Building Trustworthy Space SimulationsThrough Scientific Computing
By Wendy Sutton, Office of the Vice President of Research
Rona Oran, Computational scientist, Massachusetts Institute of Technology
In 2006, Rona Oran traveled to Türkiye with her department at Tel Aviv University to watch a total solar eclipse. As a master’s student in space physics, she found the experience fascinating and it helped change her career trajectory.
“I became very excited about the lavish structure of the corona and wondered, ‘How can we understand something so spectacular and complex?’” Oran said. “The University of Michigan had a group that modeled it, so I moved to U-M to study the Sun’s corona and the solar wind using observations and models.”
At U-M, Oran’s doctoral work in space science and scientific computing focused on space plasmas and their interactions with planetary bodies, where she developed computational models of the solar corona and solar wind.
“Modeling this system using complex computational frameworks, without a deep understanding of how those tools work, was like being an astronomer who uses a telescope but does not understand how the lenses focus light,” Oran said. “I joined the Ph.D. in Scientific Computing program at U-M, and that turned out to be one of my best career decisions. It provided a solid foundation. I learned to truly understand the tools’ advantages and limitations.”
Oran’s work modeling the corona has critical technological implications. When the solar corona and solar wind undergo large changes that reach Earth’s magnetosphere, they can cause geomagnetic storms that disrupt power grids, satellites and telecommunications and pose serious risks to astronaut safety.
The U.S. government has invested heavily in space weather models that Oran helped develop at U-M, with funding from the Center for Space Environment Modeling, led by Tamas Gombosi, Konstantin I. Gringauz Distinguished University Professor of Space Science. These models are now part of simulation tools used by NASA and NOAA to predict space weather and continue to be developed and improved by researchers.
After leaving U-M, Oran joined the Massachusetts Institute of Technology as a postdoctoral researcher, where she used simulations to study how meteorite impacts could release plasma and potentially magnetize parts of the Moon. Although the Moon does not have a magnetic field today, Apollo astronauts returned magnetized rocks. Oran investigated long-standing theories about whether the Moon may once have had a weak magnetic field or whether impact-generated plasma could explain the magnetization found in those samples. Still, the necessary computational plasma tools did not yet exist. Using tools initially developed for space weather, Oran adapted the plasma simulation framework she had used at U-M to address the unique processes in impact plasmas.
The metal asteroid 16 Psyche may have a strong remnant magnetic field.
16 Psyche’s magnetosphere for different axis orientations.
Oran then became the principal investigator on a NASA Solar System Workings program project. There, she led an international team of scientists investigating how the crustal magnetic fields of the Moon and Mercury were formed. Her scientific computing expertise enabled the team to integrate plasma simulations, impact-shock simulations of crater formation and inversion techniques for spacecraft magnetic-field measurements.
The team found that while impacts themselves could not have magnetized the Moon, they could explain some of the more intense magnetization found by Apollo astronauts, possibly reconciling a decades-long puzzle.
Oran tackled the challenge of separating solar-wind signals from a planetary body’s own magnetic field while serving as the Magnetometry Investigation Scientist for NASA’s Psyche mission through 2026. In that role, she led the development of plasma models of the asteroid 16 Psyche, which were pivotal in analyzing mission scenarios for detecting the asteroid’s magnetic field, likely the first such detection around an asteroid. Unlike the large-scale simulations she performed for the Sun and the Moon, simulating asteroidal magnetic fields led her to adopt a more advanced computational framework, one capable of simulating plasmas at much smaller scales.
She used these advanced simulations to guide mission planning with NASA stakeholders and to design the Magnetometry Investigation data pipeline that could process raw mission data and convert it into scientific insights, including 3D reconstructions of the magnetic field around the asteroid. This would help the Psyche and Magnetometry Investigation team to determine whether the field originates from the asteroid or from interplanetary space and trace its history.
Through her work, Oran explores how magnetic fields affect planetary habitability and atmospheric retention. Mars lost both its magnetic field and much of its atmosphere, while Venus lacks a magnetic field but retains a dense atmosphere. Together, these contrasts help frame the broader question she is investigating: how plasma dynamics and changing magnetic fields shape planetary atmospheres, including Earth’s far-future evolution.
Oran credits her scientific computing training at U-M, along with interdisciplinary courses in aerospace engineering, mathematics and computer science, with making her more fearless about venturing into new areas of research, constructing and validating computational models and leading interdisciplinary teams.
Changes in 16 Psyche’s magnetosphere as a result of a solar wind flowing around it.
Looking ahead, Oran is exploring how AI can accelerate her work. Because solar wind simulations are expensive to run on thousands of processors at NASA centers, she is using previously collected models and data to train AI models to more efficiently predict the solar wind. Through Schmidt Sciences capstone projects at Arizona State University, she is also giving students opportunities to apply new AI approaches to the kinds of numerical models she learned more than a decade earlier at U-M.
“With AI, it is now easier to write code,” Oran said. “But scientific computing is still critical because it helps you understand the tools. When you solve a problem that nobody has solved before, you can trust the results because you understand the sources of uncertainty and margins of error. That’s what U-M gave me. Not just a tool, but the confidence to know when I’ve discovered something real.”