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      in Environment

      MIT-led teams win National Science Foundation grants to research sustainable materials

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      Three MIT-led teams are among 16 nationwide to receive funding awards to address sustainable materials for global challenges through the National Science Foundation’s Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling societal or scientific challenges at an accelerated pace, by incorporating a multidisciplinary research approach.

      “Solutions for today’s national-scale societal challenges are hard to solve within a single discipline. Instead, these challenges require convergence to merge ideas, approaches, and technologies from a wide range of diverse sectors, disciplines, and experts,” the NSF explains in its description of the Convergence Accelerator program. Phase 1 of the award involves planning to expand initial concepts, identify new team members, participate in an NSF development curriculum, and create an early prototype.

      Sustainable microchips

      One of the funded projects, “Building a Sustainable, Innovative Ecosystem for Microchip Manufacturing,” will be led by Anuradha Murthy Agarwal, a principal research scientist at the MIT Materials Research Laboratory. The aim of this project is to help transition the manufacturing of microchips to more sustainable processes that, for example, can reduce e-waste landfills by allowing repair of chips, or enable users to swap out a rogue chip in a motherboard rather than tossing out the entire laptop or cellphone.

      “Our goal is to help transition microchip manufacturing towards a sustainable industry,” says Agarwal. “We aim to do that by partnering with industry in a multimodal approach that prototypes technology designs to minimize energy consumption and waste generation, retrains the semiconductor workforce, and creates a roadmap for a new industrial ecology to mitigate materials-critical limitations and supply-chain constraints.”

      Agarwal’s co-principal investigators are Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University, and two MIT faculty affiliated with the Materials Research Laboratory: Juejun Hu, the John Elliott Professor of Materials Science and Engineering; and Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering.

      The training component of the project will also create curricula for multiple audiences. “At Bridgewater State University, we will create a new undergraduate course on microchip manufacturing sustainability, and eventually adapt it for audiences from K-12, as well as incumbent employees,” says Serna.

      Sajan Saini and Erik Verlage of the MIT Department of Materials Science and Engineering (DMSE), and Randolph Kirchain from the MIT Materials Systems Laboratory, who have led MIT initiatives in virtual reality digital education, materials criticality, and roadmapping, are key contributors. The project also includes DMSE graduate students Drew Weninger and Luigi Ranno, and undergraduate Samuel Bechtold from Bridgewater State University’s Department of Physics.

      Sustainable topological materials

      Under the direction of Mingda Li, the Class of 1947 Career Development Professor and an Associate Professor of Nuclear Science and Engineering, the “Sustainable Topological Energy Materials (STEM) for Energy-efficient Applications” project will accelerate research in sustainable topological quantum materials.

      Topological materials are ones that retain a particular property through all external disturbances. Such materials could potentially be a boon for quantum computing, which has so far been plagued by instability, and would usher in a post-silicon era for microelectronics. Even better, says Li, topological materials can do their job without dissipating energy even at room temperatures.

      Topological materials can find a variety of applications in quantum computing, energy harvesting, and microelectronics. Despite their promise, and a few thousands of potential candidates, discovery and mass production of these materials has been challenging. Topology itself is not a measurable characteristic so researchers have to first develop ways to find hints of it. Synthesis of materials and related process optimization can take months, if not years, Li adds. Machine learning can accelerate the discovery and vetting stage.

      Given that a best-in-class topological quantum material has the potential to disrupt the semiconductor and computing industries, Li and team are paying special attention to the environmental sustainability of prospective materials. For example, some potential candidates include gold, lead, or cadmium, whose scarcity or toxicity does not lend itself to mass production and have been disqualified.

      Co-principal investigators on the project include Liang Fu, associate professor of physics at MIT; Tomas Palacios, professor of electrical engineering and computer science at MIT and director of the Microsystems Technology Laboratories; Susanne Stemmer of the University of California at Santa Barbara; and Qiong Ma of Boston College. The $750,000 one-year Phase 1 grant will focus on three priorities: building a topological materials database; identifying the most environmentally sustainable candidates for energy-efficient topological applications; and building the foundation for a Center for Sustainable Topological Energy Materials at MIT that will encourage industry-academia collaborations.

