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      in Environment

      MIT entrepreneurs think globally, act locally

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      Born and raised amid the natural beauty of the Dominican Republic, Andrés Bisonó León feels a deep motivation to help solve a problem that has been threatening the Caribbean island nation’s tourism industry, its economy, and its people.

      As Bisonó León discussed with his long-time friend and mentor, the Walter M. May and A. Hazel May Professor of Mechanical Engineering (MechE) Alexander Slocum Sr., ugly mats of toxic sargassum seaweed have been encroaching on the Dominican Republic’s pristine beaches and other beaches in the Caribbean region, and public and private organizations have fought a losing battle using expensive, environmentally damaging methods to clean it up. Slocum, who was on the U.S. Department of Energy’s Deepwater Horizon team, has extensive experience with systems that operate in the ocean.

      “In the last 10 years,” says Bisonó León, now an MBA candidate in the MIT Sloan School of Management, “sargassum, a toxic seaweed invasion, has cost the Caribbean as much as $120 million a year in cleanup and has meant a 30 to 35 percent tourism reduction, affecting not only the tourism industry, but also the environment, marine life, local economies, and human health.”

      One of Bisonó León’s discussions with Slocum took place within earshot of MechE alumnus Luke Gray ’18, SM ’20, who had worked with Slocum on other projects and was at the time was about to begin his master’s program.

      “Professor Slocum and Andrés happened to be discussing the sargassum problem in Andrés’ home country,” Gray says. “A week later I was on a plane to the DR to collect sargassum samples and survey the problem in Punta Cana. When I returned, my master’s program was underway, and I already had my thesis project!”

      Gray also had started a working partnership with Bisonó León, which both say proceeded seamlessly right from the first moment.

      “I feel that Luke right away understood the magnitude of the problem and the value we could create in the Dominican Republic and across the Caribbean by teaming up,” Bisonó León says.

      Both Bisonó León and Gray also say they felt a responsibility to work toward helping the global environment.

      “All of my major projects up until now have involved machines for climate restoration and/or adaptation,” says Gray.

      The technologies Bisonó León and Gray arrived at after 18 months of R&D were designed to provide solutions both locally and globally.

      Their Littoral Collection Module (LCM) skims sargassum seaweed off the surface of the water with nets that can be mounted on any boat. The device sits across the boat, with two large hoops holding the nets open, one on each side. As the boat travels forward, it cuts through the seaweed, which flows to the sides of the vessel and through the hoops into the nets. Effective at sweeping the seaweed from the water, the device can be employed by anyone with a boat, including local fishermen whose livelihoods have been disrupted by the seaweed’s damaging effect on tourism and the local economy.

      The sargassum can then be towed out to sea, where Bisonó León’s and Gray’s second technology can come into play. By pumping the seaweed into very deep water, where it then sinks to the bottom of the ocean, the carbon in the seaweed can be sequestered. Other methods for disposing of the seaweed generally involve putting it into landfills, where it emits greenhouse gases such as methane and carbon dioxide as it breaks down. Although some seaweed can be put to other uses, including as fertilizer, sargassum has been found to contain hard-to-remove toxic substances such as arsenic and heavy metals.

      In spring 2020, Bisonó León and Gray formed a company, SOS (Sargassum Ocean Sequestration) Carbon.

      Bisonó León says he comes from a long line of entrepreneurs who often expressed much commitment to social impact. His family has been involved in several different industries, his grandfather and great uncles having opened the first cigar factory in the Dominican Republic in 1903.

      Gray says internships with startup companies and the undergraduate projects he did with Slocum developed his interest in entrepreneurship, and his involvement with the sargassum problem only reinforced that inclination. During his master’s program, he says he became “obsessed” with finding a solution.

      “Professor Slocum let me think extremely big, and so it was almost inevitable that the distillation of our two years of work would continue in some form, and starting a company happened to be the right path. My master’s experience of taking an essentially untouched problem like sargassum and then one year later designing, building, and sending 15,000 pounds of custom equipment to test for three months on a Dominican Navy ship made me realize I had discovered a recipe I could repeat — and machine design had become my core competency,” Gray says.

