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      in Energy

      MIT Climate and Sustainability Consortium announces recipients of inaugural MCSC Seed Awards

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      The MIT Climate and Sustainability Consortium (MCSC) has awarded 20 projects a total of $5 million over two years in its first-ever 2022 MCSC Seed Awards program. The winning projects are led by principal investigators across all five of MIT’s schools.

      The goal of the MCSC Seed Awards is to engage MIT researchers and link the economy-wide work of the consortium to ongoing and emerging climate and sustainability efforts across campus. The program offers further opportunity to build networks among the awarded projects to deepen the impact of each and ensure the total is greater than the sum of its parts.

      For example, to drive progress under the awards category Circularity and Materials, the MCSC can facilitate connections between the technologists at MIT who are developing recovery approaches for metals, plastics, and fiber; the urban planners who are uncovering barriers to reuse; and the engineers, who will look for efficiency opportunities in reverse supply chains.

      “The MCSC Seed Awards are designed to complement actions previously outlined in Fast Forward: MIT’s Climate Action Plan for the Decade and, more specifically, the Climate Grand Challenges,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering, Vannevar Bush Professor of Electrical Engineering and Computer Science, and chair of the MIT Climate and Sustainability Consortium. “In collaboration with seed award recipients and MCSC industry members, we are eager to engage in interdisciplinary exploration and propel urgent advancements in climate and sustainability.” 

      By supporting MIT researchers with expertise in economics, infrastructure, community risk assessment, mobility, and alternative fuels, the MCSC will accelerate implementation of cross-disciplinary solutions in the awards category Decarbonized and Resilient Value Chains. Enhancing Natural Carbon Sinks and building connections to local communities will require associations across experts in ecosystem change, biodiversity, improved agricultural practice and engagement with farmers, all of which the consortium can begin to foster through the seed awards.

      “Funding opportunities across campus has been a top priority since launching the MCSC,” says Jeremy Gregory, MCSC executive director. “It is our honor to support innovative teams of MIT researchers through the inaugural 2022 MCSC Seed Awards program.”

      The winning projects are tightly aligned with the MCSC’s areas of focus, which were derived from a year of highly engaged collaborations with MCSC member companies. The projects apply across the member’s climate and sustainability goals.

      The MCSC’s 16 member companies span many industries, and since early 2021, have met with members of the MIT community to define focused problem statements for industry-specific challenges, identify meaningful partnerships and collaborations, and develop clear and scalable priorities. Outcomes from these collaborations laid the foundation for the focus areas, which have shaped the work of the MCSC. Specifically, the MCSC Industry Advisory Board engaged with MIT on key strategic directions, and played a critical role in the MCSC’s series of interactive events. These included virtual workshops hosted last summer, each on a specific topic that allowed companies to work with MIT and each other to align key assumptions, identify blind spots in corporate goal-setting, and leverage synergies between members, across industries. The work continued in follow-up sessions and an annual symposium.

      “We are excited to see how the seed award efforts will help our member companies reach or even exceed their ambitious climate targets, find new cross-sector links among each other, seek opportunities to lead, and ripple key lessons within their industry, while also deepening the Institute’s strong foundation in climate and sustainability research,” says Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in Materials Science and Engineering and MCSC co-director.

      As the seed projects take shape, the MCSC will provide ongoing opportunities for awardees to engage with the Industry Advisory Board and technical teams from the MCSC member companies to learn more about the potential for linking efforts to support and accelerate their climate and sustainability goals. Awardees will also have the chance to engage with other members of the MCSC community, including its interdisciplinary Faculty Steering Committee.

      “One of our mantras in the MCSC is to ‘amplify and extend’ existing efforts across campus; we’re always looking for ways to connect the collaborative industry relationships we’re building and the work we’re doing with other efforts on campus,” notes Jeffrey Grossman, the Morton and Claire Goulder and Family Professor in Environmental Systems, head of the Department of Materials Science and Engineering, and MCSC co-director. “We feel the urgency as well as the potential, and we don’t want to miss opportunities to do more and go faster.”

      The MCSC Seed Awards complement the Climate Grand Challenges, a new initiative to mobilize the entire MIT research community around developing the bold, interdisciplinary solutions needed to address difficult, unsolved climate problems. The 27 finalist teams addressed four broad research themes, which align with the MCSC’s focus areas. From these finalist teams, five flagship projects were announced in April 2022.

      The parallels between MCSC’s focus areas and the Climate Grand Challenges themes underscore an important connection between the shared long-term research interests of industry and academia. The challenges that some of the world’s largest and most influential companies have identified are complementary to MIT’s ongoing research and innovation — highlighting the tremendous opportunity to develop breakthroughs and scalable solutions quickly and effectively. Special Presidential Envoy for Climate John Kerry underscored the importance of developing these scalable solutions, including critical new technology, during a conversation with MIT President L. Rafael Reif at MIT’s first Climate Grand Challenges showcase event last month.

      Both the MCSC Seed Awards and the Climate Grand Challenges are part of MIT’s larger commitment and initiative to combat climate change; this was underscored in “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021.

      The project titles and research leads for each of the 20 awardees listed below are categorized by MCSC focus area.