      At a time when the size of silicon-based electronic circuit boards is reaching its lower limit, the promise of topological materials whose conductivity increases with decreasing size is especially attractive, Li says. In addition, topological materials can harvest wasted heat: Imagine using your body heat to power your phone. “There are different types of application scenarios, and we can go much beyond the capabilities of existing materials,” Li says, “the possibilities of topological materials are endlessly exciting.”

      Socioresilient materials design

      Researchers in the MIT Department of Materials Science and Engineering (DMSE) have been awarded $750,000 in a cross-disciplinary project that aims to fundamentally redirect materials research and development toward more environmentally, socially, and economically sustainable and resilient materials. This “socioresilient materials design” will serve as the foundation for a new research and development framework that takes into account technical, environmental, and social factors from the beginning of the materials design and development process.

      Christine Ortiz, the Morris Cohen Professor of Materials Science and Engineering, and Ellan Spero PhD ’14, an instructor in DMSE, are leading this research effort, which includes Cornell University, the University of Swansea, Citrine Informatics, Station1, and 14 other organizations in academia, industry, venture capital, the social sector, government, and philanthropy.

      The team’s project, “Mind Over Matter: Socioresilient Materials Design,” emphasizes that circular design approaches, which aim to minimize waste and maximize the reuse, repair, and recycling of materials, are often insufficient to address negative repercussions for the planet and for human health and safety.

      Too often society understands the unintended negative consequences long after the materials that make up our homes and cities and systems have been in production and use for many years. Examples include disparate and negative public health impacts due to industrial scale manufacturing of materials, water and air contamination with harmful materials, and increased risk of fire in lower-income housing buildings due to flawed materials usage and design. Adverse climate events including drought, flood, extreme temperatures, and hurricanes have accelerated materials degradation, for example in critical infrastructure, leading to amplified environmental damage and social injustice. While classical materials design and selection approaches are insufficient to address these challenges, the new research project aims to do just that.

      “The imagination and technical expertise that goes into materials design is too often separated from the environmental and social realities of extraction, manufacturing, and end-of-life for materials,” says Ortiz. 

      Drawing on materials science and engineering, chemistry, and computer science, the project will develop a framework for materials design and development. It will incorporate powerful computational capabilities — artificial intelligence and machine learning with physics-based materials models — plus rigorous methodologies from the social sciences and the humanities to understand what impacts any new material put into production could have on society. More

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      in Energy

      Working to make nuclear energy more competitive

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      Assil Halimi has loved science since he was a child, but it was a singular experience at a college internship that stoked his interest in nuclear engineering. As part of work on a conceptual design for an aircraft electric propulsion system, Halimi had to read a chart that compared the energy density of various fuel sources. He was floored to see that the value for uranium was orders of magnitude higher than the rest. “Just a fuel pellet the size of my fingertip can generate as much energy as a ton of coal or 150 gallons of oil,” Halimi points out.

      Having grown up in Algeria, in an economy dominated by oil and gas, Halimi was always aware of energy’s role in fueling growth. But here was a source that showed enormous potential. “The more I read about nuclear, the more I saw its direct relationship with climate change and how nuclear energy can potentially replace the carbonized economy,” Halimi says. “The problem we’re dealing with right now is that the source of energy is not clean. Nuclear [presented itself] as an answer, or at least as a promise that you can dig into,” he says. “I was also seeing the electrification of systems and the economy evolving.”

      A tectonic shift was brewing, and Halimi wanted in.

      Then an electrical engineering major at the Institut National des Sciences Appliquées de Lyon (INSA Lyon), Halimi added nuclear engineering as a second major. Today, the second-year doctoral student at MIT’s Department of Nuclear Science and Engineering (NSE) has expanded on his early curiosity in the field and researches methods of improving the design of small modular reactors. Under Professor Koroush Shirvan’s advisement, Halimi also studies high burnup fuel so we can extract more energy from the same amount of material.