      During the initial research and development of their technologies, Bisonó León and Gray raised $258,000 from 20 different organizations. Between June and December 2021, they succeeded in removing 3.5 million pounds of sargassum and secured contracts with Grupo Puntacana, which operates several tourist resorts, and with other hotels such as Club Med in Punta Cana. The company subcontracts with the association of fishermen in Punta Cana, employing 15 fishermen who operate LCMs and training 35 others to join as the operation expands.

      Their success so far demonstrates “’mens et manus’ at work,” says Slocum, referring to MIT’s motto, which is Latin for “mind and hand.” “Geeks hear about a very real problem that affects very real people who have no other option for their livelihoods, and they respond by inventing a solution so elegant that it can be readily deployed by those most hurt by the problem to address the problem.

      “The team was always focused on the numbers, from physics to finance, and did not let hype or doubts deter their determination to rationally solve this huge problem.”

      Slocum says he could predict Bisonó León and Gray would work well together “because they started out as good, smart people with complementary skills whose hearts and minds were in the right place.”

      “We are working on having a global impact to reduce millions of tons of CO2 per year,” says Bisonó León. “With training from Sloan and cross-disciplinary collaborative spirit, we will be able to further expand environmental and social impact platforms much needed in the Caribbean to be able to drive real change regionally and globally.”

      “I hope SOS Carbon can serve as a model and inspire similar entrepreneurial efforts,” Gray says. More

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      in Energy

      Tuning in to invisible waves on the JET tokamak

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      Research scientist Alex Tinguely is readjusting to Cambridge and Boston.

      As a postdoc with the Plasma Science and Fusion Center (PSFC), the MIT graduate spent the last two years in Oxford, England, a city he recalls can be traversed entirely “in the time it takes to walk from MIT to Harvard.” With its ancient stone walls, cathedrals, cobblestone streets, and winding paths, that small city was his home base for a big project: JET, a tokamak that is currently the largest operating magnetic fusion energy experiment in the world.

      Located at the Culham Center for Fusion Energy (CCFE), part of the U.K. Atomic Energy Authority, this key research center of the European Fusion Program has recently announced historic success. Using a 50-50 deuterium-tritium fuel mixture for the first time since 1997, JET established a fusion power record of 10 megawatts output over five seconds. It produced 59 megajoules of fusion energy, more than doubling the 22 megajoule record it set in 1997. As a member of the JET Team, Tinguely has overseen the measurement and instrumentation systems (diagnostics) contributed by the MIT group.

      A lucky chance

      The postdoctoral opportunity arose just as Tinguely was graduating with a PhD in physics from MIT. Managed by Professor Miklos Porkolab as the principal investigator for over 20 years, this postdoctoral program has prepared multiple young researchers for careers in fusion facilities around the world. The collaborative research provided Tinguely the chance to work on a fusion device that would be adding tritium to the usual deuterium fuel.

      Fusion, the process that fuels the sun and other stars, could provide a long-term source of carbon-free power on Earth, if it can be harnessed. For decades researchers have tried to create an artificial star in a doughnut-shaped bottle, or “tokamak,” using magnetic fields to keep the turbulent plasma fuel confined and away from the walls of its container long enough for fusion to occur.

      In his graduate student days at MIT, Tinguely worked on the PSFC’s Alcator C-Mod tokamak, now decommissioned, which, like most magnetic fusion devices, used deuterium to create the plasmas for experiments. JET, since beginning operation in 1983, has done the same, later joining a small number of facilities that added tritium, a radioactive isotope of hydrogen. While this addition increases the amount of fusion, it also creates much more radiation and activation.

      Tinguely considers himself fortunate to have been placed at JET.

      “There aren’t that many operating tokamaks in the U.S. right now,” says Tinguely, “not to mention one that would be running deuterium-tritium (DT), which hasn’t been run for over 20 years, and which would be making some really important measurements. I got a very lucky spot where I was an MIT postdoc, but I lived in Oxford, working on a very international project.”

      Strumming magnetic field lines

      The measurements that interest Tinguely are of low-frequency electromagnetic waves in tokamak plasmas. Tinguely uses an antenna diagnostic developed by MIT, EPFL Swiss Plasma Center, and CCFE to probe the so-called Alfvén eigenmodes when they are stable, before the energetic alpha particles produced by DT fusion plasmas can drive them toward instability.

