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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.

      Play video

      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

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      in Energy

      Crossing disciplines, adding fresh eyes to nuclear engineering

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      Sometimes patterns repeat in nature. Spirals appear in sunflowers and hurricanes. Branches occur in veins and lightning. Limiao Zhang, a doctoral student in MIT’s Department of Nuclear Science and Engineering, has found another similarity: between street traffic and boiling water, with implications for preventing nuclear meltdowns.

      Growing up in China, Zhang enjoyed watching her father repair things around the house. He couldn’t fulfill his dream of becoming an engineer, instead joining the police force, but Zhang did have that opportunity and studied mechanical engineering at Three Gorges University. Being one of four girls among about 50 boys in the major didn’t discourage her. “My father always told me girls can do anything,” she says. She graduated at the top of her class.

      In college, she and a team of classmates won a national engineering competition. They designed and built a model of a carousel powered by solar, hydroelectric, and pedal power. One judge asked how long the system could operate safely. “I didn’t have a perfect answer,” she recalls. She realized that engineering means designing products that not only function, but are resilient. So for her master’s degree, at Beihang University, she turned to industrial engineering and analyzed the reliability of critical infrastructure, in particular traffic networks.

      “Among all the critical infrastructures, nuclear power plants are quite special,” Zhang says. “Although one can provide very enormous carbon-free energy, once it fails, it can cause catastrophic results.” So she decided to switch fields again and study nuclear engineering. At the time she had no nuclear background, and hadn’t studied in the United States, but “I tried to step out of my comfort zone,” she says. “I just applied and MIT welcomed me.” Her supervisor, Matteo Bucci, and her classmates explained the basics of fission reactions as she adjusted to the new material, language, and environment. She doubted herself — “my friend told me, ‘I saw clouds above your head’” — but she passed her first-year courses and published her first paper soon afterward.

      Much of the work in Bucci’s lab deals with what’s called the boiling crisis. In many applications, such as nuclear plants and powerful computers, water cools things. When a hot surface boils water, bubbles cling to the surface before rising, but if too many form, they merge into a layer of vapor that insulates the surface. The heat has nowhere to go — a boiling crisis.

      Bucci invited Zhang into his lab in part because she saw a connection between traffic and heat transfer. The data plots of both phenomena look surprisingly similar. “The mathematical tools she had developed for the study of traffic jams were a completely different way of looking into our problem” Bucci says, “by using something which is intuitively not connected.”

      One can view bubbles as cars. The more there are, the more they interfere with each other. People studying boiling had focused on the physics of individual bubbles. Zhang instead uses statistical physics to analyze collective patterns of behavior. “She brings a different set of skills, a different set of knowledge, to our research,” says Guanyu Su, a postdoc in the lab. “That’s very refreshing.”

      In her first paper on the boiling crisis, published in Physical Review Letters, Zhang used theory and simulations to identify scale-free behavior in boiling: just as in traffic, the same patterns appear whether zoomed in or out, in terms of space or time. Both small and large bubbles matter. Using this insight, the team found certain physical parameters that could predict a boiling crisis. Zhang’s mathematical tools both explain experimental data and suggest new experiments to try. For a second paper, the team collected more data and found ways to predict the boiling crisis in a wider variety of conditions.

      Zhang’s thesis and third paper, both in progress, propose a universal law for explaining the crisis. “She translated the mechanism into a physical law, like F=ma or E=mc2,” Bucci says. “She came up with an equally simple equation.” Zhang says she’s learned a lot from colleagues in the department who are pioneering new nuclear reactors or other technologies, “but for my own work, I try to get down to the very basics of a phenomenon.”

      Bucci describes Zhang as determined, open-minded, and commendably self-critical. Su says she’s careful, optimistic, and courageous. “If I imagine going from heat transfer to city planning, that would be almost impossible for me,” he says. “She has a strong mind.” Last year, Zhang gave birth to a boy, whom she’s raising on her own as she does her research. (Her husband is stuck in China during the pandemic.) “This, to me,” Bucci says, “is almost superhuman.”