      A foot in two worlds

      The son of a computer engineer father and a mother who works as a judge, Halimi was born in Algiers and grew up in Cherchell, a small town near the capital. His interest in science grew sharper in middle school; Halimi remembers being a member of the astronomy club. As a middle and high schooler, Halimi traveled to areas with low light pollution to observe the night skies.

      As a teenager, Halimi set his goals high, enrolling in high school in both Algeria and France. Taking classes in Arabic and French, he found a fair amount of overlap between the two curricula. The divergence in the nonscientific classes gave Halimi a better understanding of the cultural perspectives. After studying the French curriculum remotely, Halimi graduated with two diplomas. He remembers having to take two baccalaureate exams, which didn’t bother him much, but he did have to miss viewing parts of the 2014 World Cup soccer tournament.

      A multidisciplinary approach to engineering

      After high school, Halimi moved to France to study engineering at INSA Lyon. He elected for a major in electrical engineering and, ever the pragmatist, also signed up for a bachelor’s degree in math and economics. “You can build a lot of amazing things, but you have to take costs into account to make sure you’re proposing something feasible that can make it in the real world,” Halimi says, explaining his motivation to study economics.

      Wrapping up his bachelor’s in math and economics in two short years, Halimi decided to pursue a double curriculum in electrical and nuclear engineering during his final year of engineering studies. Since his school in Lyon did not offer the double curriculum, Halimi had to move to Paris to study at The French Alternative Energies and Atomic Energy Commission (CEA), part of the University of Paris-Saclay. The summer before he started, he traveled to Japan and toured the Fukushima nuclear power plant.

      Halimi first conducted research at MIT NSE as part of an internship in nuclear engineering when he was still a student in France. He remembers wanting to explore work on reactor design, when an advisor at CEA recommended interning with Shirvan.

      Pragmatism in nuclear energy adoption

      Halimi’s work at MIT NSE focuses on high burnup fuel assessment and small modular reactor (SMR) design.

      Existing nuclear plants have faced stiff competition during the last decade. Improving the fuel efficiency (high burnup) is a potential way of improving the economic competitiveness of the existing reactor fleet. One challenge is that materials degrade when you keep them longer in the reactor. Halimi evaluates fuel performance and safety features of more efficient fuel operation using advanced computer simulation tools. At the 2022 TopFuel Light Water Reactor Fuel Performance Conference, Halimi presented a paper describing strategies to achieve higher burnups. He is now working on journal paper about this work.

      Halimi’s research on SMR design is motivated by the industry’s move to smaller plants that take less time to construct. The challenge, he says, is that if you simply make the reactors smaller, you lose the advantages of economies of scale and might end up with a more expensive economic proposal. Halimi’s goal is to analyze how smaller reactors can compensate for economies of scale by improving their technical design. Other advantages stacked in favor of smaller reactors is that they can be constructed faster and in series.

      Halimi analyzes the fuel performance, core design, thermal hydraulics, and safety of these small reactors. “One efficient way that I particularly assess to improve their economics is high power density operation,” he says. In late 2021 Halimi published a paper on the relationship between cost and reactor power density in Nuclear Engineering and Design Journal. The research has been featured in other conference papers.

      When he’s not working, Halimi makes time to play soccer and hopes to get back into astronomy. “I sold all my gear when I moved from Europe so I need to buy new ones at some point,” he says.

      Halimi is convinced that nuclear power will be a serious contender in the energy landscape. “You have to propose something that will make everyone happy,” Halimi laughs when he describes work in nuclear science and engineering.

      The work ahead is daunting — “Nuclear power is safe, sustainable, and reliable; now we need to be on time and on budget [to achieve] climate goals” he says — but Halimi is ready. By addressing both the competitiveness of the existing reactors through high burnup fuels and designing the next generation of nuclear plants, he is adopting a dual-pronged approach to make nuclear energy an economical and viable alternative to carbon-based fuels. More

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      in Energy

      Preparing students for the new nuclear

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      As nuclear power has gained greater recognition as a zero-emission energy source, the MIT Leaders for Global Operations (LGO) program has taken notice.