      Decarbonized and resilient value chains

      “Collaborative community mapping toolkit for resilience planning,” led by Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab (a research lead on Climate Grand Challenges flagship project) and Nicholas de Monchaux, professor and department head in the Department of Architecture
      “CP4All: Fast and local climate projections with scientific machine learning — towards accessibility for all of humanity,” led by Chris Hill, principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences and Dava Newman, director of the MIT Media Lab and the Apollo Program Professor in the Department of Aeronautics and Astronautics
      “Emissions reductions and productivity in U.S. manufacturing,” led by Mert Demirer, assistant professor of applied economics at the MIT Sloan School of Management and Jing Li, assistant professor and William Barton Rogers Career Development Chair of Energy Economics in the MIT Sloan School of Management
      “Logistics electrification through scalable and inter-operable charging infrastructure: operations, planning, and policy,” led by Alex Jacquillat, the 1942 Career Development Professor and assistant professor of operations research and statistics in the MIT Sloan School of Management
      “Powertrain and system design for LOHC-powered long-haul trucking,” led by William Green, the Hoyt Hottel Professor in Chemical Engineering in the Department of Chemical Engineering and postdoctoral officer, and Wai K. Cheng, professor in the Department of Mechanical Engineering and director of the Sloan Automotive Laboratory
      “Sustainable Separation and Purification of Biochemicals and Biofuels using Membranes,” led by John Lienhard, the Abdul Latif Jameel Professor of Water in the Department of Mechanical Engineering, director of the Abdul Latif Jameel Water and Food Systems Lab, and director of the Rohsenow Kendall Heat Transfer Laboratory; and Nicolas Hadjiconstantinou, professor in the Department of Mechanical Engineering, co-director of the Center for Computational Science and Engineering, associate director of the Center for Exascale Simulation of Materials in Extreme Environments, and graduate officer
      “Toolkit for assessing the vulnerability of industry infrastructure siting to climate change,” led by Michael Howland, assistant professor in the Department of Civil and Environmental Engineering

      Circularity and Materials

      “Colorimetric Sulfidation for Aluminum Recycling,” led by Antoine Allanore, associate professor of metallurgy in the Department of Materials Science and Engineering
      “Double Loop Circularity in Materials Design Demonstrated on Polyurethanes,” led by Brad Olsen, the Alexander and I. Michael Kasser (1960) Professor and graduate admissions co-chair in the Department of Chemical Engineering, and Kristala Prather, the Arthur Dehon Little Professor and department executive officer in the Department of Chemical Engineering
      “Engineering of a microbial consortium to degrade and valorize plastic waste,” led by Otto Cordero, associate professor in the Department of Civil and Environmental Engineering, and Desiree Plata, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering and associate professor in the Department of Civil and Environmental Engineering
      “Fruit-peel-inspired, biodegradable packaging platform with multifunctional barrier properties,” led by Kripa Varanasi, professor in the Department of Mechanical Engineering
      “High Throughput Screening of Sustainable Polyesters for Fibers,” led by Gregory Rutledge, the Lammot du Pont Professor in the Department of Chemical Engineering, and Brad Olsen, Alexander and I. Michael Kasser (1960) Professor and graduate admissions co-chair in the Department of Chemical Engineering
      “Short-term and long-term efficiency gains in reverse supply chains,” led by Yossi Sheffi, the Elisha Gray II Professor of Engineering Systems, professor in the Department of Civil and Environmental Engineering, and director of the Center for Transportation and Logistics
      The costs and benefits of circularity in building construction, led by Siqi Zheng, the STL Champion Professor of Urban and Real Estate Sustainability at the MIT Center for Real Estate and Department of Urban Studies and Planning, faculty director of the MIT Center for Real Estate, and faculty director for the MIT Sustainable Urbanization Lab; and Randolph Kirchain, principal research scientist and co-director of MIT Concrete Sustainability Hub

      Natural carbon sinks

      “Carbon sequestration through sustainable practices by smallholder farmers,” led by Joann de Zegher, the Maurice F. Strong Career Development Professor and assistant professor of operations management in the MIT Sloan School of Management, and Karen Zheng the George M. Bunker Professor and associate professor of operations management in the MIT Sloan School of Management
      “Coatings to protect and enhance diverse microbes for improved soil health and crop yields,” led by Ariel Furst, the Raymond A. (1921) And Helen E. St. Laurent Career Development Professor of Chemical Engineering in the Department of Chemical Engineering, and Mary Gehring, associate professor of biology in the Department of Biology, core member of the Whitehead Institute for Biomedical Research, and graduate officer
      “ECO-LENS: Mainstreaming biodiversity data through AI,” led by John Fernández, professor of building technology in the Department of Architecture and director of MIT Environmental Solutions Initiative
      “Growing season length, productivity, and carbon balance of global ecosystems under climate change,” led by Charles Harvey, professor in the Department of Civil and Environmental Engineering, and César Terrer, assistant professor in the Department of Civil and Environmental Engineering

      Social dimensions and adaptation

      “Anthro-engineering decarbonization at the million-person scale,” led by Manduhai Buyandelger, professor in the Anthropology Section, and Michael Short, the Class of ’42 Associate Professor of Nuclear Science and Engineering in the Department of Nuclear Science and Engineering
      “Sustainable solutions for climate change adaptation: weaving traditional ecological knowledge and STEAM,” led by Janelle Knox-Hayes, the Lister Brothers Associate Professor of Economic Geography and Planning and head of the Environmental Policy and Planning Group in the Department of Urban Studies and Planning, and Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab (a research lead on a Climate Grand Challenges flagship project) More

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      in Environment

      MIT J-WAFS announces 2022 seed grant recipients

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      The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) at MIT has awarded eight MIT principal investigators with 2022 J-WAFS seed grants. The grants support innovative MIT research that has the potential to have significant impact on water- and food-related challenges.

      The only program at MIT that is dedicated to water- and food-related research, J-WAFS has offered seed grant funding to MIT principal investigators and their teams for the past eight years. The grants provide up to $75,000 per year, overhead-free, for two years to support new, early-stage research in areas such as water and food security, safety, supply, and sustainability. Past projects have spanned many diverse disciplines, including engineering, science, technology, and business innovation, as well as social science and economics, architecture, and urban planning. 

      Seven new projects led by eight researchers will be supported this year. With funding going to four different MIT departments, the projects address a range of challenges by employing advanced materials, technology innovations, and new approaches to resource management. The new projects aim to remove harmful chemicals from water sources, develop drought monitoring systems for farmers, improve management of the shellfish industry, optimize water purification materials, and more.

      “Climate change, the pandemic, and most recently the war in Ukraine have exacerbated and put a spotlight on the serious challenges facing global water and food systems,” says J-WAFS director John H. Lienhard. He adds, “The proposals chosen this year have the potential to create measurable, real-world impacts in both the water and food sectors.”  

