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      Pursuing a practical approach to research

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      Koroush Shirvan, the John Clark Hardwick Career Development Professor in the Department of Nuclear Science and Engineering (NSE), knows that the nuclear industry has traditionally been wary of innovations until they are shown to have proven utility. As a result, he has relentlessly focused on practical applications in his research, work that has netted him the 2022 Reactor Technology Award from the American Nuclear Society. “The award has usually recognized practical contributions to the field of reactor design and has not often gone to academia,” Shirvan says.

      One of these “practical contributions” is in the field of accident-tolerant fuels, a program launched by the U.S. Nuclear Regulatory Commission in the wake of the 2011 Fukushima Daiichi incident. The goal within this program, says Shirvan, is to develop new forms of nuclear fuels that can tolerate heat. His team, with students from over 16 countries, is working on numerous possibilities that range in composition and method of production.

      Another aspect of Shirvan’s research focuses on how radiation impacts heat transfer mechanisms in the reactor. The team found fuel corrosion to be the driving force. “[The research] informs how nuclear fuels perform in the reactor, from a practical point of view,” Shirvan says.

      Optimizing nuclear reactor design

      A summer internship when Shirvan was an undergraduate at the University of Florida at Gainesville seeded his drive to focus on practical applications in his studies. A nearby nuclear utility was losing millions because of crud accumulating on fuel rods. Over time, the company was solving the problem by using more fuel, before it had extracted all the life from earlier batches.

      Placement of fuel rods in nuclear reactors is a complex problem with many factors — the life of the fuel, location of hot spots — affecting outcomes. Nuclear reactors change their configuration of fuel rods every 18-24 months to optimize close to 15-20 constraints, leading to roughly 200-800 assemblies. The mind-boggling nature of the problem means that plants have to rely on experienced engineers.

      During his internship, Shirvan optimized the program used to place fuel rods in the reactor. He found that certain rods in assemblies were more prone to the crud deposits, and reworked their configurations, optimizing for these rods’ performance instead of adding assemblies.

      In recent years, Shirvan has applied a branch of artificial intelligence — reinforcement learning — to the configuration problem and created a software program used by the largest U.S. nuclear utility. “This program gives even a layperson the ability to reconfigure the fuels and the reactor without having expert knowledge,” Shirvan says.

      From advanced math to counting jelly beans

      Shirvan’s own expertise in nuclear science and engineering developed quite organically. He grew up in Tehran, Iran, and when he was 14 the family moved to Gainesville, where Shirvan’s aunt and family live. He remembers an awkward couple of years at the new high school where he was grouped in with newly arrived international students, and placed in entry-level classes. “I went from doing advanced mathematics in Iran to counting jelly beans,” he laughs.

      Shirvan applied to the University of Florida for his undergraduate studies since it made economic sense; the school gave full scholarships to Floridian students who received a certain minimum SAT score. Shirvan qualified. His uncle, who was a professor in the nuclear engineering department then, encouraged Shirvan to take classes in the department. Under his uncle’s mentorship, the courses Shirvan took, and his internship, cemented his love of the interdisciplinary approach that the field demanded.

      Having always known that he wanted to teach — he remembers finishing his math tests early in Tehran so he could earn the reward of being class monitor — Shirvan knew graduate school was next. His uncle encouraged him to apply to MIT and to the University of Michigan, home to reputable programs in the field. Shirvan chose MIT because “only at MIT was there a program on nuclear design. There were faculty dedicated to designing new reactors, looking at multiple disciplines, and putting all of that together.” He went on to pursue his master’s and doctoral studies at NSE under the supervision of Professor Mujid Kazimi, focusing on compact pressurized and boiling water reactor designs. When Kazimi passed away suddenly in 2015, Shirvan was a research scientist, and switched to tenure track to guide the professor’s team.

      Another project that Shirvan took in 2015: leadership of MIT’s course on nuclear reactor technology for utility executives. Offered only by the Institute, the program is an introduction to nuclear engineering and safety for personnel who might not have much background in the area. “It’s a great course because you get to see what the real problems are in the energy sector … like grid stability,” Shirvan says.