      What makes MIT’s “Alfvén Eigenmode Active Diagnostic” essential is that without it researchers cannot see, or measure, stable eigenmodes. Unstable modes show up clearly as magnetic fluctuations in the data, but stable waves are invisible without prompting from the antenna. These measurements help researchers understand the physics of Alfvén waves and their potential for degrading fusion performance, providing insights that will be increasingly important for future DT fusion devices.

      Tinguely likens the diagnostic to fingers on guitar strings.

      “The magnetic field lines in the tokamak are like guitar strings. If you have nothing to give energy to the strings — or give energy to the waves of the magnetic field lines — they just sit there, they don’t do anything. The energetic plasma particles can essentially ‘play the guitar strings,’ strum the magnetic field lines of the plasma, and that’s when you can see the waves in your plasma. But if the energetic particle drive of the waves is not strong enough you won’t see them, so you need to come along and ‘pluck the strings’ with our antenna. And that’s how you learn some information about the waves.”

      Much of Tinguely’s experience on JET took place during the Covid-19 pandemic, when off-site operation and analysis were the norm. However, because the MIT diagnostic needed to be physically turned on and off, someone from Tinguely’s team needed to be on site twice a day, a routine that became even less convenient when tritium was introduced.

      “When you have deuterium and tritium, you produce a lot of neutrons. So, some of the buildings became off-limits during operation, which meant they had to be turned on really early in the morning, like 6:30 a.m., and then turned off very late at night, around 10:30 p.m.”

      Looking to the future

      Now a research scientist at the PSFC, Tinguely continues to work at JET remotely. He sometimes wishes he could again ride that train from Oxford to Culham — which he fondly remembers for its clean, comfortable efficiency — to see work colleagues and to visit local friends. The life he created for himself in England included practice and performance with the 125-year-old Oxford Bach Choir, as well as weekly dinner service at The Gatehouse, a facility that offers free support for the local homeless and low-income communities.

      “Being back is exciting too,” he says. “It’s fun to see how things have changed, how people and projects have grown, what new opportunities have arrived.”

      He refers specifically to a project that is beginning to take up more of his time: SPARC, the tokamak the PSFC supports in collaboration with Commonwealth Fusion Systems. Designed to use deuterium-tritium to make net fusion gains, SPARC will be able to use the latest research on JET to advantage. Tinguely is already exploring how his expertise with Alfvén eigenmodes can support the experiment.

      “I actually had an opportunity to do my PhD — or DPhil as they would call it — at Oxford University, but I went to MIT for grad school instead,” Tinguely reveals. “So, this is almost like closure, in a sense. I got to have my Oxford experience in the end, just in a different way, and have the MIT experience too.”

      He adds, “And I see myself being here at MIT for some time.” More

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      in Environment

      Courtney Lesoon and Elizabeth Yarina win Fulbright-Hays Scholarships

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      Two MIT doctoral students in the MIT School of Architecture and Planning have received the prestigious Fulbright-Hays Scholarship for Doctoral Dissertation Research Award. Courtney Lesoon and Elizabeth “Lizzie” Yarina are the first awardees from MIT in more than a decade.

      The fellowship provides opportunities for doctoral students to engage in full-time dissertation research abroad. The program, funded by the U.S. Department of Education, is designed to contribute to the development and improvement of the study of modern foreign languages and area studies. Applicants anticipate pursuing a teaching career in the United States following completion of their dissertation. There were 138 individuals from 47 institutions named scholars for the 2021 cycle.

      Courtney Lesoon

      Lesoon is a doctoral candidate in the Aga Khan Program for Islamic Architecture, in the History, Theory and Criticism Section of the Department of Architecture. Lesoon earned her BA from College of the Holy Cross and was a 2012-13 Fulbright U.S. Student grantee to the United Arab Emirates, where her research concerned contemporary art and emerging cultural institutions. Her dissertation is titled “Spatializing Ahl al-ʿIlm: Learning and the Rise of the Early Islamic City.” Losoon’s fieldwork will be done in Morocco, Egypt, and Turkey.