      Zhang will graduate at the end of the year, and has started looking for jobs back in China. She wants to continue in the energy field, though maybe not nuclear. “I will use my interdisciplinary knowledge,” she says. “I hope I can design safer and more efficient and more reliable systems to provide energy for our society.” More

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      in Environment

      Mitigating hazards with vulnerability in mind

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      From tropical storms to landslides, the form and frequency of natural hazards vary widely. But the feelings of vulnerability they can provoke are universal.

      Growing up in hazard-prone cities, Ipek Bensu Manav, a civil and environmental engineering PhD candidate with the MIT Concrete Sustainability Hub (CSHub), noticed that this vulnerability was always at the periphery. Today, she’s studying vulnerability, in both its engineering and social dimensions, with the aim of promoting more hazard-resilient communities.

      Her research at CSHub has taken her across the country to attend impactful conferences and allowed her to engage with prominent experts and decision-makers in the realm of resilience. But more fundamentally, it has also taken her beyond the conventional bounds of engineering, reshaping her understanding of the practice.

      From her time in Miami, Florida, and Istanbul, Turkey, Manav is no stranger to natural hazards. Istanbul, which suffered a devastating earthquake in 1999, is predicted to experience an equally violent tremor in the near future, while Miami ranks among the top cities in the U.S. in terms of natural disaster risk due to its vulnerability to hurricanes.

      “Growing up in Miami, I’d always hear about hurricane season on the news,” recounts Manav, “While in Istanbul there was a constant fear about the next big earthquake. Losing people and [witnessing] those kinds of events instilled in me a desire to tame nature.”

      It was this desire to “push the bounds of what is possible” — and to protect lives in the process — that motivated Manav to study civil engineering at Boğaziçi University. Her studies there affirmed her belief in the formidable power of engineering to “outsmart nature.”

      This, in part, led her to continue her studies at MIT CSHub — a team of interdisciplinary researchers who study how to achieve resilient and sustainable infrastructure. Her role at CSHub has given her the opportunity to study resilience in depth. It has also challenged her understanding of natural disasters — and whether they are “natural” at all.

      “Over the past few decades, some policy choices have increased the risk of experiencing disasters,” explains Manav. “An increasingly popular sentiment among resilience researchers is that natural disasters are not ‘natural,’ but are actually man-made. At CSHub we believe there is an opportunity to do better with the growing knowledge and engineering and policy research.”

      As a part of the CSHub portfolio, Manav’s research looks not just at resilient engineering, but the engineering of resilient communities.

      Her work draws on a metric developed at CSHub known as city texture, which is a measurement of the rectilinearity of a city’s layout. City texture, Manav and her colleagues have found, is a versatile and informative measurement. By capturing a city’s order or disorder, it can predict variations in wind flow — variations currently too computationally intensive for most cities to easily render.  

      Manav has derived this metric for her native South Florida. A city texture analysis she conducted there found that numerous census tracts could experience wind speeds 50 percent greater than currently predicted. Mitigating these wind variations could lead to some $697 million in savings annually.

      Such enormous hazard losses and the growing threat of climate change have presented her with a new understanding of engineering.

      “With resilience and climate change at the forefront of engineering, the focus has shifted,” she explains, “from defying limits and building impressive structures to making structures that adapt to the changing environment around us.”

      Witnessing this shift has reoriented her relationship with engineering. Rather than viewing it as a distinct science, she has begun to place it in its broader social and political context — and to recognize how those social and political dynamics often determine engineering outcomes.

      “When I started grad school, I often felt ‘Oh this is an engineering problem. I can engineer a solution’,” recounts Manav. “But as I’ve read more about resilience, I’ve realized that it’s just as much a concern of politics and policy as it is of engineering.”

      She attributes her awareness of policy to MIT CSHub’s collaboration with the Portland Cement Association and the Ready Mixed Concrete Research & Education Foundation. The commitment of the concrete and cement industries to resilient construction has exposed her to the myriad policies that dictate the resilience of communities.