      A multipronged approach to savings

      Another very real problem nuclear utilities face is cost. Contrary to what one hears on the news, one of the biggest stumbling blocks to building new nuclear facilities in the United States is cost, which today can be up to three times that of renewables, Shirvan says. While many approaches such as advanced manufacturing have been tried, Shirvan believes that the solution to decrease expenditures lies in designing more compact reactors.

      His team has developed an open-source advanced nuclear cost tool and has focused on two different designs: a small water reactor using compact steam technology and a horizontal gas reactor. Compactness also means making fuels more efficient, as Shirvan’s work does, and in improving the heat exchange device. It’s all back to the basics and bringing “commercial viable arguments in with your research,” Shirvan explains.

      Shirvan is excited about the future of the U.S. nuclear industry, and that the 2022 Inflation Reduction Act grants the same subsidies to nuclear as it does for renewables. In this new level playing field, advanced nuclear still has a long way to go in terms of affordability, he admits. “It’s time to push forward with cost-effective design,” Shirvan says, “I look forward to supporting this by continuing to guide these efforts with research from my team.” More

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      Machine learning facilitates “turbulence tracking” in fusion reactors

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      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

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      Simulating neutron behavior in nuclear reactors

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      Amelia Trainer applied to MIT because she lost a bet.

      As part of what the fourth-year nuclear science and engineering (NSE) doctoral student labels her “teenage rebellious phase,” Trainer was quite convinced she would just be wasting the application fee were she to submit an application. She wasn’t even “super sure” she wanted to go to college. But a high-school friend was convinced Trainer would get into a “top school” if she only applied. A bet followed: If Trainer lost, she would have to apply to MIT. Trainer lost — and is glad she did.

      Growing up in Daytona Beach, Florida, good grades were Trainer’s thing. Seeing friends participate in interschool math competitions, Trainer decided she would tag along and soon found she loved them. She remembers being adept at reading the room: If teams were especially struggling over a problem, Trainer figured the answer had to be something easy, like zero or one. “The hardest problems would usually have the most goofball answers,” she laughs.

      Simulating neutron behavior

      As a doctoral student, hard problems in math, specifically computational reactor physics, continue to be Trainer’s forte.

      Her research, under the guidance of Professor Benoit Forget in MIT NSE’s Computational Reactor Physics Group (CRPG), focuses on modeling complicated neutron behavior in reactors. Simulation helps forecast the behavior of reactors before millions of dollars sink into development of a potentially uneconomical unit. Using simulations, Trainer can see “where the neutrons are going, how much heat is being produced, and how much power the reactor can generate.” Her research helps form the foundation for the next generation of nuclear power plants.

      To simulate neutron behavior inside of a nuclear reactor, you first need to know how neutrons will interact with the various materials inside the system. These neutrons can have wildly different energies, thereby making them susceptible to different physical phenomena. For the entirety of her graduate studies, Trainer has been primarily interested in the physics regarding slow-moving neutrons and their scattering behavior.

      When a slow neutron scatters off of a material, it can induce or cancel out molecular vibrations between the material’s atoms. The effect that material vibrations can have on neutron energies, and thereby on reactor behavior, has been heavily approximated over the years. Trainer is primarily interested in chipping away at these approximations by creating scattering data for materials that have historically been misrepresented and by exploring new techniques for preparing slow-neutron scattering data.

      Trainer remembers waiting for a simulation to complete in the early days of the Covid-19 pandemic, when she discovered a way to predict neutron behavior with limited input data. Traditionally, “people have to store large tables of what neutrons will do under specific circumstances,” she says. “I’m really happy about it because it’s this really cool method of sampling what your neutron does from very little information,” Trainer says.

      Amelia Trainer — Modeling complicated neutron behavior in nuclear reactors

      As part of her research, Trainer often works closely with two software packages: OpenMC and NJOY. OpenMC is a Monte Carlo neutron transport simulation code that was developed in the CRPG and is used to simulate neutron behavior in reactor systems. NJOY is a nuclear data processing tool, and is used to create, augment, and prepare material data that is fed into tools like OpenMC. By editing both these codes to her specifications, Trainer is able to observe the effect that “upstream” material data has on the “downstream” reactor calculations. Through this, she hopes to identify additional problems: approximations that could lead to a noticeable misrepresentation of the physics.