      “Courtney’s project presents an innovative idea that has not, to my knowledge, been investigated before,” says Nasser Rabbat, professor and director of the MIT Aga Khan Program. “How did the emergence and evolution of a particularly Islamic learning system affect the development of the city in the early Islamic period? Her work enriches the thinking about premodern urbanism and education everywhere by theorizing the intricate relationship between traveling, learning, and the city.”

      “I’ll be working in different manuscripts collections in Morocco, Egypt, and Turkey to investigate where and how scholars were learning inside of the early Islamic city before the formal institutionalization of higher education,” says Lesoon. “I’m interested in how learning — as a set of social practices — informed urban life. My project speaks to two different fields; Islamic urbanism and Islamic intellectual history. I’m really excited about my time on Fulbright-Hays; it will be a really fruitful time for my research and writing.”

      Before arriving at MIT, Lesoon worked as a research assistant in the Art of the Middle East Department at the Los Angeles County Museum of Art. Recently, she was awarded the 2021 Margaret B. Ševčenko Prize for “the best unpublished essay written by a junior scholar” for her paper “The Sphero-conical as Apothecary Vessel: An Argument for Dedicated Use.” Lesoon earned her MA from the University of Michigan at Ann Arbor, where her thesis investigated an 18th-century “Damascus Room” and its acquisition as a collected interior in the United States.

      Lizzie Yarina

      Yarina is a doctoral candidate in the MIT Department of Urban Studies and Planning (DUSP) and a research fellow at the MIT Norman B. Leventhal Center for Advanced Urbanism. She is presently co-editing a volume on the relationship between climate models and the built environment with a multidisciplinary team of editors and contributors. Yarina was a research scientist at the MIT Urban Risk Lab, where she was part of a team examining alternatives to the Federal Emergency Management Agency’s post-disaster housing systems; she also conducted research on disaster preparedness in Japan. Her award supports her doctoral research under the title “Modeling the Mekong: Climate Adaptation Imaginaries in Delta Regions,” which will include fieldwork in Vietnam, the Netherlands, Thailand, and Cambodia.

      “Lizzie’s research brings together three dimensions critical to global well-being and sustainability: adapting to the inevitability of changing ecosystems wrought by the climate crisis; questioning the equity, appropriateness, and relationality of adaptation planning models spanning the global North and the global South; and understanding how to develop durable and just climate futures,” says Christopher Zegras, professor of mobility and urban planning and department head for DUSP. “Her work will be an important contribution toward the long-term health of our planet and of communities working to justly adapt to climate change.”

      Previously, Yarina was awarded a U.S. Scholarship Fulbright to New Zealand to research spatial mapping and policy implications of Pacific Islander migration to New Zealand.

      “My dissertation project looks at climate adaptation planning in delta regions,” she says. “My focus is on Vietnam’s Mekong River Delta, but I’m also looking at how models that are used in delta adaptation planning move between different deltas, including the Netherlands Rhine Delta and the Mississippi Delta.”

      Working on her masters at MIT, Yarina had a teaching fellowship in Singapore, where she conducted research on climate adaptation plans in four major cities in Southeast Asia.

      “Through that process I learned about the role of Dutch experts and Dutch models in shaping how climate adaptation planning was taking place in Southeast Asia,” she says. “This project expands on that work from looking at a single city to examining a regional plan at the scale of a delta.”

      Yarina holds a joint masters in architecture and masters of city planning from MIT, and a BS in architecture from the University of Michigan. More

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      in Energy

      Seeing the plasma edge of fusion experiments in new ways with artificial intelligence

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      To make fusion energy a viable resource for the world’s energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

      Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT’s Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces — factors that impact fusion reactor designs.

      To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma’s behavior. However, “first principles” simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop “reduced” computer models that run much faster, but with quantified levels of accuracy.

      For decades, tokamak physicists have regularly used a reduced “two-fluid theory” rather than higher-fidelity models to simulate boundary plasmas in experiment, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

      “A successful theory is supposed to predict what you’re going to observe,” explains Mathews, “for example, the temperature, the density, the electric potential, the flows. And it’s the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.”

      In the first paper, published in Physical Review E, Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even to noisy pressure measurements.

      In the second paper, published in Physics of Plasmas, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult — if not impossible — to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid model’s predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, “one should check every connection between every variable,” says Mathews.