      The 2022 J-WAFS seed grant researchers and their projects are:

      Gang Chen, the Carl Richard Soderberg Professor of Power Engineering in MIT’s Department of Mechanical Engineering, is using sunlight to desalinate water. The use of solar energy for desalination is not a new idea, particularly solar thermal evaporation methods. However, the solar thermal evaporation process has an overall low efficiency because it relies on breaking hydrogen bonds among individual water molecules, which is very energy-intensive. Chen and his lab recently discovered a photomolecular effect that dramatically lowers the energy required for desalination. 

      The bonds among water molecules inside a water cluster in liquid water are mostly hydrogen bonds. Chen discovered that a photon with energy larger than the bonding energy between the water cluster and the remaining water liquids can cleave off the water cluster at the water-air interface, colliding with air molecules and disintegrating into 60 or even more individual water molecules. This effect has the potential to significantly boost clean water production via new desalination technology that produces a photomolecular evaporation rate that exceeds pure solar thermal evaporation by at least ten-fold. 

      John E. Fernández is the director of the MIT Environmental Solutions Initiative (ESI) and a professor in the Department of Architecture, and also affiliated with the Department of Urban Studies and Planning. Fernández is working with Scott D. Odell, a postdoc in the ESI, to better understand the impacts of mining and climate change in water-stressed regions of Chile.

      The country of Chile is one of the world’s largest exporters of both agricultural and mineral products; however, little research has been done on climate change effects at the intersection of these two sectors. Fernández and Odell will explore how desalination is being deployed by the mining industry to relieve pressure on continental water supplies in Chile, and with what effect. They will also research how climate change and mining intersect to affect Andean glaciers and agricultural communities dependent upon them. The researchers intend for this work to inform policies to reduce social and environmental harms from mining, desalination, and climate change.

      Ariel L. Furst is the Raymond (1921) and Helen St. Laurent Career Development Professor of Chemical Engineering at MIT. Her 2022 J-WAFS seed grant project seeks to effectively remove dangerous and long-lasting chemicals from water supplies and other environmental areas. 

      Perfluorooctanoic acid (PFOA), a component of Teflon, is a member of a group of chemicals known as per- and polyfluoroalkyl substances (PFAS). These human-made chemicals have been extensively used in consumer products like nonstick cooking pans. Exceptionally high levels of PFOA have been measured in water sources near manufacturing sites, which is problematic as these chemicals do not readily degrade in our bodies or the environment. The majority of humans have detectable levels of PFAS in their blood, which can lead to significant health issues including cancer, liver damage, and thyroid effects, as well as developmental effects in infants. Current remediation methods are limited to inefficient capture and are mostly confined to laboratory settings. Furst’s proposed method utilizes low-energy, scaffolded enzyme materials to move beyond simple capture to degrade these hazardous pollutants.

      Heather J. Kulik is an associate professor in the Department of Chemical Engineering at MIT who is developing novel computational strategies to identify optimal materials for purifying water. Water treatment requires purification by selectively separating small ions from water. However, human-made, scalable materials for water purification and desalination are often not stable in typical operating conditions and lack precision pores for good separation. 

      Metal-organic frameworks (MOFs) are promising materials for water purification because their pores can be tailored to have precise shapes and chemical makeup for selective ion affinity. Yet few MOFs have been assessed for their properties relevant to water purification. Kulik plans to use virtual high-throughput screening accelerated by machine learning models and molecular simulation to accelerate discovery of MOFs. Specifically, Kulik will be looking for MOFs with ultra-stable structures in water that do not break down at certain temperatures. 

      Gregory C. Rutledge is the Lammot du Pont Professor of Chemical Engineering at MIT. He is leading a project that will explore how to better separate oils from water. This is an important problem to solve given that industry-generated oil-contaminated water is a major source of pollution to the environment.

      Emulsified oils are particularly challenging to remove from water due to their small droplet sizes and long settling times. Microfiltration is an attractive technology for the removal of emulsified oils, but its major drawback is fouling, or the accumulation of unwanted material on solid surfaces. Rutledge will examine the mechanism of separation behind liquid-infused membranes (LIMs) in which an infused liquid coats the surface and pores of the membrane, preventing fouling. Robustness of the LIM technology for removal of different types of emulsified oils and oil mixtures will be evaluated. César Terrer is an assistant professor in the Department of Civil and Environmental Engineering whose J-WAFS project seeks to answer the question: How can satellite images be used to provide a high-resolution drought monitoring system for farmers? 

      Drought is recognized as one of the world’s most pressing issues, with direct impacts on vegetation that threaten water resources and food production globally. However, assessing and monitoring the impact of droughts on vegetation is extremely challenging as plants’ sensitivity to lack of water varies across species and ecosystems. Terrer will leverage a new generation of remote sensing satellites to provide high-resolution assessments of plant water stress at regional to global scales. The aim is to provide a plant drought monitoring product with farmland-specific services for water and socioeconomic management.

      Michael Triantafyllou is the Henry L. and Grace Doherty Professor in Ocean Science and Engineering in the Department of Mechanical Engineering. He is developing a web-based system for natural resources management that will deploy geospatial analysis, visualization, and reporting to better manage and facilitate aquaculture data.  By providing value to commercial fisheries’ permit holders who employ significant numbers of people and also to recreational shellfish permit holders who contribute to local economies, the project has attracted support from the Massachusetts Division of Marine Fisheries as well as a number of local resource management departments.

      Massachusetts shell fisheries generated roughly $339 million in 2020, accounting for 17 percent of U.S. East Coast production. Managing such a large industry is a time-consuming process, given there are thousands of acres of coastal areas grouped within over 800 classified shellfish growing areas. Extreme climate events present additional challenges. Triantafyllou’s research will help efforts to enforce environmental regulations, support habitat restoration efforts, and prevent shellfish-related food safety issues. More

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      in Energy

      MIT expands research collaboration with Commonwealth Fusion Systems to build net energy fusion machine, SPARC

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      MIT’s Plasma Science and Fusion Center (PSFC) will substantially expand its fusion energy research and education activities under a new five-year agreement with Institute spinout Commonwealth Fusion Systems (CFS).