      “Spending time with our partners made me realize how much of a policy issue [resilience] is,” she explains. “And working with them has provided me with a seat at the table with the people engaged in resilience.”

      Opportunities for engagement have been plentiful. She has attended numerous conferences and met with leaders in the realm of sustainability and resilience, including the International Code Council (ICC), Smart Home America, and Strengthen Alabama Homes.

      Some opportunities have proven particularly fortuitous. When attending a presentation hosted by the ICC and the National Association for the Advancement of Colored People (NAACP) that highlighted people of color working on building codes, Manav felt inspired to reach out to the presenters. Soon after, she found herself collaborating with them on a policy report on resilience in communities of color.

      “For me, it was a shifting point, going from prophesizing about what we could be doing, to observing what is being done. It was a very humbling experience,” she says. “Having worked in this lab made me feel more comfortable stepping outside of my comfort zone and reaching out.”

      Manav credits this growing confidence to her mentorship at CSHub. More than just providing support, CSHub Co-director Randy Kirchain has routinely challenged her and inspired further growth.

      “There have been countless times that I’ve reached out to him because I was feeling unsure of myself or my ideas,” says Manav. “And he’s offered clarity and assurance.”

      Before her first conference, she recalls Kirchain staying in the office well into the evening to help her practice and hone her presentation. He’s also advocated for her on research projects to ensure that her insight is included and that she receives the credit she deserves. But most of all, he’s been a great person to work with.

      “Randy is a lighthearted, funny, and honest person to be around,” recounts Manav. “He builds in me the confidence to dive straight into whatever task I’m tackling.”

      That current task is related to equity. Inspired by her conversations with members of the NAACP, Manav has introduced a new dimension to her research — social vulnerability.

      In contrast to place vulnerability, which captures the geographical susceptibility to hazards, social vulnerability captures the extent to which residents have the resources to respond to and recover from hazard events. Household income could act as a proxy for these resources, and the spread of household income across geographies and demographics can help derive metrics of place and social vulnerability. And these metrics matter.

      “Selecting different metrics favors different people when distributing hazard mitigation and recovery funds,” explains Manav. “If we’re looking at just the dollar value of losses, then wealthy households with more valuable properties disproportionally benefit. But, conversely, if we look at losses as a percentage of income, we’re going to prioritize low-income households that might not necessarily have the resources to recover.”

      Manav has incorporated metrics of social vulnerability into her city texture loss estimations. The resulting approach could predict unmitigated damage, estimate subsequent hazard losses, and measure the disparate impact of those losses on low-income and socially vulnerable communities.

      Her hope is that this streamlined approach could change how funds are disbursed and give communities the tools to solve the entwined challenges of climate change and equity.

      The city texture work Manav has adopted is quite different from the gravity-defying engineering that drew her to the field. But she’s found that it is often more pragmatic and impactful.

      Rather than mastering the elements, she’s learning how to adapt to them and help others do the same. Solutions to climate change, she’s discovered, demand the collaboration of numerous parties — as well as a willingness to confront one’s own vulnerabilities and make the decision to reach out.  More

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      in Energy

      The boiling crisis — and how to avoid it

      by

      It’s rare for a pre-teen to become enamored with thermodynamics, but those consumed by such a passion may consider themselves lucky to end up at a place like MIT. Madhumitha Ravichandran certainly does. A PhD student in Nuclear Science and Engineering (NSE), Ravichandran first encountered the laws of thermodynamics as a middle school student in Chennai, India. “They made complete sense to me,” she says. “While looking at the refrigerator at home, I wondered if I might someday build energy systems that utilized these same principles. That’s how it started, and I’ve sustained that interest ever since.”

      She’s now drawing on her knowledge of thermodynamics in research carried out in the laboratory of NSE Assistant Professor Matteo Bucci, her doctoral supervisor. Ravichandran and Bucci are gaining key insights into the “boiling crisis” — a problem that has long saddled the energy industry.

