our understanding of these stubborn problems and streamline engineering structural design.

Kian Shakerin

Advisor: Jay McMahon
Lab: ORCCA (Orbital Research Cluster for Celestial Applications) Lab

The aim of my research is to advance the autonomous operational capabilities of spacecraft in dynamic environments. Modern missions often take place in dynamically complex regions of space and face large operational burdens. To overcome these issues and enable the next-generation of space missions, it is imperative for spacecraft to perform and maneuver with little to no operator input. I will explore the use of decision making and Machine Learning techniques such as POMDPs and Reinforcement Learning to develop an integrated high-level mission planning framework with guidance and control algorithms for adaptive maneuvering. The research will primarily focus on operations about small celestial bodies, such as asteroids and comets, because of their high scientific value. I also aim to explore the results of the work across a wider set of domains such as LEO and XGEO.

Michael Sola

Advisor: Marcus Holzinger
Lab: VADeR (Vision, Autonomy, and Decision Research) Lab

Space Domain Awareness (SDA) is a field in aerospace engineering that gathers “actionable knowledge required to predict, avoid, deter, operate through, recover from, and attribute cause to losing or degrading space capabilities and services." Current SDA systems were designed to work only within the confines of Earth's gravitational influence, i.e. LEO to GEO orbits. However, these systems have difficulty sensing beyond GEO. The growing interest in the area of influence around the moon, cislunar space, requires an expansion of SDA capabilities to this region. The following research will develop a solution to this gap by developing a self-sufficient sensor network concept for SDA between GEO and cislunar space. The solution will require novel methods for mission design, orbit determination, remote object detection, tracking, and characterization, and sensor tasking. The research will fuse these methods to develop a theoretical network of sensors placed in various cislunar orbital families that has the capability to autonomously maintain its own architecture while simultaneously searching for non-cooperative objects. This capability will ensure military and civilian leaders have adequate situational awareness and intelligence of activities in the cislunar region to make informed decisions on matters of national security.

James Walker

Advisor: Hanspeter Schaub
Lab: Autonomous Vehicle Systems (AVS) Lab

I am investigating charge control for spacecraft. While in orbit, spacecraft are exposed to the charged plasma of the space environment. As electrons and ions impact the spacecraft, they stick to the surface and cause the spacecraft to accumulate charge. Two charged spacecraft in close proximity to each other exert electrostatic forces and torques on one another. My research involves developing methods for controlling the electric potential on a target object in order to control these electrostatic forces and torques. If we are able to control the potential difference between two spacecraft, we can improve the safety and efficiency of proximity and rendezvous operations. Additionally, by maintaining a controlled electric potential between a servicing craft and a target object, we can use the electrostatic forces to contactlessly "pull" the target object, a method called the Electrostatic Tractor. This would specifically be useful for removal of large debris from Geostationary orbits.