      “This expanded relationship puts MIT and PSFC in a prime position to be an even stronger academic leader that can help deliver the research and education needs of the burgeoning fusion energy industry, in part by utilizing the world’s first burning plasma and net energy fusion machine, SPARC,” says PSFC director Dennis Whyte. “CFS will build SPARC and develop a commercial fusion product, while MIT PSFC will focus on its core mission of cutting-edge research and education.”

      Commercial fusion energy has the potential to play a significant role in combating climate change, and there is a concurrent increase in interest from the energy sector, governments, and foundations. The new agreement, administered by the MIT Energy Initiative (MITEI), where CFS is a startup member, will help PSFC expand its fusion technology efforts with a wider variety of sponsors. The collaboration enables rapid execution at scale and technology transfer into the commercial sector as soon as possible.

      This new agreement doubles CFS’ financial commitment to PSFC, enabling greater recruitment and support of students, staff, and faculty. “We’ll significantly increase the number of graduate students and postdocs, and just as important they will be working on a more diverse set of fusion science and technology topics,” notes Whyte. It extends the collaboration between PSFC and CFS that resulted in numerous advances toward fusion power plants, including last fall’s demonstration of a high-temperature superconducting (HTS) fusion electromagnet with record-setting field strength of 20 tesla.

      The combined magnetic fusion efforts at PSFC will surpass those in place during the operations of the pioneering Alcator C-Mod tokamak device that operated from 1993 to 2016. This increase in activity reflects a moment when multiple fusion energy technologies are seeing rapidly accelerating development worldwide, and the emergence of a new fusion energy industry that would require thousands of trained people.

      MITEI director Robert Armstrong adds, “Our goal from the beginning was to create a membership model that would allow startups who have specific research challenges to leverage the MITEI ecosystem, including MIT faculty, students, and other MITEI members. The team at the PSFC and MITEI have worked seamlessly to support CFS, and we are excited for this next phase of the relationship.”

      PSFC is supporting CFS’ efforts toward realizing the SPARC fusion platform, which facilitates rapid development and refinement of elements (including HTS magnets) needed to build ARC, a compact, modular, high-field fusion power plant that would set the stage for commercial fusion energy production. The concepts originated in Whyte’s nuclear science and engineering class 22.63 (Principles of Fusion Engineering) and have been carried forward by students and PSFC staff, many of whom helped found CFS; the new activity will expand research into advanced technologies for the envisioned pilot plant.

      “This has been an incredibly effective collaboration that has resulted in a major breakthrough for commercial fusion with the successful demonstration of revolutionary fusion magnet technology that will enable the world’s first commercially relevant net energy fusion device, SPARC, currently under construction,” says Bob Mumgaard SM ’15, PhD ’15, CEO of Commonwealth Fusion Systems. “We look forward to this next phase in the collaboration with MIT as we tackle the critical research challenges ahead for the next steps toward fusion power plant development.”

      In the push for commercial fusion energy, the next five years are critical, requiring intensive work on materials longevity, heat transfer, fuel recycling, maintenance, and other crucial aspects of power plant development. It will need innovation from almost every engineering discipline. “Having great teams working now, it will cut the time needed to move from SPARC to ARC, and really unleash the creativity. And the thing MIT does so well is cut across disciplines,” says Whyte.

      “To address the climate crisis, the world needs to deploy existing clean energy solutions as widely and as quickly as possible, while at the same time developing new technologies — and our goal is that those new technologies will include fusion power,” says Maria T. Zuber, MIT’s vice president for research. “To make new climate solutions a reality, we need focused, sustained collaborations like the one between MIT and Commonwealth Fusion Systems. Delivering fusion power onto the grid is a monumental challenge, and the combined capabilities of these two organizations are what the challenge demands.”

      On a strategic level, climate change and the imperative need for widely implementable carbon-free energy have helped orient the PSFC team toward scalability. “Building one or 10 fusion plants doesn’t make a difference — we have to build thousands,” says Whyte. “The design decisions we make will impact the ability to do that down the road. The real enemy here is time, and we want to remove as many impediments as possible and commit to funding a new generation of scientific leaders. Those are critically important in a field with as much interdisciplinary integration as fusion.” More

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      in Energy

      Team creates map for production of eco-friendly metals

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      In work that could usher in more efficient, eco-friendly processes for producing important metals like lithium, iron, and cobalt, researchers from MIT and the SLAC National Accelerator Laboratory have mapped what is happening at the atomic level behind a particularly promising approach called metal electrolysis.

      By creating maps for a wide range of metals, they not only determined which metals should be easiest to produce using this approach, but also identified fundamental barriers behind the efficient production of others. As a result, the researchers’ map could become an important design tool for optimizing the production of all these metals.

      The work could also aid the development of metal-air batteries, cousins of the lithium-ion batteries used in today’s electric vehicles.

      Most of the metals key to society today are produced using fossil fuels. These fuels generate the high temperatures necessary to convert the original ore into its purified metal. But that process is a significant source of greenhouse gases — steel alone accounts for some 7 percent of carbon dioxide emissions globally. As a result, researchers from around the world are working to identify more eco-friendly ways for the production of metals.

      One promising approach is metal electrolysis, in which a metal oxide, the ore, is zapped with electricity to create pure metal with oxygen as the byproduct. That is the reaction explored at the atomic level in new research reported in the April 8 issue of the journal Chemistry of Materials.

      Donald Siegel is department chair and professor of mechanical engineering at the University of Texas at Austin. Says Siegel, who was not involved in the Chemistry of Materials study: “This work is an important contribution to improving the efficiency of metal production from metal oxides. It clarifies our understanding of low-carbon electrolysis processes by tracing the underlying thermodynamics back to elementary metal-oxygen interactions. I expect that this work will aid in the creation of design rules that will make these industrially important processes less reliant on fossil fuels.”

      Yang Shao-Horn, the JR East Professor of Engineering in MIT’s Department of Materials Science and Engineering (DMSE) and Department of Mechanical Engineering, is a leader of the current work, with Michal Bajdich of SLAC.

      “Here we aim to establish some basic understanding to predict the efficiency of electrochemical metal production and metal-air batteries from examining computed thermodynamic barriers for the conversion between metal and metal oxides,” says Shao-Horn, who is on the research team for MIT’s new Center for Electrification and Decarbonization of Industry, a winner of the Institute’s first-ever Climate Grand Challenges competition. Shao-Horn is also affiliated with MIT’s Materials Research Laboratory and Research Laboratory of Electronics.