      A love of geometry and poetry

      Trainer discovered the coolness of science as a child. Her mother, who cares for indoor plants and runs multiple greenhouses, and her father, a blacksmith and farrier, who explored materials science through his craft, were self-taught inspirations.

      Trainer’s father urged his daughter to learn and pursue any topics that she found exciting and encouraged her to read poems from “Calvin and Hobbes” out loud when she struggled with a speech impediment in early childhood. Reading the same passages every day helped her memorize them. “The natural manifestation of that extended into [a love of] poetry,” Trainer says.

      A love of poetry, combined with Trainer’s propensity for fun, led her to compose an ode to pi as part of an MIT-sponsored event for alumni. “I was really only in it for the cupcake,” she laughs. (Participants received an indulgent treat).

      Play video

      MIT Matters: A Love Poem to Pi

      Computations and nuclear science

      After being accepted at MIT, Trainer knew she wanted to study in a field that would take her skills at the levels they were at — “my math skills were pretty underdeveloped in the grand scheme of things,” she says. An open-house weekend at MIT, where she met with faculty from the NSE department, and the opportunity to contribute to a discipline working toward clean energy, cemented Trainer’s decision to join NSE.

      As a high schooler, Trainer won a scholarship to Embry-Riddle Aeronautical University to learn computer coding and knew computational physics might be more aligned with her interests. After she joined MIT as an undergraduate student in 2014, she realized that the CRPG, with its focus on coding and modeling, might be a good fit. Fortunately, a graduate student from Forget’s team welcomed Trainer’s enthusiasm for research even as an undergraduate first-year. She has stayed with the lab ever since. 

      Research internships at Los Alamos National Laboratory, the creators of NJOY, have furthered Trainer’s enthusiasm for modeling and computational physics. She met a Los Alamos scientist after he presented a talk at MIT and it snowballed into a collaboration where she could work on parts of the NJOY code. “It became a really cool collaboration which led me into a deep dive into physics and data preparation techniques, which was just so fulfilling,” Trainer says. As for what’s next, Trainer was awarded the Rickover fellowship in nuclear engineering by the the Department of Energy’s Naval Reactors Division and will join the program in Pittsburgh after she graduates.

      For many years, Trainer’s cats, Jacques and Monster, have been a constant companion. “Neutrons, computers, and cats, that’s my personality,” she laughs. Work continues to fuel her passion. To borrow a favorite phrase from Spaceman Spiff, Trainer’s favorite “Calvin” avatar, Trainer’s approach to research has invariably been: “Another day, another mind-boggling adventure.” More

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      Computing for the health of the planet

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      The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

      Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

      The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

      The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty — 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

      Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

      “By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. “They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.”

      “These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “The college plays a pivotal role in MIT’s overarching effort to hire climate-focused faculty — introducing the critical role of computing to address the health of the planet through innovative research and curriculum.”

      The four new faculty members are:

      Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

      Priya Donti will join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24. Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is also co-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

      Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

      Sherrie Wang will join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wang is currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell. More

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      MIT students contribute to success of historic fusion experiment

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      For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition in a laboratory, a grand challenge of the 21st century. The High-Energy-Density Physics (HEDP) group at MIT’s Plasma Science and Fusion Center has focused on an approach called inertial confinement fusion (ICF), which uses lasers to implode a pellet of fuel in a quest for ignition. This group, including nine former and current MIT students, was crucial to an historic ICF ignition experiment performed in 2021; the results were published on the anniversary of that success.

      On Aug. 8, 2021, researchers at the National Ignition Facility (NIF), Lawrence Livermore National Laboratory (LLNL), used 192 laser beams to illuminate the inside of a tiny gold cylinder encapsulating a spherical capsule filled with deuterium-tritium fuel in their quest to produce significant fusion energy. Although researchers had followed this process many times before, using different parameters, this time the ensuing implosion produced an historic fusion yield of 1.37 megaJoules, as measured by a suite of neutron diagnostics. These included the MIT-developed and analyzed Magnetic Recoil Spectrometer (MRS). This result was published in Physical Review Letters on Aug. 8, the one-year anniversary of the ground-breaking development, unequivocally indicating that the first controlled fusion experiment reached ignition.