      Mathews’ advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. “This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. I’m excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.”

      These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

      “Abhi’s work is a major achievement with the potential for broad application,” he says. “For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.”

      Mathews sees exciting research ahead.

      “Translating these techniques into fusion experiments for real edge plasmas is one goal we have in sight, and work is currently underway,” he says. “But this is just the beginning.”

      Mathews was supported in this work by the Manson Benedict Fellowship, Natural Sciences and Engineering Research Council of Canada, and U.S. Department of Energy Office of Science under the Fusion Energy Sciences program.​ More

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      in Security

      Meet the 2021-22 Accenture Fellows

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      Launched in October of 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology come together to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.

      This year’s Accenture Fellows work across disciplines including robotics, manufacturing, artificial intelligence, and biomedicine. Their research covers a wide array of subjects, including: advancing manufacturing through computational design, with the potential to benefit global vaccine production; designing low-energy robotics for both consumer electronics and the aerospace industry; developing robotics and machine learning systems that may aid the elderly in their homes; and creating ingestible biomedical devices that can help gather medical data from inside a patient’s body.

      Student nominations from each unit within the School of Engineering, as well as from the four other MIT schools and the MIT Schwarzman College of Computing, were invited as part of the application process. Five exceptional students were selected as fellows in the initiative’s second year.

      Xinming (Lily) Liu is a PhD student in operations research at MIT Sloan School of Management. Her work is focused on behavioral and data-driven operations for social good, incorporating human behaviors into traditional optimization models, designing incentives, and analyzing real-world data. Her current research looks at the convergence of social media, digital platforms, and agriculture, with particular attention to expanding technological equity and economic opportunity in developing countries. Liu earned her BS from Cornell University, with a double major in operations research and computer science.

      Caris Moses is a PhD student in electrical engineering and computer science specializing inartificial intelligence. Moses’ research focuses on using machine learning, optimization, and electromechanical engineering to build robotics systems that are robust, flexible, intelligent, and can learn on the job. The technology she is developing holds promise for industries including flexible, small-batch manufacturing; robots to assist the elderly in their households; and warehouse management and fulfillment. Moses earned her BS in mechanical engineering from Cornell University and her MS in computer science from Northeastern University.

      Sergio Rodriguez Aponte is a PhD student in biological engineering. He is working on the convergence of computational design and manufacturing practices, which have the potential to impact industries such as biopharmaceuticals, food, and wellness/nutrition. His current research aims to develop strategies for applying computational tools, such as multiscale modeling and machine learning, to the design and production of manufacturable and accessible vaccine candidates that could eventually be available globally. Rodriguez Aponte earned his BS in industrial biotechnology from the University of Puerto Rico at Mayaguez.

      Soumya Sudhakar SM ’20 is a PhD student in aeronautics and astronautics. Her work is focused on theco-design of new algorithms and integrated circuits for autonomous low-energy robotics that could have novel applications in aerospace and consumer electronics. Her contributions bring together the emerging robotics industry, integrated circuits industry, aerospace industry, and consumer electronics industry. Sudhakar earned her BSE in mechanical and aerospace engineering from Princeton University and her MS in aeronautics and astronautics from MIT.

      So-Yoon Yang is a PhD student in electrical engineering and computer science. Her work on the development of low-power, wireless, ingestible biomedical devices for health care is at the intersection of the medical device, integrated circuit, artificial intelligence, and pharmaceutical fields. Currently, the majority of wireless biomedical devices can only provide a limited range of medical data measured from outside the body. Ingestible devices hold promise for the next generation of personal health care because they do not require surgical implantation, can be useful for detecting physiological and pathophysiological signals, and can also function as therapeutic alternatives when treatment cannot be done externally. Yang earned her BS in electrical and computer engineering from Seoul National University in South Korea and her MS in electrical engineering from Caltech. More

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      in Energy

      Helping to make nuclear fusion a reality

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      Up until she served in the Peace Corps in Malawi, Rachel Bielajew was open to a career reboot. Having studied nuclear engineering as an undergraduate at the University of Michigan at Ann Arbor, graduate school had been on her mind. But seeing the drastic impacts of climate change play out in real-time in Malawi — the lives of the country’s subsistence farmers swing wildly, depending on the rains — convinced Bielajew of the importance of nuclear engineering. Bielajew was struck that her high school students in the small town of Chisenga had a shaky understanding of math, but universally understood global warming. “The concept of the changing world due to human impact was evident, and they could see it,” Bielajew says.