      In addition to Shao-Horn and Bajdich, other authors of the Chemistry of Materials paper are Jaclyn R. Lunger, first author and a DMSE graduate student; mechanical engineering senior Naomi Lutz; and DMSE graduate student Jiayu Peng.

      Other applications

      The work could also aid in developing metal-air batteries such as lithium-air, aluminum-air, and zinc-air batteries. These cousins of the lithium-ion batteries used in today’s electric vehicles have the potential to electrify aviation because their energy densities are much higher. However, they are not yet on the market due to a variety of problems including inefficiency.

      Charging metal-air batteries also involves electrolysis. As a result, the new atomic-level understanding of these reactions could not only help engineers develop efficient electrochemical routes for metal production, but also design more efficient metal-air batteries.

      Learning from water splitting

      Electrolysis is also used to split water into oxygen and hydrogen, which stores the resulting energy. That hydrogen, in turn, could become an eco-friendly alternative to fossil fuels. Since much more is known about water electrolysis, the focus of Bajdich’s work at SLAC, than the electrolysis of metal oxides, the team compared the two processes for the first time.

      The result: “Slowly, we uncovered the elementary steps involved in metal electrolysis,” says Bajdich. The work was challenging, says Lunger, because “it was unclear to us what those steps are. We had to figure out how to get from A to B,” or from a metal oxide to metal and oxygen.

      All of the work was conducted with supercomputer simulations. “It’s like a sandbox of atoms, and then we play with them. It’s a little like Legos,” says Bajdich. More specifically, the team explored different scenarios for the electrolysis of several metals. Each involved different catalysts, molecules that boost the speed of a reaction.

      Says Lunger, “To optimize the reaction, you want to find the catalyst that makes it most efficient.” The team’s map is essentially a guide for designing the best catalysts for each different metal.

      What’s next? Lunger noted that the current work focused on the electrolysis of pure metals. “I’m interested in seeing what happens in more complex systems involving multiple metals. Can you make the reaction more efficient if there’s sodium and lithium present, or cadmium and cesium?”

      This work was supported by a U.S. Department of Energy Office of Science Graduate Student Research award. It was also supported by an MIT Energy Initiative fellowship, the Toyota Research Institute through the Accelerated Materials Design and Discovery Program, the Catalysis Science Program of Department of Energy, Office of Basic Energy Sciences, and by the Differentiate Program through the U.S. Advanced Research Projects Agency — Energy.  More

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      in Security

      Engineers use artificial intelligence to capture the complexity of breaking waves

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      Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex to predict.

      Now, MIT engineers have found a new way to model how waves break. The team used machine learning along with data from wave-tank experiments to tweak equations that have traditionally been used to predict wave behavior. Engineers typically rely on such equations to help them design resilient offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

      The updated model made more accurate predictions of how and when waves break, the researchers found. For instance, the model estimated a wave’s steepness just before breaking, and its energy and frequency after breaking, more accurately than the conventional wave equations.

      Their results, published today in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. Knowing precisely how these waves interact can help hone the design of offshore structures. It can also improve predictions for how the ocean interacts with the atmosphere. Having better estimates of how waves break can help scientists predict, for instance, how much carbon dioxide and other atmospheric gases the ocean can absorb.

      “Wave breaking is what puts air into the ocean,” says study author Themis Sapsis, an associate professor of mechanical and ocean engineering and an affiliate of the Institute for Data, Systems, and Society at MIT. “It may sound like a detail, but if you multiply its effect over the area of the entire ocean, wave breaking starts becoming fundamentally important to climate prediction.”

      The study’s co-authors include lead author and MIT postdoc Debbie Eeltink, Hubert Branger and Christopher Luneau of Aix-Marseille University, Amin Chabchoub of Kyoto University, Jerome Kasparian of the University of Geneva, and T.S. van den Bremer of Delft University of Technology.

      Learning tank

      To predict the dynamics of a breaking wave, scientists typically take one of two approaches: They either attempt to precisely simulate the wave at the scale of individual molecules of water and air, or they run experiments to try and characterize waves with actual measurements. The first approach is computationally expensive and difficult to simulate even over a small area; the second requires a huge amount of time to run enough experiments to yield statistically significant results.

      The MIT team instead borrowed pieces from both approaches to develop a more efficient and accurate model using machine learning. The researchers started with a set of equations that is considered the standard description of wave behavior. They aimed to improve the model by “training” the model on data of breaking waves from actual experiments.

      “We had a simple model that doesn’t capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking,” Eeltink explains. “Then we wanted to use machine learning to learn the difference between the two.”

      The researchers obtained wave breaking data by running experiments in a 40-meter-long tank. The tank was fitted at one end with a paddle which the team used to initiate each wave. The team set the paddle to produce a breaking wave in the middle of the tank. Gauges along the length of the tank measured the water’s height as waves propagated down the tank.

      “It takes a lot of time to run these experiments,” Eeltink says. “Between each experiment you have to wait for the water to completely calm down before you launch the next experiment, otherwise they influence each other.”

      Safe harbor

      In all, the team ran about 250 experiments, the data from which they used to train a type of machine-learning algorithm known as a neural network. Specifically, the algorithm is trained to compare the real waves in experiments with the predicted waves in the simple model, and based on any differences between the two, the algorithm tunes the model to fit reality.

      After training the algorithm on their experimental data, the team introduced the model to entirely new data — in this case, measurements from two independent experiments, each run at separate wave tanks with different dimensions. In these tests, they found the updated model made more accurate predictions than the simple, untrained model, for instance making better estimates of a breaking wave’s steepness.

      The new model also captured an essential property of breaking waves known as the “downshift,” in which the frequency of a wave is shifted to a lower value. The speed of a wave depends on its frequency. For ocean waves, lower frequencies move faster than higher frequencies. Therefore, after the downshift, the wave will move faster. The new model predicts the change in frequency, before and after each breaking wave, which could be especially relevant in preparing for coastal storms.