      Governed by the Lawson criterion, a plasma ignites when the internal fusion heating power is high enough to overcome the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop that very rapidly increases the plasma temperature. In the case of ICF, ignition is a state where the fusion plasma can initiate a “fuel burn propagation” into the surrounding dense and cold fuel, enabling the possibility of high fusion-energy gain.

      “This historic result certainly demonstrates that the ignition threshold is a real concept, with well-predicted theoretical calculations, and that a fusion plasma can be ignited in a laboratory” says HEDP Division Head Johan Frenje.

      The HEDP division has contributed to the success of the ignition program at the NIF for more than a decade by providing and using a dozen diagnostics, implemented by MIT PhD students and staff, which have been critical for assessing the performance of an implosion. The hundreds of co-authors on the paper attest to the collaborative effort that went into this milestone. MIT’s contributors included the only student co-authors.

      “The students are responsible for implementing and using a diagnostic to obtain data important to the ICF program at the NIF, says Frenje. “Being responsible for running a diagnostic at the NIF has allowed them to actively participate in the scientific dialog and thus get directly exposed to cutting-edge science.”

      Students involved from the MIT Department of Physics were Neel Kabadi, Graeme Sutcliffe, Tim Johnson, Jacob Pearcy, and Ben Reichelt; students from the Department of Nuclear Science and Engineering included Brandon Lahmann, Patrick Adrian, and Justin Kunimune.

      In addition, former student Alex Zylstra PhD ’15, now a physicist at LLNL, was the experimental lead of this record implosion experiment. More

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      High energy and hungry for the hardest problems

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      A high school track star and valedictorian, Anne White has always relished moving fast and clearing high hurdles. Since joining the Department of Nuclear Science and Engineering (NSE) in 2009 she has produced path-breaking fusion research, helped attract a more diverse cohort of students and scholars into the discipline, and, during a worldwide pandemic, assumed the role of department head as well as co-lead of an Institute-wide initiative to address climate change. For her exceptional leadership, innovation, and accomplishments in education and research, White was named the School of Engineering Distinguished Professor of Engineering in July 2020.

      But White declares little interest in recognition or promotions. “I don’t care about all that stuff,” she says. She’s in the race for much bigger stakes. “I want to find ways to save the world with nuclear,” she says.

      Tackling turbulence

      It was this goal that drew White to MIT. Her research, honed during graduate studies at the University of California at Los Angeles, involved developing a detailed understanding of conditions inside fusion devices, and resolving issues critical to realizing the vision of fusion energy — a carbon-free, nearly limitless source of power generated by 150-million-degree plasma.

      Harnessing this superheated, gaseous form of matter requires a special donut-shaped device called a tokamak, which contains the plasma within magnetic fields. When White entered fusion around the turn of the millennium, models of plasma behavior in tokamaks didn’t reliably match observed or experimental conditions. She was determined to change that picture, working with MIT’s state-of-the-art research tokamak, Alcator C-Mod.

      Play video

      Alcator C-Mod Tokamak Tour

      White believed solving the fusion puzzle meant getting a handle on plasma turbulence — the process by which charged atomic particles, breaking out of magnetic confinement, transport heat from the core to the cool edges of the tokamak. Although researchers knew that fusion energy depends on containing and controlling the heat of plasma reactions, White recalls that when she began grad school, “it was not widely accepted that turbulence was important, and that it was central to heat transport. She “felt it was critical to compare experimental measurements to first principles physics models, so we could demonstrate the significance of turbulence and give tokamak models better predictive ability.”

      In a series of groundbreaking studies, White’s team created the tools for measuring turbulence in different conditions, and developed computational models that could account for variations in turbulence, all validated by experiments. She was one of the first fusion scientists both to perform experiments and conduct simulations. “We lived in the domain between these two worlds,” she says.

      White’s turbulence models opened up approaches for managing turbulence and maximizing tokamak performance, paving the way for net-energy fusion energy devices, including ITER, the world’s largest fusion experiment, and SPARC, a compact, high-magnetic-field tokamak, a collaboration between MIT’s Plasma Science and Fusion Center and Commonwealth Fusion Systems.