      Bielajew was looking to work on solutions that could positively impact global problems and feed her love of physics. Nuclear engineering, especially the study of fusion as a carbon-free energy source, checked off both boxes. Bielajew is now a fourth-year doctoral candidate in the Department of Nuclear Science and Engineering (NSE). She researches magnetic confinement fusion in the Plasma Science and Fusion Center (PSFC) with Professor Anne White.

      Researching fusion’s big challenge

      You need to confine plasma effectively in order to generate the extremely high temperatures (100 million degrees Celsius) fusion needs, without melting the walls of the tokamak, the device that hosts these reactions. Magnets can do the job, but “plasmas are weird, they behave strangely and are challenging to understand,” Bielajew says. Small instabilities in plasma can coalesce into fluctuating turbulence that can drive heat and particles out of the machine.

      In high-confinement mode, the edges of the plasma have less tolerance for such unruly behavior. “The turbulence gets damped out and sheared apart at the edge,” Bielajew says. This might seem like a good thing, but high-confinement plasmas have their own challenges. They are so tightly bound that they create edge-localized modes (ELMs), bursts of damaging particles and energy, that can be extremely damaging to the machine.

      The questions Bielajew is looking to answer: How do we get high confinement without ELMs? How do turbulence and transport play a role in plasmas? “We do not fully understand turbulence, even though we have studied it for a long time,” Bielajew says, “It is a big and important problem to solve for fusion to be a reality. I like that challenge,” Bielajew adds.

      A love of science

      Confronting such challenges head-on has been part of Bielajew’s toolkit since she was a child growing up in Ann Arbor, Michigan. Her father, Alex Bielajew, is a professor of nuclear engineering at the University of Michigan, and Bielajew’s mother also pursued graduate studies.

      Bielajew’s parents encouraged her to follow her own path and she found it led to her father’s chosen profession: nuclear engineering. Once she decided to pursue research in fusion, MIT stood out as a school she could set her sights on. “I knew that MIT had an extensive program in fusion and a lot of faculty in the field,” Bielajew says. The mechanics of the application were challenging: Chisenga had limited internet access, so Bielajew had to ride on the back of a pickup truck to meet a friend in a city a few hours away and use his phone as a hotspot to send the documents.

      A similar tenacity has surfaced in Bielajew’s approach to research during the Covid-19 pandemic. Working off a blueprint, Bielajew built the Correlation Cyclotron Emission Diagnostic, which measures turbulent electron temperature fluctuations. Through a collaboration, Bielajew conducts her plasma research at the ASDEX Upgrade tokamak in Germany. Traditionally, Bielajew would ship the diagnostic to Germany, follow and install it, and conduct the research in person. The pandemic threw a wrench in the plans, so Bielajew shipped the diagnostic and relied on team members to install it. She Zooms into the control room and trusts others to run the plasma experiments.

      DEI advocate

      Bielajew is very hands-on with another endeavor: improving diversity, equity, and inclusion (DEI) in her own backyard. Having grown up with parental encouragement and in an environment that never doubted her place as a woman in engineering, Bielajew realizes not everyone has the same opportunities. “I wish that the world was in a place where all I had to do was care about my research, but it’s not,” Bielajew says. While science can solve many problems, more fundamental ones about equity need humans to act in specific ways, she points out. “I want to see more women represented, more people of color. Everyone needs a voice in building a better world,” Bielajew says.

      To get there, Bielajew co-launched NSE’s Graduate Application Assistance Program, which connects underrepresented student applicants with NSE mentors. She has been the DEI officer with NSE’s student group, ANS, and is very involved in the department’s DEI committee.

      As for future research, Bielajew hopes to concentrate on the experiments that make her question existing paradigms about plasmas under high confinement. Bielajew has registered more head-scratching “hmm” moments than “a-ha” ones. Measurements from her experiments drive the need for more intensive study.