      “When you want to forecast when high waves of a swell would reach a harbor, and you want to leave the harbor before those waves arrive, then if you get the wave frequency wrong, then the speed at which the waves are approaching is wrong,” Eeltink says.

      The team’s updated wave model is in the form of an open-source code that others could potentially use, for instance in climate simulations of the ocean’s potential to absorb carbon dioxide and other atmospheric gases. The code can also be worked into simulated tests of offshore platforms and coastal structures.

      “The number one purpose of this model is to predict what a wave will do,” Sapsis says. “If you don’t model wave breaking right, it would have tremendous implications for how structures behave. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors.”

      This research is supported, in part, by the Swiss National Science Foundation, and by the U.S. Office of Naval Research. More

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      in Energy

      How can we reduce the carbon footprint of global computing?

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      The voracious appetite for energy from the world’s computers and communications technology presents a clear threat for the globe’s warming climate. That was the blunt assessment from presenters in the intensive two-day Climate Implications of Computing and Communications workshop held on March 3 and 4, hosted by MIT’s Climate and Sustainability Consortium (MCSC), MIT-IBM Watson AI Lab, and the Schwarzman College of Computing.

      The virtual event featured rich discussions and highlighted opportunities for collaboration among an interdisciplinary group of MIT faculty and researchers and industry leaders across multiple sectors — underscoring the power of academia and industry coming together.

      “If we continue with the existing trajectory of compute energy, by 2040, we are supposed to hit the world’s energy production capacity. The increase in compute energy and demand has been increasing at a much faster rate than the world energy production capacity increase,” said Bilge Yildiz, the Breene M. Kerr Professor in the MIT departments of Nuclear Science and Engineering and Materials Science and Engineering, one of the workshop’s 18 presenters. This computing energy projection draws from the Semiconductor Research Corporations’s decadal report.To cite just one example: Information and communications technology already account for more than 2 percent of global energy demand, which is on a par with the aviation industries emissions from fuel.“We are the very beginning of this data-driven world. We really need to start thinking about this and act now,” said presenter Evgeni Gousev, senior director at Qualcomm.  Innovative energy-efficiency optionsTo that end, the workshop presentations explored a host of energy-efficiency options, including specialized chip design, data center architecture, better algorithms, hardware modifications, and changes in consumer behavior. Industry leaders from AMD, Ericsson, Google, IBM, iRobot, NVIDIA, Qualcomm, Tertill, Texas Instruments, and Verizon outlined their companies’ energy-saving programs, while experts from across MIT provided insight into current research that could yield more efficient computing.Panel topics ranged from “Custom hardware for efficient computing” to “Hardware for new architectures” to “Algorithms for efficient computing,” among others.

      Visual representation of the conversation during the workshop session entitled “Energy Efficient Systems.”

      Image: Haley McDevitt

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      The goal, said Yildiz, is to improve energy efficiency associated with computing by more than a million-fold.“I think part of the answer of how we make computing much more sustainable has to do with specialized architectures that have very high level of utilization,” said Darío Gil, IBM senior vice president and director of research, who stressed that solutions should be as “elegant” as possible.  For example, Gil illustrated an innovative chip design that uses vertical stacking to reduce the distance data has to travel, and thus reduces energy consumption. Surprisingly, more effective use of tape — a traditional medium for primary data storage — combined with specialized hard drives (HDD), can yield a dramatic savings in carbon dioxide emissions.Gil and presenters Bill Dally, chief scientist and senior vice president of research of NVIDIA; Ahmad Bahai, CTO of Texas Instruments; and others zeroed in on storage. Gil compared data to a floating iceberg in which we can have fast access to the “hot data” of the smaller visible part while the “cold data,” the large underwater mass, represents data that tolerates higher latency. Think about digital photo storage, Gil said. “Honestly, are you really retrieving all of those photographs on a continuous basis?” Storage systems should provide an optimized mix of of HDD for hot data and tape for cold data based on data access patterns.Bahai stressed the significant energy saving gained from segmenting standby and full processing. “We need to learn how to do nothing better,” he said. Dally spoke of mimicking the way our brain wakes up from a deep sleep, “We can wake [computers] up much faster, so we don’t need to keep them running in full speed.”Several workshop presenters spoke of a focus on “sparsity,” a matrix in which most of the elements are zero, as a way to improve efficiency in neural networks. Or as Dally said, “Never put off till tomorrow, where you could put off forever,” explaining efficiency is not “getting the most information with the fewest bits. It’s doing the most with the least energy.”Holistic and multidisciplinary approaches“We need both efficient algorithms and efficient hardware, and sometimes we need to co-design both the algorithm and the hardware for efficient computing,” said Song Han, a panel moderator and assistant professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT.Some presenters were optimistic about innovations already underway. According to Ericsson’s research, as much as 15 percent of the carbon emissions globally can be reduced through the use of existing solutions, noted Mats Pellbäck Scharp, head of sustainability at Ericsson. For example, GPUs are more efficient than CPUs for AI, and the progression from 3G to 5G networks boosts energy savings.“5G is the most energy efficient standard ever,” said Scharp. “We can build 5G without increasing energy consumption.”Companies such as Google are optimizing energy use at their data centers through improved design, technology, and renewable energy. “Five of our data centers around the globe are operating near or above 90 percent carbon-free energy,” said Jeff Dean, Google’s senior fellow and senior vice president of Google Research.Yet, pointing to the possible slowdown in the doubling of transistors in an integrated circuit — or Moore’s Law — “We need new approaches to meet this compute demand,” said Sam Naffziger, AMD senior vice president, corporate fellow, and product technology architect. Naffziger spoke of addressing performance “overkill.” For example, “we’re finding in the gaming and machine learning space we can make use of lower-precision math to deliver an image that looks just as good with 16-bit computations as with 32-bit computations, and instead of legacy 32b math to train AI networks, we can use lower-energy 8b or 16b computations.”

      Visual representation of the conversation during the workshop session entitled “Wireless, networked, and distributed systems.”