      Two years ago, LGO began a collaboration with MIT’s Department of Nuclear Science and Engineering (NSE) as a way to showcase the vital contribution of both business savvy and scientific rigor that LGO’s dual-degree graduates can offer this growing field.

      “We saw that the future of fission and fusion required business acumen and management acumen,” says Professor Anne White, NSE department head. “People who are going to be leaders in our discipline, and leaders in the nuclear enterprise, are going to need all of the technical pieces of the puzzle that our engineering department can provide in terms of education and training. But they’re also going to need a much broader perspective on how the technology connects with society through the lens of business.”

      The resulting response has been positive: “Companies are seeing the value of nuclear technology for their operations,” White says, and this often happens in unexpected ways.

      For example, graduate student Santiago Andrade recently completed a research project at Caterpillar Inc., a preeminent manufacturer of mining and construction equipment. Caterpillar is one of more than 20 major companies that partner with the LGO program, offering six-month internships to each student. On the surface, it seemed like an improbable pairing; what could Andrade, who was pursuing his master’s in nuclear science and engineering, do for a manufacturing company? However, Caterpillar wanted to understand the technical and commercial feasibility of using nuclear energy to power mining sites and data centers when wind and solar weren’t viable.

      “They are leaving no stone unturned in the search of financially smart solutions that can support the transition to a clean energy dependency,” Andrade says. “My project, along with many others’, is part of this effort.”

      “The research done through the LGO program with Santiago is enabling Caterpillar to understand how alternative technologies, like the nuclear microreactor, could participate in these markets in the future,” says Brian George, product manager for large electric power solutions at Caterpillar. “Our ability to connect our customers with the research will provide for a more accurate understanding of the potential opportunity, and helps provide exposure for our customers to emerging technologies.”

      With looming threats of climate change, White says, “We’re going to require more opportunities for nuclear technologies to step in and be part of those solutions. A cohort of LGO graduates will come through this program with technical expertise — a master’s degree in nuclear engineering — and an MBA. There’s going to be a tremendous talent pool out there to help companies and governments.”

      Andrade, who completed an undergraduate degree in chemical engineering and had a strong background in thermodynamics, applied to LGO unsure of which track to choose, but he knew he wanted to confront the world’s energy challenge. When MIT Admissions suggested that he join LGO’s new nuclear track, he was intrigued by how it could further his career.

      “Since the NSE department offers opportunities ranging from energy to health care and from quantum engineering to regulatory policy, the possibilities of career tracks after graduation are countless,” he says.

      He was also inspired by the fact that, as he says, “Nuclear is one of the less-popular solutions in terms of our energy transition journey. One of the things that attracted me is that it’s not one of the most popular, but it’s one of the most useful.”

      In addition to his work at Caterpillar, Andrade connected deeply with professors. He worked closely with professors Jacopo Buongiorno and John Parsons as a research assistant, helping them develop a business model to successfully support the deployment of nuclear microreactors. After graduation, he plans to work in the clean energy sector with an eye to innovations in the nuclear energy technology space.

      His LGO classmate, Lindsey Kennington, a control systems engineer, echoes his sentiments: This is a revolutionary time for nuclear technology.

      “Before MIT, I worked on a lot of nuclear waste or nuclear weapons-related projects. All of them were fission-related. I got disillusioned because of all the bureaucracy and the regulation,” Kennington says. “However, now there are a lot of new nuclear technologies coming straight out of MIT. Commonwealth Fusion Systems, a fusion startup, represents a prime example of MIT’s close relationship to new nuclear tech. Small modular reactors are another emerging technology being developed by MIT. Exposure to these cutting-edge technologies was the main sell factor for me.”

      Kennington conducted an internship with National Grid, where she used her expertise to evaluate how existing nuclear power plants could generate hydrogen. At MIT, she studied nuclear and energy policy, which offered her additional perspective that traditional engineering classes might not have provided. Because nuclear power has long been a hot-button issue, Kennington was able to gain nuanced insight about the pathways and roadblocks to its implementation.

