Ian Ochs got his scientific start in biology at Philadelphia’s Fox Chase Cancer Center, where his parents worked. In his early teens, the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient did small-scale DNA experiments in his mother’s basic research lab. In high school, he first worked with biostatistics, and later on using computers to predict protein folding. “I found that really cool,” Ochs says. “That was my introduction to computer science and how you could use it to automate mathematical methods.”
Ochs majored in physics at Harvard University but continued pursuing research that combined that field with biology and math. He studied evolutionary dynamics with Michael Desai, using mathematical models to examine how organisms can improve survival through acquired genetic mutations. During his junior year, Ochs considered research areas for graduate school and decided he wanted experience in a more traditional physics area: fusion energy. Although fusion plasmas and evolving populations of organisms sound wildly different, “they’re both incredibly complicated systems,” Ochs says, “and you’re trying to find really simple tools that give you general principles to describe them.”
His Harvard advisor, Melissa Franklin, connected Ochs with Princeton University’s Nathaniel Fisch, and Ochs received an Energy Department summer fellowship to work at the Princeton Plasma Physics Laboratory, a DOE facility.Fusion reactors house plasma, a swirling mix of ions and free electrons. Hydrogen isotopes fuse in this super-hot fluid, producing high-energy alpha particles (also known as helium ions). That energy can be released as heat or can create instabilities within the plasma. Ochs explored ways to channel alpha particle energy into waves to help confine the plasma or to serve other uses within the reactor. Ochs enjoyed the research so much that he returned to Princeton for graduate school and eventually pursued his thesis work under Fisch’s supervision.
Ochs’ graduate studies started with an experimental project designed to separate nuclear waste by first turning it into a plasma. Though the experimental platform he worked on was discontinued, the initiative led him to think about plasma collisions and transport processes more broadly.
Plasmas in centrifuges will always contain collisions, Ochs notes, so “how can we actually make use of the collisions to accomplish our separation goals?” During these crashes, particle, momentum and heat transport in the plasma forms an interconnected and highly nonlinear system. For instance, energy transport heats and cools the plasma yet also changes its viscosity, altering its rotation. This modifies the plasma’s viscous heating, feeding back on energy transport.
To examine these transport problems, Ochs and another Princeton graduate student, Elijah Kolmes, built a novel code they named MITNS and used it to check insights from their models against the full set of nonlinear differential equations.
Ochs is now building on the intuition developed from transport modeling and applying it to his original question of how to extract energy from alpha particles using waves. “I’m hoping to pull together a cohesive picture of how the wave-plasma interaction works, focusing on the connections between the energy and the momentum transport,” he says.
MITNS is a fluid code, a type that considers the average position and velocity of particles rather than calculating them individually. During his 2018 Lawrence Livermore National Laboratory practicum, Ochs worked with Dick Berger on a different type: a kinetic code called LOKI. These algorithms, which demand more computational power, consider individual particle positions and velocities within plasma.
Ochs worked to adapt LOKI to study magnetized plasmas. Incorporating such forces into a code that wasn’t designed to include them required some debugging, but Ochs demonstrated in simulations that LOKI could identify known magnetized plasma waves. Once his analytic theory of wave-mediated momentum and energy transport is more fully developed, he hopes to run full simulations with the revised LOKI code to test and further extend the theory.
Ochs was recently awarded a Jacobus Fellowship – a top honor for Princeton graduate students – that will support the final year of his degree. He expects to graduate in August 2021 and then take a postdoctoral research position at a national laboratory or in academia.
The DOE CSGF has helped Ochs learn new programming methods and connected him with computational researchers studying a variety of problems, he says. “Often techniques from a very different area of science will turn out to be really useful even if they just spur a new way of thinking about your own research problem.”
Image caption: This simulation examined how temperature gradients can help flush ash from a magneto-inertial fusion reactor’s core and boost energy yield. The panels show changes in alpha particle concentration (left), fuel concentration (center) and fusion power density (right). At left, the core alpha particle (ash) concentration has declined (blue shades). The core fuel concentration (middle) correspondingly increases (yellow). Fusion power density (right) is thus enhanced at the core (yellow center, surrounded by green). Credit: Ochs, I. E., & Fisch, N. J. (2018). Favorable collisional demixing of ash and fuel in magnetized inertial fusion. Physical Review Letters, 121(23), 235002.