      Ravichandran was well prepared for this work by the time she arrived at MIT in 2017. As an undergraduate at India’s Sastra University, she pursued research on “two-phase flows,” examining the transitions water undergoes between its liquid and gaseous forms. She continued to study droplet evaporation and related phenomena during an internship in early 2017 in the Bucci Lab. That was an eye-opening experience, Ravichandran explains. “Back at my university in India, only 2 to 3 percent of the mechanical engineering students were women, and there were no women on the faculty. It was the first time I had faced social inequities because of my gender, and I went through some struggles, to say the least.”

      MIT offered a welcome contrast. “The amount of freedom I was given made me extremely happy,” she says. “I was always encouraged to explore my ideas, and I always felt included.” She was doubly happy because, midway through the internship, she learned that she’d been accepted to MIT’s graduate program.

      As a PhD student, her research has followed a similar path. She continues to study boiling and heat transfer, but Bucci gave this work some added urgency. They’re now investigating the aforementioned boiling crisis, which affects nuclear reactors and other kinds of power plants that rely on steam generation to drive turbines. In a light water nuclear reactor, water is heated by fuel rods in which nuclear fission has occurred. Heat removal is most efficient when the water circulating past the rods boils. However, if too many bubbles form on the surface, enveloping the fuel rods in a layer of vapor, heat transfer is greatly reduced. That’s not only diminishes power generation, it can also be dangerous because the fuel rods must be continuously cooled to avoid a dreaded meltdown accident.

      Nuclear plants operate at low power ratings to provide an ample safety margin and thereby prevent such a scenario from occurring. Ravichandran believes these standards may be overly cautious, owing to the fact that people aren’t yet sure of the conditions that bring about the boiling crisis. This hurts the economic viability of nuclear power, she says, at a time when we desperately need carbon-free power sources. But Ravichandran and other researchers in the Bucci Lab are starting to fill some major gaps in our understanding.

      They initially ran experiments to determine how quickly bubbles form when water hits a hot surface, how big the bubbles get, how long they grow, and how the surface temperature changes. “A typical experiment lasted two minutes, but it took more than three weeks to pick out every bubble that formed and track its growth and evolution,” Ravichandran explains.

      To streamline this process, she and Bucci are implementing a machine learning approach, based on neural network technology. Neural networks are good at recognizing patterns, including those associated with bubble nucleation. “These networks are data hungry,” Ravichandran says. “The more data they’re fed, the better they perform.” The networks were trained on experimental results pertaining to bubble formation on different surfaces; the networks were then tested on surfaces for which the NSE researchers had no data and didn’t know what to expect.

      After gaining experimental validation of the output from the machine learning models, the team is now trying to get these models to make reliable predictions as to when the bubble crisis, itself, will occur. The ultimate goal is to have a fully autonomous system that can not only predict the boiling crisis, but also show why it happens and automatically shut down experiments before things go too far and lab equipment starts melting.

      In the meantime, Ravichandran and Bucci have made some important theoretical advances, which they report on in a recently published paper for Applied Physics Letters. There had been a debate in the nuclear engineering community as to whether the boiling crisis is caused by bubbles covering the fuel rod surface or due to bubbles growing on top of each other, extending outward from the surface. Ravichandran and Bucci determined that it is a surface-level phenomenon. In addition, they’ve identified the three main factors that trigger the boiling crisis. First, there’s the number of bubbles that form over a given surface area and, second, the average bubble size. The third factor is the product of the bubble frequency (the number of bubbles forming within a second at a given site) and the time it takes for a bubble to reach its full size.

      Ravichandran is happy to have shed some new light on this issue but acknowledges that there’s still much work to be done. Although her research agenda is ambitious and nearly all consuming, she never forgets where she came from and the sense of isolation she felt while studying engineering as an undergraduate. She has, on her own initiative, been mentoring female engineering students in India, providing both research guidance and career advice.

      “I sometimes feel there was a reason I went through those early hardships,” Ravichandran says. “That’s what made me decide that I want to be an educator.” She’s also grateful for the opportunities that have opened up for her since coming to MIT. A recipient of a 2021-22 MathWorks Engineering Fellowship, she says, “now it feels like the only limits on me are those that I’ve placed on myself.” More