      Bielajew’s dogs, Dobby and Winky, keep her company through it all. They came home with her from Malawi. More

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      in Energy

      Radio-frequency wave scattering improves fusion simulations

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      In the quest for fusion energy, understanding how radio-frequency (RF) waves travel (or “propagate”) in the turbulent interior of a fusion furnace is crucial to maintaining an efficient, continuously operating power plant. Transmitted by an antenna in the doughnut-shaped vacuum chamber common to magnetic confinement fusion devices called tokamaks, RF waves heat the plasma fuel and drive its current around the toroidal interior. The efficiency of this process can be affected by how the wave’s trajectory is altered (or “scattered”) by conditions within the chamber.

      Researchers have tried to study these RF processes using computer simulations to match the experimental conditions. A good match would validate the computer model, and raise confidence in using it to explore new physics and design future RF antennas that perform efficiently. While the simulations can accurately calculate how much total current is driven by RF waves, they do a poor job at predicting where exactly in the plasma this current is produced.

      Now, in a paper published in the Journal of Plasma Physics, MIT researchers suggest that the models for RF wave propagation used for these simulations have not properly taken into account the way these waves are scattered as they encounter dense, turbulent filaments present in the edge of the plasma known as the “scrape-off layer” (SOL).

      Bodhi Biswas, a graduate student at the Plasma Science and Fusion Center (PSFC) under the direction of Senior Research Scientist Paul Bonoli, School of Engineering Distinguished Professor of Engineering Anne White, and Principal Research Scientist Abhay Ram, who is the paper’s lead author. Ram compares the scattering that occurs in this situation to a wave of water hitting a lily pad: “The wave crashing with the lily pad will excite a secondary, scattered wave that makes circular ripples traveling outward from the plant. The incoming wave has transferred energy to the scattered wave. Some of this energy is reflected backwards (in relation to the incoming wave), some travels forwards, and some is deflected to the side. The specifics all depend on the particular attributes of the wave, the water, and the lily pad. In our case, the lily pad is the plasma filament.”

      Until now, researchers have not properly taken these filaments and the scattering they provoke into consideration when modeling the turbulence inside a tokamak, leading to an underestimation of wave scattering. Using data from PSFC tokamak Alcator C-Mod, Biswas shows that using the new method of modeling RF-wave scattering from SOL turbulence provides results considerably different from older models, and a much better match to experiments. Notably, the “lower-hybrid” wave spectrum, crucial to driving plasma current in a steady-state tokamak, appears to scatter asymmetrically, an important effect not accounted for in previous models.

      Biswas’s advisor Paul Bonoli is well acquainted with traditional “ray-tracing” models, which evaluate a wave trajectory by dividing it into a series of rays. He has used this model, with its limitations, for decades in his own research to understand plasma behavior. Bonoli says he is pleased that “the research results in Bodhi’s doctoral thesis have refocused attention on the profound effect that edge turbulence can have on the propagation and absorption of radio-frequency power.”

      Although ray-tracing treatments of scattering do not fully capture all the wave physics, a “full-wave” model that does would be prohibitively expensive. To solve the problem economically, Biswas splits his analysis into two parts: (1) using ray tracing to model the trajectory of the wave in the tokamak assuming no turbulence, while (2) modifying this ray-trajectory with the new scattering model that accounts for the turbulent plasma filaments.

      “This scattering model is a full-wave model, but computed over a small region and in a simplified geometry so that it is very quick to do,” says Biswas. “The result is a ray-tracing model that, for the first time, accounts for full-wave scattering physics.”

      Biswas notes that this model bridges the gap between simple scattering models that fail to match experiment and full-wave models that are prohibitively expensive, providing reasonable accuracy at low cost.

      “Our results suggest scattering is an important effect, and that it must be taken into account when designing future RF antennas. The low cost of our scattering model makes this very doable.”

      “This is exciting progress,” says Syun’ichi Shiraiwa, staff research physicist at the Princeton Plasma Physics Laboratory. “I believe that Bodhi’s work provides a clear path to the end of a long tunnel we have been in. His work not only demonstrates that the wave scattering, once accurately accounted for, can explain the experimental results, but also answers a puzzling question: why previous scattering models were incomplete, and their results unsatisfying.”