      “I don’t think that other engineering departments emphasize that focus on policy quite as much. [Those classes] have been one of the most enriching parts of being in the nuclear department,” she says.

      Most of all, she says, it’s a pivotal time to be part of a new, blossoming program at the forefront of clean energy, especially as fusion research grows more prevalent.

      “We’re at an inflection point,” she says. “Whether or not we figure out fusion in the next five, 10, or 20 years, people are going to be working on it — and it’s a really exciting time to not only work on the science but to actually help the funding and business side grow.”

      White puts it simply.

      “This is not your parents’ nuclear,” she says. “It’s something totally different. Our discipline is evolving so rapidly that people who have technical expertise in nuclear will have a huge advantage in this next generation.” More

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      in Energy

      A new way to assess radiation damage in reactors

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      A new method could greatly reduce the time and expense needed for certain important safety checks in nuclear power reactors. The approach could save money and increase total power output in the short run, and it might increase plants’ safe operating lifetimes in the long run.

      One of the most effective ways to control greenhouse gas emissions, many analysts argue, is to prolong the lifetimes of existing nuclear power plants. But extending these plants beyond their originally permitted operating lifetimes requires monitoring the condition of many of their critical components to ensure that damage from heat and radiation has not led, and will not lead, to unsafe cracking or embrittlement.

      Today, testing of a reactor’s stainless steel components — which make up much of the plumbing systems that prevent heat buildup, as well as many other parts — requires removing test pieces, known as coupons, of the same kind of steel that are left adjacent to the actual components so they experience the same conditions. Or, it requires the removal of a tiny piece of the actual operating component. Both approaches are done during costly shutdowns of the reactor, prolonging these scheduled outages and costing millions of dollars per day.

      Now, researchers at MIT and elsewhere have come up with a new, inexpensive, hands-off test that can produce similar information about the condition of these reactor components, with far less time required during a shutdown. The findings are reported today in the journal Acta Materiala in a paper by MIT professor of nuclear science and engineering Michael Short; Saleem Al Dajani ’19 SM ’20, who did his master’s work at MIT on this project and is now a doctoral student at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia; and 13 others at MIT and other institutions.

      The test involves aiming laser beams at the stainless steel material, which generates surface acoustic waves (SAWs) on the surface. Another set of laser beams is then used to detect and measure the frequencies of these SAWs. Tests on material aged identically to nuclear power plants showed that the waves produced a distinctive double-peaked spectral signature when the material was degraded.

      Short and Al Dajani embarked on the process in 2018, looking for a more rapid way to detect a specific kind of degradation, called spinodal decomposition, that can take place in austenitic stainless steel, which is used for components such as the 2- to 3-foot wide pipes that carry coolant water to and from the reactor core. This process can lead to embrittlement, cracking, and potential failure in the event of an emergency.

      While spinodal decomposition is not the only type of degradation that can occur in reactor components, it is a primary concern for the lifetime and sustainability of nuclear reactors, Short says.

      “We were looking for a signal that can link material embrittlement with properties we can measure, that can be used to estimate lifetimes of structural materials,” Al Dajani says.

      They decided to try a technique Short and his students and collaborators had expanded upon, called transient grating spectroscopy, or TGS, on samples of reactor materials known to have experienced spinodal decomposition as a result of their reactor-like thermal aging history. The method uses laser beams to stimulate, and then measure, SAWs on a material. The idea was that the decomposition should slow down the rate of heat flow through the material, that slowdown would be detectable by the TGS method.

      However, it turns out there was no such slowdown. “We went in with a hypothesis about what we would see, and we were wrong,” Short says.

      That’s often the way things work out in science, he says. “You go in guns blazing, looking for a certain thing, for a great reason, and you turn out to be wrong. But if you look carefully, you find other patterns in the data that reveal what nature actually has to say.”

      Instead, what showed up in the data was that, while a material would usually produce a single frequency peak for the material’s SAWs, in the degraded samples there was a splitting into two peaks.

      “It was a very clear pattern in the data,” Short recalls. “We just didn’t expect it, but it was right there screaming at us in the measurements.”