      Laser-focused on turbulence

      Growing up in the desert city of Yuma, Arizona, White spent her free time outdoors, hiking and camping. “I was always in the space of protecting the environment,” she says. The daughter of two lawyers who taught her “to argue quickly and efficiently,” she excelled in math and physics in high school. Awarded a full ride at the University of Arizona, she was intent on a path in science, one where she could tackle problems like global warming, as it was known then. Physics seemed like the natural concentration for her.

      But there was unexpected pushback. The physics advisor believed her physics grades were lackluster. “I said, ‘Who cares what this guy thinks; I’ll take physics classes anyway,’” recalls White. Being tenacious and “thick skinned,” says White, turned out to be life-altering. “I took nuclear physics, which opened my eyes to fission, which then set me off on a path of understanding nuclear power and advanced nuclear systems,” she says. Math classes introduced her to chaotic systems, and she decided she wanted to study turbulence. Then, at a Society of Physics Students meeting White says she attended for the free food, she learned about fusion.

      “I realized this was what I wanted to do,” says White. “I became totally laser focused on turbulence and tokamaks.”

      At UCLA, she began to develop instruments and methods for measuring and modeling plasma turbulence, working on three different fusion research reactors, and earning fellowships from the Department of Energy (DOE) during her graduate and post-graduate years in fusion energy science. At MIT, she received a DOE Early Career Award that enabled her to build a research team that she now considers her “legacy.”

      As she expanded her research portfolio, White was also intent on incorporating fusion into the NSE curriculum at the undergraduate and graduate level, and more broadly, on making NSE a destination for students concerned about climate change. In recognition of her efforts, she received the 2014 Junior Bose Teaching Award. She also helped design the EdX course, Nuclear Engineering: Science, Systems and Society, introducing thousands of online learners to the potential of the field. “I have to be in the classroom,” she says. “I have to be with students, interacting, and sharing knowledge and lines of inquiry with them.”

      But even as she deepened her engagement with teaching and with her fusion research, which was helping spur development of new fusion energy technologies, White could not resist leaping into a consequential new undertaking: chairing the department. “It sounds cheesy, but I did it for my kid,” she says. “I can be helpful working on fusion, but I thought, what if I can help more by enabling other people across all areas of nuclear? This department gave me so much, I wanted to give back.”

      Although the pandemic struck just months after she stepped into the role in 2019, White propelled the department toward a new strategic plan. “It captures all the urgency and passion of the faculty, and is attractive to new students, with more undergraduates enrolling and more graduate students applying,” she says. White sees the department advancing the broader goals of the field, “articulating why nuclear is fundamentally important across many dimensions for carbon-free electricity and generation.” This means getting students involved in advanced fission technologies such as nuclear batteries and small modular reactors, as well as giving them an education in fusion that will help catalyze a nascent energy industry.

      Restless for a challenge

      White feels she’s still growing into the leadership role. “I’m really enthusiastic and sometimes too intense for people, so I have to dial it back during challenging conversations,” she says. She recently completed a Harvard Business School course on leadership.

      As the recently named co-chair of MIT’s Climate Nucleus (along with Professor Noelle Selin), charged with overseeing MIT’s campus initiatives around climate change, White says she draws on a repertoire of skills that come naturally to her: listening carefully, building consensus, and seeing value in the diversity of opinion. She is optimistic about mobilizing the Institute around goals to lower MIT’s carbon footprint, “using the entire campus as a research lab,” she says.

      In the midst of this push, White continues to advance projects of concern to her, such as making nuclear physics education more accessible. She developed an in-class module involving a simple particle detector for measuring background radiation. “Any high school or university student could build this experiment in 10 minutes and see alpha particle clusters and muons,” she says.

      White is also planning to host “Rising Stars,” an international conference intended to help underrepresented groups break barriers to entry in the field of nuclear science and engineering. “Grand intellectual challenges like saving the world appeal to all genders and backgrounds,” she says.

