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      in Energy

      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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      in Energy

      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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      in Energy

      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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      in Energy

      High energy and hungry for the hardest problems

      by

      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

    • 88 Shares149 Views

      in Environment

      High-energy and hungry for the hardest problems

      by

      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

    • 88 Shares159 Views

      in Energy

      Solving a longstanding conundrum in heat transfer

      by

      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

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      in Energy

      A better way to quantify radiation damage in materials

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      It was just a piece of junk sitting in the back of a lab at the MIT Nuclear Reactor facility, ready to be disposed of. But it became the key to demonstrating a more comprehensive way of detecting atomic-level structural damage in materials — an approach that will aid the development of new materials, and could potentially support the ongoing operation of carbon-emission-free nuclear power plants, which would help alleviate global climate change.

      A tiny titanium nut that had been removed from inside the reactor was just the kind of material needed to prove that this new technique, developed at MIT and at other institutions, provides a way to probe defects created inside materials, including those that have been exposed to radiation, with five times greater sensitivity than existing methods.

      The new approach revealed that much of the damage that takes place inside reactors is at the atomic scale, and as a result is difficult to detect using existing methods. The technique provides a way to directly measure this damage through the way it changes with temperature. And it could be used to measure samples from the currently operating fleet of nuclear reactors, potentially enabling the continued safe operation of plants far beyond their presently licensed lifetimes.

      The findings are reported today in the journal Science Advances in a paper by MIT research specialist and recent graduate Charles Hirst PhD ’22; MIT professors Michael Short, Scott Kemp, and Ju Li; and five others at the University of Helsinki, the Idaho National Laboratory, and the University of California at Irvine.

      Rather than directly observing the physical structure of a material in question, the new approach looks at the amount of energy stored within that structure. Any disruption to the orderly structure of atoms within the material, such as that caused by radiation exposure or by mechanical stresses, actually imparts excess energy to the material. By observing and quantifying that energy difference, it’s possible to calculate the total amount of damage within the material — even if that damage is in the form of atomic-scale defects that are too small to be imaged with microscopes or other detection methods.

      The principle behind this method had been worked out in detail through calculations and simulations. But it was the actual tests on that one titanium nut from the MIT nuclear reactor that provided the proof — and thus opened the door to a new way of measuring damage in materials.

      The method they used is called differential scanning calorimetry. As Hirst explains, this is similar in principle to the calorimetry experiments many students carry out in high school chemistry classes, where they measure how much energy it takes to raise the temperature of a gram of water by one degree. The system the researchers used was “fundamentally the exact same thing, measuring energetic changes. … I like to call it just a fancy furnace with a thermocouple inside.”

      The scanning part has to do with gradually raising the temperature a bit at a time and seeing how the sample responds, and the differential part refers to the fact that two identical chambers are measured at once, one empty, and one containing the sample being studied. The difference between the two reveals details of the energy of the sample, Hirst explains.

      “We raise the temperature from room temperature up to 600 degrees Celsius, at a constant rate of 50 degrees per minute,” he says. Compared to the empty vessel, “your material will naturally lag behind because you need energy to heat your material. But if there are changes in the energy inside the material, that will change the temperature. In our case, there was an energy release when the defects recombine, and then it will get a little bit of a head start on the furnace … and that’s how we are measuring the energy in our sample.”

      Hirst, who carried out the work over a five-year span as his doctoral thesis project, found that contrary to what had been believed, the irradiated material showed that there were two different mechanisms involved in the relaxation of defects in titanium at the studied temperatures, revealed by two separate peaks in calorimetry. “Instead of one process occurring, we clearly saw two, and each of them corresponds to a different reaction that’s happening in the material,” he says.

      They also found that textbook explanations of how radiation damage behaves with temperature weren’t accurate, because previous tests had mostly been carried out at extremely low temperatures and then extrapolated to the higher temperatures of real-life reactor operations. “People weren’t necessarily aware that they were extrapolating, even though they were, completely,” Hirst says.

      “The fact is that our common-knowledge basis for how radiation damage evolves is based on extremely low-temperature electron radiation,” adds Short. “It just became the accepted model, and that’s what’s taught in all the books. It took us a while to realize that our general understanding was based on a very specific condition, designed to elucidate science, but generally not applicable to conditions in which we actually want to use these materials.”

      Now, the new method can be applied “to materials plucked from existing reactors, to learn more about how they are degrading with operation,” Hirst says.

      “The single biggest thing the world can do in order to get cheap, carbon-free power is to keep current reactors on the grid. They’re already paid for, they’re working,” Short adds.  But to make that possible, “the only way we can keep them on the grid is to have more certainty that they will continue to work well.” And that’s where this new way of assessing damage comes into play.

      While most nuclear power plants have been licensed for 40 to 60 years of operation, “we’re now talking about running those same assets out to 100 years, and that depends almost fully on the materials being able to withstand the most severe accidents,” Short says. Using this new method, “we can inspect them and take them out before something unexpected happens.”

      In practice, plant operators could remove a tiny sample of material from critical areas of the reactor, and analyze it to get a more complete picture of the condition of the overall reactor. Keeping existing reactors running is “the single biggest thing we can do to keep the share of carbon-free power high,” Short stresses. “This is one way we think we can do that.”

      Sergei Dudarev, a fellow at the United Kingdom Atomic Energy Authority who was not associated with this work, says this “is likely going to be impactful, as it confirms, in a nice systematic manner, supported both by experiment and simulations, the unexpectedly significant part played by the small invisible defects in microstructural evolution of materials exposed to irradiation.”

      The process is not just limited to the study of metals, nor is it limited to damage caused by radiation, the researchers say. In principle, the method could be used to measure other kinds of defects in materials, such as those caused by stresses or shockwaves, and it could be applied to materials such as ceramics or semiconductors as well.

      In fact, Short says, metals are the most difficult materials to measure with this method, and early on other researchers kept asking why this team was focused on damage to metals. That was partly because reactor components tend to be made of metal, and also because “It’s the hardest, so, if we crack this problem, we have a tool to crack them all!”

      Measuring defects in other kinds of materials can be up to 10,000 times easier than in metals, he says. “If we can do this with metals, we can make this extremely, ubiquitously applicable.” And all of it enabled by a small piece of junk that was sitting at the back of a lab.

      The research team included Fredric Granberg and Kai Nordlund at the University of Helsinki in Finland; Boopathy Kombaiah and Scott Middlemas at Idaho National Laboratory; and Penghui Cao at the University of California at Irvine. The work was supported by the U.S. National Science Foundation, an Idaho National Laboratory research grant, and a Euratom Research and Training program grant. More

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      in Energy

      New hardware offers faster computation for artificial intelligence, with much less energy

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      As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

      Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

      A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

      Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

      “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

      “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

      “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

      These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

      “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

      Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

      Accelerating deep learning

      Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

      The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

      In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

      The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

      To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

      PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

      Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

      Surprising speed

      PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

      “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

      “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

      Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

      Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

      Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

      At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

      “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

      “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

      “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

      “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

      This research is funded, in part, by the MIT-IBM Watson AI Lab. More