      Cast austenitic stainless steels like those used in reactor components are what’s known as duplex steels, actually a mixture of two different crystal structures in the same material by design. But while one of the two types is quite impervious to spinodal decomposition, the other is quite vulnerable to it. When the material starts to degrade, the difference shows up in the different frequency responses of the material, which is what the team found in their data.

      That finding was a total surprise, though. “Some of my current and former students didn’t believe it was happening,” Short says. “We were unable to convince our own team this was happening, with the initial statistics we had.” So, they went back and carried out further tests, which continued to strengthen the significance of the results. They reached a point where the confidence level was 99.9 percent that spinodal decomposition was indeed coincident with the wave peak separation.

      “Our discussions with those who opposed our initial hypotheses ended up taking our work to the next level,” Al Dajani says.

      The tests they did used large lab-based lasers and optical systems, so the next step, which the researchers are hard at work on, is miniaturizing the whole system into something that can be an easily portable test kit to use to check reactor components on-site, reducing the length of shutdowns. “We’re making great strides, but we still have some way to go,” he says.

      But when they achieve that next step, he says, it could make a significant difference. “Every day that your nuclear plant goes down, for a typical gigawatt-scale reactor, you lose about $2 million a day in lost electricity,” Al Dajani says, “so shortening outages is a huge thing in the industry right now.”

      He adds that the team’s goal was to find ways to enable existing plants to operate longer: “Let them be down for less time and be as safe or safer than they are right now — not cutting corners, but using smart science to get us the same information with far less effort.” And that’s what this new technique seems to offer.

      Short hopes that this could help to enable the extension of power plant operating licenses for some additional decades without compromising safety, by enabling frequent, simple and inexpensive testing of the key components. Existing, large-scale plants “generate just shy of a billion dollars in carbon-free electricity per plant each year,” he says, whereas bringing a new plant online can take more than a decade. “To bridge that gap, keeping our current nukes online is the single biggest thing we can do to fight climate change.”

      The team included researchers at MIT, Idaho National Laboratory, Manchester University and Imperial College London in the UK, Oak Ridge National Laboratory, the Electric Power Research Institute, Northeastern University, the University of California at Berkeley, and KAUST. The work was supported by the International Design Center at MIT and the Singapore University of Technology and Design, the U.S. Nuclear Regulatory Commission, and the U.S. National Science Foundation. More

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      in Energy

      Ian Hutchinson: A lifetime probing plasma, on Earth and in space

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      Ordinary folks gazing at the night sky can readily spot Earth’s close neighbors and the light of distant stars. But when Ian Hutchinson scans the cosmos, he takes in a great deal more. There is, for instance, the constant rush of plasma — highly charged ionized gases — from the sun. As this plasma flows by solid bodies such as the moon, it interacts with them electromagnetically, sometimes generating a phenomenon called an electron hole — a perturbation in the gaseous solar tide that forms a solitary, long-lived wave. Hutchinson, a professor in the MIT Department of Nuclear Science and Engineering (NSE), knows they exist because he found a way to measure them.

      “When I look up at the moon with my sweetheart, my wife of 48 years, I imagine that streaming from its dark side are electron holes that my students and I predicted, and that we then discovered,” he says. “It’s quite sentimental to me.”

      Hutchinson’s studies of these wave phenomena, summed up in a paper, “Electron holes in phase space: What they are and why they matter,” recently earned the 2022 Ronald C. Davidson Award for Plasma Physics presented by the American Physical Society’s Division of Plasma Physics.

      Measuring perturbations in plasma

      Hutchinson’s exploration of electron holes was sparked by his work over many decades in fusion energy, another branch of plasma physics. He has made many contributions to the design, operation, and experimental investigation of tokamaks — a toroidal magnetic confinement device — intended to replicate and harness the fiery thermonuclear reactions in the plasma of stars for carbon-free energy on Earth. Hutchinson took a particular interest in how to measure the plasma, notably the flow at the edges of tokamaks.