      These projects, her departmental and institutional duties, and most recently a new job chairing DOE’s Fusion Energy Sciences Advisory Committee leave her precious little time for a life outside work. But she makes time for walks and backpacking with her husband and toddler son, and reading the latest books by female faculty colleagues, such as “The New Breed,” by Media Lab robotics researcher Kate Darling, and “When People Want Punishment,” by Lily Tsai, Ford Professor of Political Science. “There are so many things I don’t know and want to understand,” says White.

      Yet even at leisure, White doesn’t slow down. “It’s restlessness: I love to learn, and anytime someone says a problem is hard, or impossible, I want to tackle it,” she says. There’s no time off, she believes, when the goal is “solving climate change and amplifying the work of other people trying to solve it.” More

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      in Environment

      High-energy and hungry for the hardest problems

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      A high school track star and valedictorian, Anne White has always relished moving fast and clearing high hurdles. Since joining the Department of Nuclear Science and Engineering (NSE) in 2009 she has produced path-breaking fusion research, helped attract a more diverse cohort of students and scholars into the discipline, and, during a worldwide pandemic, assumed the role of department head as well as co-lead of an Institute-wide initiative to address climate change. For her exceptional leadership, innovation, and accomplishments in education and research, White was named the School of Engineering Distinguished Professor of Engineering in July 2020.

      But White declares little interest in recognition or promotions. “I don’t care about all that stuff,” she says. She’s in the race for much bigger stakes. “I want to find ways to save the world with nuclear,” she says.

      Tackling turbulence

      It was this goal that drew White to MIT. Her research, honed during graduate studies at the University of California at Los Angeles, involved developing a detailed understanding of conditions inside fusion devices, and resolving issues critical to realizing the vision of fusion energy — a carbon-free, nearly limitless source of power generated by 150-million-degree plasma.

      Harnessing this superheated, gaseous form of matter requires a special donut-shaped device called a tokamak, which contains the plasma within magnetic fields. When White entered fusion around the turn of the millennium, models of plasma behavior in tokamaks didn’t reliably match observed or experimental conditions. She was determined to change that picture, working with MIT’s state-of-the-art research tokamak, Alcator C-Mod.

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      Alcator C-Mod Tokamak Tour

      White believed solving the fusion puzzle meant getting a handle on plasma turbulence — the process by which charged atomic particles, breaking out of magnetic confinement, transport heat from the core to the cool edges of the tokamak. Although researchers knew that fusion energy depends on containing and controlling the heat of plasma reactions, White recalls that when she began grad school, “it was not widely accepted that turbulence was important, and that it was central to heat transport. She “felt it was critical to compare experimental measurements to first principles physics models, so we could demonstrate the significance of turbulence and give tokamak models better predictive ability.”

      In a series of groundbreaking studies, White’s team created the tools for measuring turbulence in different conditions, and developed computational models that could account for variations in turbulence, all validated by experiments. She was one of the first fusion scientists both to perform experiments and conduct simulations. “We lived in the domain between these two worlds,” she says.

      White’s turbulence models opened up approaches for managing turbulence and maximizing tokamak performance, paving the way for net-energy fusion energy devices, including ITER, the world’s largest fusion experiment, and SPARC, a compact, high-magnetic-field tokamak, a collaboration between MIT’s Plasma Science and Fusion Center and Commonwealth Fusion Systems.

      Laser-focused on turbulence

      Growing up in the desert city of Yuma, Arizona, White spent her free time outdoors, hiking and camping. “I was always in the space of protecting the environment,” she says. The daughter of two lawyers who taught her “to argue quickly and efficiently,” she excelled in math and physics in high school. Awarded a full ride at the University of Arizona, she was intent on a path in science, one where she could tackle problems like global warming, as it was known then. Physics seemed like the natural concentration for her.

      But there was unexpected pushback. The physics advisor believed her physics grades were lackluster. “I said, ‘Who cares what this guy thinks; I’ll take physics classes anyway,’” recalls White. Being tenacious and “thick skinned,” says White, turned out to be life-altering. “I took nuclear physics, which opened my eyes to fission, which then set me off on a path of understanding nuclear power and advanced nuclear systems,” she says. Math classes introduced her to chaotic systems, and she decided she wanted to study turbulence. Then, at a Society of Physics Students meeting White says she attended for the free food, she learned about fusion.