      Image: Haley McDevitt

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      Other presenters singled out compute at the edge as a prime energy hog.“We also have to change the devices that are put in our customers’ hands,” said Heidi Hemmer, senior vice president of engineering at Verizon. As we think about how we use energy, it is common to jump to data centers — but it really starts at the device itself, and the energy that the devices use. Then, we can think about home web routers, distributed networks, the data centers, and the hubs. “The devices are actually the least energy-efficient out of that,” concluded Hemmer.Some presenters had different perspectives. Several called for developing dedicated silicon chipsets for efficiency. However, panel moderator Muriel Medard, the Cecil H. Green Professor in EECS, described research at MIT, Boston University, and Maynooth University on the GRAND (Guessing Random Additive Noise Decoding) chip, saying, “rather than having obsolescence of chips as the new codes come in and in different standards, you can use one chip for all codes.”Whatever the chip or new algorithm, Helen Greiner, CEO of Tertill (a weeding robot) and co-founder of iRobot, emphasized that to get products to market, “We have to learn to go away from wanting to get the absolute latest and greatest, the most advanced processor that usually is more expensive.” She added, “I like to say robot demos are a dime a dozen, but robot products are very infrequent.”Greiner emphasized consumers can play a role in pushing for more energy-efficient products — just as drivers began to demand electric cars.Dean also sees an environmental role for the end user.“We have enabled our cloud customers to select which cloud region they want to run their computation in, and they can decide how important it is that they have a low carbon footprint,” he said, also citing other interfaces that might allow consumers to decide which air flights are more efficient or what impact installing a solar panel on their home would have.However, Scharp said, “Prolonging the life of your smartphone or tablet is really the best climate action you can do if you want to reduce your digital carbon footprint.”Facing increasing demandsDespite their optimism, the presenters acknowledged the world faces increasing compute demand from machine learning, AI, gaming, and especially, blockchain. Panel moderator Vivienne Sze, associate professor in EECS, noted the conundrum.“We can do a great job in making computing and communication really efficient. But there is this tendency that once things are very efficient, people use more of it, and this might result in an overall increase in the usage of these technologies, which will then increase our overall carbon footprint,” Sze said.Presenters saw great potential in academic/industry partnerships, particularly from research efforts on the academic side. “By combining these two forces together, you can really amplify the impact,” concluded Gousev.Presenters at the Climate Implications of Computing and Communications workshop also included: Joel Emer, professor of the practice in EECS at MIT; David Perreault, the Joseph F. and Nancy P. Keithley Professor of EECS at MIT; Jesús del Alamo, MIT Donner Professor and professor of electrical engineering in EECS at MIT; Heike Riel, IBM Fellow and head science and technology at IBM; and Takashi Ando, principal research staff member at IBM Research. The recorded workshop sessions are available on YouTube. More

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      in Security

      From seawater to drinking water, with the push of a button

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      MIT researchers have developed a portable desalination unit, weighing less than 10 kilograms, that can remove particles and salts to generate drinking water.

      The suitcase-sized device, which requires less power to operate than a cell phone charger, can also be driven by a small, portable solar panel, which can be purchased online for around $50. It automatically generates drinking water that exceeds World Health Organization quality standards. The technology is packaged into a user-friendly device that runs with the push of one button.

      Unlike other portable desalination units that require water to pass through filters, this device utilizes electrical power to remove particles from drinking water. Eliminating the need for replacement filters greatly reduces the long-term maintenance requirements.

      This could enable the unit to be deployed in remote and severely resource-limited areas, such as communities on small islands or aboard seafaring cargo ships. It could also be used to aid refugees fleeing natural disasters or by soldiers carrying out long-term military operations.

      “This is really the culmination of a 10-year journey that I and my group have been on. We worked for years on the physics behind individual desalination processes, but pushing all those advances into a box, building a system, and demonstrating it in the ocean, that was a really meaningful and rewarding experience for me,” says senior author Jongyoon Han, a professor of electrical engineering and computer science and of biological engineering, and a member of the Research Laboratory of Electronics (RLE).

      Joining Han on the paper are first author Junghyo Yoon, a research scientist in RLE; Hyukjin J. Kwon, a former postdoc; SungKu Kang, a postdoc at Northeastern University; and Eric Brack of the U.S. Army Combat Capabilities Development Command (DEVCOM). The research has been published online in Environmental Science and Technology.

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      Filter-free technology

      Commercially available portable desalination units typically require high-pressure pumps to push water through filters, which are very difficult to miniaturize without compromising the energy-efficiency of the device, explains Yoon.

      Instead, their unit relies on a technique called ion concentration polarization (ICP), which was pioneered by Han’s group more than 10 years ago. Rather than filtering water, the ICP process applies an electrical field to membranes placed above and below a channel of water. The membranes repel positively or negatively charged particles — including salt molecules, bacteria, and viruses — as they flow past. The charged particles are funneled into a second stream of water that is eventually discharged.

      The process removes both dissolved and suspended solids, allowing clean water to pass through the channel. Since it only requires a low-pressure pump, ICP uses less energy than other techniques.

      But ICP does not always remove all the salts floating in the middle of the channel. So the researchers incorporated a second process, known as electrodialysis, to remove remaining salt ions.

      Yoon and Kang used machine learning to find the ideal combination of ICP and electrodialysis modules. The optimal setup includes a two-stage ICP process, with water flowing through six modules in the first stage then through three in the second stage, followed by a single electrodialysis process. This minimized energy usage while ensuring the process remains self-cleaning.

      “While it is true that some charged particles could be captured on the ion exchange membrane, if they get trapped, we just reverse the polarity of the electric field and the charged particles can be easily removed,” Yoon explains.

      They shrunk and stacked the ICP and electrodialysis modules to improve their energy efficiency and enable them to fit inside a portable device. The researchers designed the device for nonexperts, with just one button to launch the automatic desalination and purification process. Once the salinity level and the number of particles decrease to specific thresholds, the device notifies the user that the water is drinkable.

      The researchers also created a smartphone app that can control the unit wirelessly and report real-time data on power consumption and water salinity.

      Beach tests

      After running lab experiments using water with different salinity and turbidity (cloudiness) levels, they field-tested the device at Boston’s Carson Beach.