      Heat generated from fusion reactions may escape magnetic confinement and build up along these edges, leading to potential temperature spikes that impact the performance of the confinement device. Hutchinson discovered how to interpret signals from small probes to measure and track plasma velocity at the tokamak’s edge.

      “My theoretical work also showed that these probes quite likely induce electron holes,” he says. But proving this contention required experiments at resolutions in time and space beyond what tokamaks allow. That’s when Hutchinson had an important insight.

      “I realized that the phenomena we were trying to investigate can actually be measured with exquisite accuracy by satellites that travel through plasma surrounding Earth and other solid bodies,” he says. Although plasmas in space are at a much larger scale than the plasmas generated in the laboratory, measurements of these gases by a satellite is analogous “to a situation where we fly a tiny micron-sized spacecraft through the wakes of probes at the edge of tokamaks,” says Hutchinson.

      Using satellite data provided by NASA, Hutchinson set about analyzing solar plasma as it whips by the moon. “We predicted instabilities and the generation of electron holes,” he recounts. “Our theory passed with flying colors: We saw lots of holes in the wake of the moon, and few elsewhere.”

      Developing tokamaks

      Hutchinson grew up in the English midlands and attended Cambridge University, where he became “intrigued by plasma physics in a course taught by an entertaining and effective teacher,” he says.

      Hutchinson headed for doctoral studies at Australian National University on fellowship. The experience afforded him his first opportunity for research on plasma confinement. “There I was at the ends of the Earth, and I was one of very few scientists worldwide with a tokamak almost to myself,” he says. “It was a device that had risen to the top of everyone’s agenda in fusion research as something we really needed to understand.”

      His dissertation, which examined instabilities in plasma, and his hands-on experience with the device, brought him to the attention of Ronald Parker SM ’63, PhD ’67, now emeritus professor of nuclear science and engineering and electrical engineering and computer science, who was building MIT’s Alcator tokamak program.

      In 1976, Hutchinson joined this group, spending three years as a research scientist. After an interval in Britain, he returned to MIT with a faculty position in NSE, and soon, a leadership role in developing the next phase of the Institute’s fusion experiment, the Alcator-C Mod tokamak.

      “This was a major development of the high-magnetic field approach to fusion,” says Hutchinson. Powerful magnets are essential for containing the superhot plasma; the MIT group developed an experiment with a magnetic field more than 150,000 times the strength of the Earth’s magnetic field. “We were in the business of determining whether tokamaks had sufficiently good confinement to function as fusion reactors,” he says.

      Hutchinson oversaw the nearly six-year construction of the device, which was funded by the U.S. Department of Energy. He then led its operation starting in 1993, creating a national facility for experiments that drew scientists and students from around the world. At the time, it was the largest research group on campus at MIT.

      In their studies, scientists employed novel heating and sustainment techniques using radio waves and microwaves. They also discovered new methods for performing diagnostics inside the tokamak. “Alcator C-Mod demonstrated excellent confinement in a more compact and cost-effective device,” says Hutchinson. “It was unique in the world.”

      Hutchinson is proud of Alcator C-Mod’s technological achievements, including its record for highest plasma pressure for a magnetic confinement device. But this large-scale project holds even greater significance for him. “Alcator C-Mod helped beat a new path in fusion research, and has become the basis for the SPARC tokamak now under construction,” he says.

      SPARC is a compact, high-magnetic field fusion energy device under development through a collaboration between MIT’s Plasma Science and Fusion Center and startup Commonwealth Fusions Systems. Its goal is to demonstrate net energy gain from fusion, prove the viability of fusion as a source of carbon-free energy, and tip the scales in the race against climate change. A number of SPARC’s leaders are students Hutchinson taught. “This is a source of considerable satisfaction,” he says. “Some of their down-to-Earth realism comes from me, and perhaps some of their aspirations have been molded by their work with me.” 

      A new phase

      After leading Alcator C-Mod for 15 years and generating hundreds of journal articles, Hutchinson served as NSE’s department head from 2003 to 2009. He wrote the standard textbook on measuring plasmas, and has more recently written “A Student’s Guide to Numerical Methods” (2015), which evolved from a course he taught to introduce graduate students to computational problem-solving in physics and engineering.