      “I realized this was what I wanted to do,” says White. “I became totally laser focused on turbulence and tokamaks.”

      At UCLA, she began to develop instruments and methods for measuring and modeling plasma turbulence, working on three different fusion research reactors, and earning fellowships from the Department of Energy (DOE) during her graduate and post-graduate years in fusion energy science. At MIT, she received a DOE Early Career Award that enabled her to build a research team that she now considers her “legacy.”

      As she expanded her research portfolio, White was also intent on incorporating fusion into the NSE curriculum at the undergraduate and graduate level, and more broadly, on making NSE a destination for students concerned about climate change. In recognition of her efforts, she received the 2014 Junior Bose Teaching Award. She also helped design the EdX course, Nuclear Engineering: Science, Systems and Society, introducing thousands of online learners to the potential of the field. “I have to be in the classroom,” she says. “I have to be with students, interacting, and sharing knowledge and lines of inquiry with them.”

      But even as she deepened her engagement with teaching and with her fusion research, which was helping spur development of new fusion energy technologies, White could not resist leaping into a consequential new undertaking: chairing the department. “It sounds cheesy, but I did it for my kid,” she says. “I can be helpful working on fusion, but I thought, what if I can help more by enabling other people across all areas of nuclear? This department gave me so much, I wanted to give back.”

      Although the pandemic struck just months after she stepped into the role in 2019, White propelled the department toward a new strategic plan. “It captures all the urgency and passion of the faculty, and is attractive to new students, with more undergraduates enrolling and more graduate students applying,” she says. White sees the department advancing the broader goals of the field, “articulating why nuclear is fundamentally important across many dimensions for carbon-free electricity and generation.” This means getting students involved in advanced fission technologies such as nuclear batteries and small modular reactors, as well as giving them an education in fusion that will help catalyze a nascent energy industry.

      Restless for a challenge

      White feels she’s still growing into the leadership role. “I’m really enthusiastic and sometimes too intense for people, so I have to dial it back during challenging conversations,” she says. She recently completed a Harvard Business School course on leadership.

      As the recently named co-chair of MIT’s Climate Nucleus (along with Professor Noelle Selin), charged with overseeing MIT’s campus initiatives around climate change, White says she draws on a repertoire of skills that come naturally to her: listening carefully, building consensus, and seeing value in the diversity of opinion. She is optimistic about mobilizing the Institute around goals to lower MIT’s carbon footprint, “using the entire campus as a research lab,” she says.

      In the midst of this push, White continues to advance projects of concern to her, such as making nuclear physics education more accessible. She developed an in-class module involving a simple particle detector for measuring background radiation. “Any high school or university student could build this experiment in 10 minutes and see alpha particle clusters and muons,” she says.

      White is also planning to host “Rising Stars,” an international conference intended to help underrepresented groups break barriers to entry in the field of nuclear science and engineering. “Grand intellectual challenges like saving the world appeal to all genders and backgrounds,” she says.

      These projects, her departmental and institutional duties, and most recently a new job chairing DOE’s Fusion Energy Sciences Advisory Committee leave her precious little time for a life outside work. But she makes time for walks and backpacking with her husband and toddler son, and reading the latest books by female faculty colleagues, such as “The New Breed,” by Media Lab robotics researcher Kate Darling, and “When People Want Punishment,” by Lily Tsai, Ford Professor of Political Science. “There are so many things I don’t know and want to understand,” says White.

      Yet even at leisure, White doesn’t slow down. “It’s restlessness: I love to learn, and anytime someone says a problem is hard, or impossible, I want to tackle it,” she says. There’s no time off, she believes, when the goal is “solving climate change and amplifying the work of other people trying to solve it.” More

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      in Energy

      Solving a longstanding conundrum in heat transfer

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      It is a problem that has beguiled scientists for a century. But, buoyed by a $625,000 Distinguished Early Career Award from the U.S. Department of Energy (DoE), Matteo Bucci, an associate professor in the Department of Nuclear Science and Engineering (NSE), hopes to be close to an answer.

      Tackling the boiling crisis

      Whether you’re heating a pot of water for pasta or are designing nuclear reactors, one phenomenon — boiling — is vital for efficient execution of both processes.