      Work is now underway to apply this model to more plasmas from Alcator C-Mod and other tokamaks. Biswas believes that this new model will be particularly applicable to high-density tokamak plasmas, for which the standard ray-tracing model has been noticeably inaccurate. He is also excited that the model could be validated by DIII-D National Fusion Facility, a fusion experiment on which the PSFC collaborates.

      “The DIII-D tokamak will soon be capable of launching lower hybrid waves and measuring its electric field in the scrape-off layer. These measurements could provide direct evidence of the asymmetric scattering effect predicted by our model.” More

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      in Security

      Saving seaweed with machine learning

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      Last year, Charlene Xia ’17, SM ’20 found herself at a crossroads. She was finishing up her master’s degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program.

      “I had two thoughts: I’m either going to get a PhD to work on a project that protects our planet, or I’m going to start a restaurant,” recalls Xia.

      Xia poured over her extensive cookbook collection, researching international cuisines as she anxiously awaited word about her graduate school applications. She even looked into the cost of a food truck permit in the Boston area. Just as she started hatching plans to open a plant-based skewer restaurant, Xia received word that she had been accepted into the mechanical engineering graduate program at MIT.

      Shortly after starting her doctoral studies, Xia’s advisor, Professor David Wallace, approached her with an interesting opportunity. MathWorks, a software company known for developing the MATLAB computing platform, had announced a new seed funding program in MIT’s Department of Mechanical Engineering. The program encouraged collaborative research projects focused on the health of the planet.

      “I saw this as a super-fun opportunity to combine my passion for food, my technical expertise in ocean engineering, and my interest in sustainably helping our planet,” says Xia.

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      From MIT Mechanical Engineering: “Saving Seaweed with Machine Learning”

      Wallace knew Xia would be up to the task of taking an interdisciplinary approach to solve an issue related to the health of the planet. “Charlene is a remarkable student with extraordinary talent and deep thoughtfulness. She is pretty much fearless, embracing challenges in almost any domain with the well-founded belief that, with effort, she will become a master,” says Wallace.

      Alongside Wallace and Associate Professor Stefanie Mueller, Xia proposed a project to predict and prevent the spread of diseases in aquaculture. The team focused on seaweed farms in particular.

      Already popular in East Asian cuisines, seaweed holds tremendous potential as a sustainable food source for the world’s ever-growing population. In addition to its nutritive value, seaweed combats various environmental threats. It helps fight climate change by absorbing excess carbon dioxide in the atmosphere, and can also absorb fertilizer run-off, keeping coasts cleaner.

      As with so much of marine life, seaweed is threatened by the very thing it helps mitigate against: climate change. Climate stressors like warm temperatures or minimal sunlight encourage the growth of harmful bacteria such as ice-ice disease. Within days, entire seaweed farms are decimated by unchecked bacterial growth.

      To solve this problem, Xia turned to the microbiota present in these seaweed farms as a predictive indicator of any threat to the seaweed or livestock. “Our project is to develop a low-cost device that can detect and prevent diseases before they affect seaweed or livestock by monitoring the microbiome of the environment,” says Xia.

      The team pairs old technology with the latest in computing. Using a submersible digital holographic microscope, they take a 2D image. They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

      “Using a machine learning network, you can take a 2D image and reconstruct it almost in real time to get an idea of what the microbiome looks like in a 3D space,” says Xia.

      The software can be run in a small Raspberry Pi that could be attached to the holographic microscope. To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

      In that work, under the guidance of Professor Allan Adams and Professor Joseph Paradiso in the Media Lab, Xia focused on developing small underwater communication devices that can relay data about the ocean back to researchers. Rather than the usual $4,000, these devices were designed to cost less than $100, helping lower the cost barrier for those interested in uncovering the many mysteries of our oceans. The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

      By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

      “It’s almost like having the ‘internet of things’ underwater,” adds Xia. “I’m developing this whole underwater camera system alongside the wireless communication I developed that can give me the data while I’m sitting on dry land.”

      Armed with these data about the microbiome, Xia and her team can detect whether or not a disease is about to strike and jeopardize seaweed or livestock before it is too late.

      While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prompt people to rethink how they consider food production in general.

      “We should think about farming and food production in terms of the entire ecosystem,” she says. “My meta-goal for this project would be to get people to think about food production in a more holistic and natural way.” More