      After this, his 40th year on the MIT faculty, Hutchinson will be stepping back from teaching. “It’s important for new generations of students to be taught by people at the pinnacle of their mental and intellectual capacity, and when you reach my age, you’re aware of the fact that you’re slowing down,” he says.

      Hutchinson’s at no loss for ways to spend his time. As a devout Christian, he speaks and writes about the relationship between religion and science, trying to help skeptics on both sides find common ground. He sings in two choral groups, and is very busy grandparenting four grandsons. For a complete change of pace, Hutchinson goes fly fishing.

      But he still has plans to explore new frontiers in plasma physics. “I’m gratified to say I still do important research,” he says. “I’ve solved most of the problems in electron holes, and now I need to say something about ion holes!” More

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      MIT scientists contribute to National Ignition Facility fusion milestone

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      On Monday, Dec. 5, at around 1 a.m., a tiny sphere of deuterium-tritium fuel surrounded by a cylindrical can of gold called a hohlraum was targeted by 192 lasers at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) in California. Over the course of billionths of a second, the lasers fired, generating X-rays inside the gold can, and imploding the sphere of fuel.

      On that morning, for the first time ever, the lasers delivered 2.1 megajoules of energy and yielded 3.15 megajoules in return, achieving a historic fusion energy gain well above 1 — a result verified by diagnostic tools developed by the MIT Plasma Science and Fusion Center (PSFC). The use of these tools and their importance was referenced by Arthur Pak, a LLNL staff scientist who spoke at a U.S. Department of Energy press event on Dec. 13 announcing the NIF’s success.

      Johan Frenje, head of the PSFC High-Energy-Density Physics division, notes that this milestone “will have profound implications for laboratory fusion research in general.”

      Since the late 1950s, researchers worldwide have pursued fusion ignition and energy gain in a laboratory, considering it one of the grand challenges of the 21st century. Ignition can only be reached 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 inertial confinement fusion, the method used at the NIF, ignition can initiate a “fuel burn propagation” into the surrounding dense and cold fuel, and when done correctly, enable fusion-energy gain.

      Frenje and his PSFC division initially designed dozens of diagnostic systems that were implemented at the NIF, including the vitally important magnetic recoil neutron spectrometer (MRS), which measures the neutron energy spectrum, the data from which fusion yield, plasma ion temperature, and spherical fuel pellet compression (“fuel areal density”) can be determined. Overseen by PSFC Research Scientist Maria Gatu Johnson since 2013, the MRS is one of two systems at the NIF relied upon to measure the absolute neutron yield from the Dec. 5 experiment because of its unique ability to accurately interpret an implosion’s neutron signals.

      “Before the announcement of this historic achievement could be made, the LLNL team wanted to wait until Maria had analyzed the MRS data to an adequate level for a fusion yield to be determined,” says Frenje.

      Response around MIT to NIF’s announcement has been enthusiastic and hopeful. “This is the kind of breakthrough that ignites the imagination,” says Vice President for Research Maria Zuber, “reminding us of the wonder of discovery and the possibilities of human ingenuity. Although we have a long, hard path ahead of us before fusion can deliver clean energy to the electrical grid, we should find much reason for optimism in today’s announcement. Innovation in science and technology holds great power and promise to address some of the world’s biggest challenges, including climate change.”

      Frenje also credits the rest of the team at the PSFC’s High-Energy-Density Physics division, the Laboratory for Laser Energetics at the University of Rochester, LLNL, and other collaborators for their support and involvement in this research, as well as the National Nuclear Security Administration of the Department of Energy, which has funded much of their work since the early 1990s. He is also proud of the number of MIT PhDs that have been generated by the High-Energy-Density Physics Division and subsequently hired by LLNL, including the experimental lead for this experiment, Alex Zylstra PhD ’15.

      “This is really a team effort,” says Frenje. “Without the scientific dialogue and the extensive know-how at the HEDP Division, the critical contributions made by the MRS system would not have happened.” More

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      in Energy

      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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      in Energy

      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