      “Boiling is a very effective heat transfer mechanism; it’s the way to remove large amounts of heat from the surface, which is why it is used in many high-power density applications,” Bucci says. An example use case: nuclear reactors.

      To the layperson, boiling appears simple — bubbles form and burst, removing heat. But what if so many bubbles form and coalesce that they form a band of vapor that prevents further heat transfer? Such a problem is a known entity and is labeled the boiling crisis. It would lead to runaway heat, and a failure of fuel rods in nuclear reactors. So “understanding and determining under which conditions the boiling crisis is likely to happen is critical to designing more efficient and cost-competitive nuclear reactors,” Bucci says.

      Early work on the boiling crisis dates back nearly a century ago, to 1926. And while much work has been done, “it is clear that we haven’t found an answer,” Bucci says. The boiling crisis remains a challenge because while models abound, the measurement of related phenomena to prove or disprove these models has been difficult. “[Boiling] is a process that happens on a very, very small length scale and over very, very short times,” Bucci says. “We are not able to observe it at the level of detail necessary to understand what really happens and validate hypotheses.”

      But, over the past few years, Bucci and his team have been developing diagnostics that can measure the phenomena related to boiling and thereby provide much-needed answers to a classic problem. Diagnostics are anchored in infrared thermometry and a technique using visible light. “By combining these two techniques I think we’re going to be ready to answer standing questions related to heat transfer, we can make our way out of the rabbit hole,” Bucci says. The grant award from the U.S. DoE for Nuclear Energy Projects will aid in this and Bucci’s other research efforts.

      An idyllic Italian childhood

      Tackling difficult problems is not new territory for Bucci, who grew up in the small town of Città di Castello near Florence, Italy. Bucci’s mother was an elementary school teacher. His father used to have a machine shop, which helped develop Bucci’s scientific bent. “I liked LEGOs a lot when I was a kid. It was a passion,” he adds.

      Despite Italy going through a severe pullback from nuclear engineering during his formative years, the subject fascinated Bucci. Job opportunities in the field were uncertain but Bucci decided to dig in. “If I have to do something for the rest of my life, it might as well be something I like,” he jokes. Bucci attended the University of Pisa for undergraduate and graduate studies in nuclear engineering.

      His interest in heat transfer mechanisms took root during his doctoral studies, a research subject he pursued in Paris at the French Alternative Energies and Atomic Energy Commission (CEA). It was there that a colleague suggested work on the boiling water crisis. This time Bucci set his sights on NSE at MIT and reached out to Professor Jacopo Buongiorno to inquire about research at the institution. Bucci had to fundraise at CEA to conduct research at MIT. He arrived just a couple of days before the Boston Marathon bombing in 2013 with a round-trip ticket. But Bucci has stayed ever since, moving on to become a research scientist and then associate professor at NSE.

      Bucci admits he struggled to adapt to the environment when he first arrived at MIT, but work and friendships with colleagues — he counts NSE’s Guanyu Su and Reza Azizian as among his best friends — helped conquer early worries.

      The integration of artificial intelligence

      In addition to diagnostics for boiling, Bucci and his team are working on ways of integrating artificial intelligence and experimental research. He is convinced that “the integration of advanced diagnostics, machine learning, and advanced modeling tools will blossom in a decade.”

      Bucci’s team is developing an autonomous laboratory for boiling heat transfer experiments. Running on machine learning, the setup decides which experiments to run based on a learning objective the team assigns. “We formulate a question and the machine will answer by optimizing the kinds of experiments that are necessary to answer those questions,” Bucci says, “I honestly think this is the next frontier for boiling,” he adds.

      “It’s when you climb a tree and you reach the top, that you realize that the horizon is much more vast and also more beautiful,” Bucci says of his zeal to pursue more research in the field.

      Even as he seeks new heights, Bucci has not forgotten his origins. Commemorating Italy’s hosting of the World Cup in 1990, a series of posters showcasing a soccer field fitted into the Roman Colosseum occupies pride of place in his home and office. Created by Alberto Burri, the posters are of sentimental value: The (now deceased) Italian artist also hailed from Bucci’s hometown — Città di Castello. More