Matthew Penza ’19, Computer Science
Certificate(s): Statistics and Machine Learning
I interned at the Princeton Plasma Physics Laboratory (PPPL) on the Small, Clean Fusion Reactors Project. My role was twofold. First, I implemented a time-series model of the nonadiabaticity (gaining or losing heat over time) of the magnetic strength of muon particles (μ) inside magnetic mirror machines. Second, I analyzed the characteristics of muon particles in diverging magnetic fields as a chaotic dynamical system that is highly sensitive to starting conditions. These are important properties to understand to further the development of the Princeton Field-Reversed Configuration (PFRC) fusion reactor, a promising future source of clean energy. FRCs are unique among fusion-reactor designs because they are small enough to be transported intact, allowing for mobility and modularization. While working at PPPL, I had the opportunity to personally collect and analyze experimental data. This summer allowed me to step outside of my comfort zone as a computer science major, and it was a valuable chance to tackle an unfamiliar problem.