      Yoon and Kwon set the box near the shore and tossed the feed tube into the water. In about half an hour, the device had filled a plastic drinking cup with clear, drinkable water.

      “It was successful even in its first run, which was quite exciting and surprising. But I think the main reason we were successful is the accumulation of all these little advances that we made along the way,” Han says.

      The resulting water exceeded World Health Organization quality guidelines, and the unit reduced the amount of suspended solids by at least a factor of 10. Their prototype generates drinking water at a rate of 0.3 liters per hour, and requires only 20 watts of power per liter.

      “Right now, we are pushing our research to scale up that production rate,” Yoon says.

      One of the biggest challenges of designing the portable system was engineering an intuitive device that could be used by anyone, Han says.

      Yoon hopes to make the device more user-friendly and improve its energy efficiency and production rate through a startup he plans to launch to commercialize the technology.

      In the lab, Han wants to apply the lessons he’s learned over the past decade to water-quality issues that go beyond desalination, such as rapidly detecting contaminants in drinking water.

      “This is definitely an exciting project, and I am proud of the progress we have made so far, but there is still a lot of work to do,” he says.

      For example, while “development of portable systems using electro-membrane processes is an original and exciting direction in off-grid, small-scale desalination,” the effects of fouling, especially if the water has high turbidity, could significantly increase maintenance requirements and energy costs, notes Nidal Hilal, professor of engineering and director of the New York University Abu Dhabi Water research center, who was not involved with this research.

      “Another limitation is the use of expensive materials,” he adds. “It would be interesting to see similar systems with low-cost materials in place.”

      The research was funded, in part, by the DEVCOM Soldier Center, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), the Experimental AI Postdoc Fellowship Program of Northeastern University, and the Roux AI Institute. More

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      Machine learning, harnessed to extreme computing, aids fusion energy development

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      MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science — predicting the temperature and density profiles of a magnetically confined plasma via first-principles simulation of plasma turbulence. Solving this problem by brute force is beyond the capabilities of even the most advanced supercomputers. Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution.

      Fusion energyFusion offers the promise of unlimited, carbon-free energy through the same physical process that powers the sun and the stars. It requires heating the fuel to temperatures above 100 million degrees, well above the point where the electrons are stripped from their atoms, creating a form of matter called plasma. On Earth, researchers use strong magnetic fields to isolate and insulate the hot plasma from ordinary matter. The stronger the magnetic field, the better the quality of the insulation that it provides.

      Rodriguez-Fernandez and Howard have focused on predicting the performance expected in the SPARC device, a compact, high-magnetic-field fusion experiment, currently under construction by the MIT spin-out company Commonwealth Fusion Systems (CFS) and researchers from MIT’s Plasma Science and Fusion Center. While the calculation required an extraordinary amount of computer time, over 8 million CPU-hours, what was remarkable was not how much time was used, but how little, given the daunting computational challenge.

      The computational challenge of fusion energyTurbulence, which is the mechanism for most of the heat loss in a confined plasma, is one of the science’s grand challenges and the greatest problem remaining in classical physics. The equations that govern fusion plasmas are well known, but analytic solutions are not possible in the regimes of interest, where nonlinearities are important and solutions encompass an enormous range of spatial and temporal scales. Scientists resort to solving the equations by numerical simulation on computers. It is no accident that fusion researchers have been pioneers in computational physics for the last 50 years.

      One of the fundamental problems for researchers is reliably predicting plasma temperature and density given only the magnetic field configuration and the externally applied input power. In confinement devices like SPARC, the external power and the heat input from the fusion process are lost through turbulence in the plasma. The turbulence itself is driven by the difference in the extremely high temperature of the plasma core and the relatively cool temperatures of the plasma edge (merely a few million degrees). Predicting the performance of a self-heated fusion plasma therefore requires a calculation of the power balance between the fusion power input and the losses due to turbulence.

      These calculations generally start by assuming plasma temperature and density profiles at a particular location, then computing the heat transported locally by turbulence. However, a useful prediction requires a self-consistent calculation of the profiles across the entire plasma, which includes both the heat input and turbulent losses. Directly solving this problem is beyond the capabilities of any existing computer, so researchers have developed an approach that stitches the profiles together from a series of demanding but tractable local calculations. This method works, but since the heat and particle fluxes depend on multiple parameters, the calculations can be very slow to converge.

      However, techniques emerging from the field of machine learning are well suited to optimize just such a calculation. Starting with a set of computationally intensive local calculations run with the full-physics, first-principles CGYRO code (provided by a team from General Atomics led by Jeff Candy) Rodriguez-Fernandez and Howard fit a surrogate mathematical model, which was used to explore and optimize a search within the parameter space. The results of the optimization were compared to the exact calculations at each optimum point, and the system was iterated to a desired level of accuracy. The researchers estimate that the technique reduced the number of runs of the CGYRO code by a factor of four.

      New approach increases confidence in predictionsThis work, described in a recent publication in the journal Nuclear Fusion, is the highest fidelity calculation ever made of the core of a fusion plasma. It refines and confirms predictions made with less demanding models. Professor Jonathan Citrin, of the Eindhoven University of Technology and leader of the fusion modeling group for DIFFER, the Dutch Institute for Fundamental Energy Research, commented: “The work significantly accelerates our capabilities in more routinely performing ultra-high-fidelity tokamak scenario prediction. This algorithm can help provide the ultimate validation test of machine design or scenario optimization carried out with faster, more reduced modeling, greatly increasing our confidence in the outcomes.” 

      In addition to increasing confidence in the fusion performance of the SPARC experiment, this technique provides a roadmap to check and calibrate reduced physics models, which run with a small fraction of the computational power. Such models, cross-checked against the results generated from turbulence simulations, will provide a reliable prediction before each SPARC discharge, helping to guide experimental campaigns and improving the scientific exploitation of the device. It can also be used to tweak and improve even simple data-driven models, which run extremely quickly, allowing researchers to sift through enormous parameter ranges to narrow down possible experiments or possible future machines.

      The research was funded by CFS, with computational support from the National Energy Research Scientific Computing Center, a U.S. Department of Energy Office of Science User Facility. More