Research Experiences

During my undergraduate studies, I joined the Plasma and Fusion Research Group at UF, which became my first research experience and my first opportunity to tackle problems in fusion energy sciences. Closely advised by my mentor, Dr. Chris McDevitt, I learned about physics-constrained deep learning techniques and how to apply them to physics and engineering problems. My first project involved developing a physics-constrained deep learning surrogate model that rapidly predicts the plasma-capsule material mix within an inertial fusion setting. The surrogate model accurately learns the self-similar solution to the ion mass flux equation based on physical constraints only in a zero-data limit. Additionally, the model can potentially be extended to other problems involving self-similar variables and solutions. Through this experience, I have learned about various academic concepts outside of my nuclear engineering curriculum such as data science, computational physics, and high-performance computing. I am currently completing a technical write-up for this project, which will become my first authored journal publication.

During the summer of 2024, I completed a 10-week internship as an Undergraduate Research Aide within Argonne National Laboratory's Computational Science (CPS) Division. I was mentored by Dr. Paul Romano, the Project Lead of the OpenMC Monte Carlo Code and the Group Leader of the CPS Particle Transport Group. During my time at Argonne, my focus lied in exploring novel multiphysics coupling methods for the design and analysis of fusion energy machines. Neutrons created by fusion reactions deposit their energy throughout the machine's components; as such, the design process must account for accurate heat transfer mechanisms, neutron loads, and temperature-feedback effects to material properties (e.g., nuclear cross sections). I focused on learning about neutron transport theory, the OpenMC Monte Carlo Code, and multiphysics coupling environments such as preCICE. I also gained knowledge in new areas such as computational fluid dynamics, high-performance computing, and code compilation and installation (which, to this day, remains one of my most useful skills!). Through this research experience, I solidified my interest for nuclear modeling, fusion machine design, and a nuclear engineer's approach to contributing to fusion energy's commercialization. I aim to make fusion neutronics one of the main scopes of my graduate studies and hope to connect with more individuals and groups conducting research in this area.

During summer of 2023, I worked with General Atomics research scientists Casey Kong, Matthew Quinn, and Anthony Allen on optimizing the method currently in use for the material assessment of inertial fusion capsules used at the National Ignition Facility. The method, which leverages machine learning models and computer vision algorithms, is able to develop and characterize 3D X-ray tomographies of the capsules in an almost automated workflow. To optimize analysis time and computing and scanning resources, my task was investigating how scan parameters such as the scanning time and number of tomography projections affected the accuracy of the capsule's assessment. Through extensive experimental data collection and analysis, I was able to describe the relation between scan parameters and assessment quality and make a recommendation as to the optimal scanning time. Beyond my technical work, I was able to learn from experts through the General Atomics/DIII-D Science, Engineering, and Technology (SET) meetings, in which scientists and engineers at GA/DIII-D shared novel developments in fusion research and technology. I also met what I believe will be a great part of the core of future fusion scientists. This experience was part of the Science Undergraduate Laboratory Internships (SULI) program by the US Department of Energy.