Computer science and technology

Subterms

    More stories

    • 100 Shares149 Views

      in Energy

      Ms. Nuclear Energy is winning over nuclear skeptics

      by

      First-year MIT nuclear science and engineering (NSE) doctoral student Kaylee Cunningham is not the first person to notice that nuclear energy has a public relations problem. But her commitment to dispel myths about the alternative power source has earned her the moniker “Ms. Nuclear Energy” on TikTok and a devoted fan base on the social media platform.

      Cunningham’s activism kicked into place shortly after a week-long trip to Iceland to study geothermal energy. During a discussion about how the country was going to achieve its net zero energy goals, a representative from the University of Reykjavik balked at Cunnigham’s suggestion of including a nuclear option in the alternative energy mix. “The response I got was that we’re a peace-loving nation, we don’t do that,” Cunningham remembers. “I was appalled by the reaction, I mean we’re talking energy not weapons here, right?” she asks. Incredulous, Cunningham made a TikTok that targeted misinformation. Overnight she garnered 10,000 followers and “Ms. Nuclear Energy” was off to the races. Ms. Nuclear Energy is now Cunningham’s TikTok handle.

      Kaylee Cunningham: Dispelling myths and winning over skeptics

      A theater and science nerd

      TikTok is a fitting platform for a theater nerd like Cunningham. Born in Melrose, Massachusetts, Cunningham’s childhood was punctuated by moves to places where her roofer father’s work took the family. She moved to North Carolina shortly after fifth grade and fell in love with theater. “I was doing theater classes, the spring musical, it was my entire world,” Cunningham remembers. When she moved again, this time to Florida halfway through her first year of high school, she found the spring musical had already been cast. But she could help behind the scenes. Through that work, Cunningham gained her first real exposure to hands-on tech. She was hooked.

      Soon Cunningham was part of a team that represented her high school at the student Astronaut Challenge, an aerospace competition run by Florida State University. Statewide winners got to fly a space shuttle simulator at the Kennedy Space Center and participate in additional engineering challenges. Cunningham’s team was involved in creating a proposal to help NASA’s Asteroid Redirect Mission, designed to help the agency gather a large boulder from a near-earth asteroid. The task was Cunningham’s induction into an understanding of radiation and “anything nuclear.” Her high school engineering teacher, Nirmala Arunachalam, encouraged Cunningham’s interest in the subject.

      The Astronaut Challenge might just have been the end of Cunningham’s path in nuclear engineering had it not been for her mother. In high school, Cunningham had also enrolled in computer science classes and her love of the subject earned her a scholarship at Norwich University in Vermont where she had pursued a camp in cybersecurity. Cunningham had already laid down the college deposit for Norwich.

      But Cunningham’s mother persuaded her daughter to pay another visit to the University of Florida, where she had expressed interest in pursuing nuclear engineering. To her pleasant surprise, the department chair, Professor James Baciak, pulled out all the stops, bringing mother and daughter on a tour of the on-campus nuclear reactor and promising Cunningham a paid research position. Cunningham was sold and Backiak has been a mentor throughout her research career.

      Merging nuclear engineering and computer science

      Undergraduate research internships, including one at Oak Ridge National Laboratory, where she could combine her two loves, nuclear engineering and computer science, convinced Cunningham she wanted to pursue a similar path in graduate school.

      Cunningham’s undergraduate application to MIT had been rejected but that didn’t deter her from applying to NSE for graduate school. Having spent her early years in an elementary school barely 20 minutes from campus, she had grown up hearing that “the smartest people in the world go to MIT.” Cunningham figured that if she got into MIT, it would be “like going back home to Massachusetts” and that she could fit right in.

      Under the advisement of Professor Michael Short, Cunningham is looking to pursue her passions in both computer science and nuclear engineering in her doctoral studies.

      The activism continues

      Simultaneously, Cunningham is determined to keep her activism going.

      Her ability to digest “complex topics into something understandable to people who have no connection to academia” has helped Cunningham on TikTok. “It’s been something I’ve been doing all my life with my parents and siblings and extended family,” she says.

      Punctuating her video snippets with humor — a Simpsons reference is par for the course — helps Cunningham break through to her audience who love her goofy and tongue-in-cheek approach to the subject matter without compromising accuracy. “Sometimes I do stupid dances and make a total fool of myself, but I’ve really found my niche by being willing to engage and entertain people and educate them at the same time.”

      Such education needs to be an important part of an industry that’s received its share of misunderstandings, Cunningham says. “Technical people trying to communicate in a way that the general people don’t understand is such a concerning thing,” she adds. Case in point: the response in the wake of the Three Mile Island accident, which prevented massive contamination leaks. It was a perfect example of how well our safety regulations actually work, Cunningham says, “but you’d never guess from the PR fallout from it all.”

      As Ms. Nuclear Energy, Cunningham receives her share of skepticism. One viewer questioned the safety of nuclear reactors if “tons of pollution” was spewing out from them. Cunningham produced a TikTok that addressed this misconception. Pointing to the “pollution” in a photo, Cunningham clarifies that it’s just water vapor. The TikTok has garnered over a million views. “It really goes to show how starving for accurate information the public really is,” Cunningham says, “ in this age of having all the information we could ever want at our fingertips, it’s hard to sift through and decide what’s real and accurate and what isn’t.”

      Another reason for her advocacy: doing her part to encourage young people toward a nuclear science or engineering career. “If we’re going to start putting up tons of small modular reactors around the country, we need people to build them, people to run them, and we need regulatory bodies to inspect and keep them safe,” Cunningham points out. “ And we don’t have enough people entering the workforce in comparison to those that are retiring from the workforce,” she adds. “I’m able to engage those younger audiences and put nuclear engineering on their radar,” Cunningham says. The advocacy has been paying off: Cunningham regularly receives — and responds to — inquiries from high school junior girls looking for advice on pursuing nuclear engineering.

      All the activism is in service toward a clear end goal. “At the end of the day, the fight is to save the planet,” Cunningham says, “I honestly believe that nuclear power is the best chance we’ve got to fight climate change and keep our planet alive.” More

    • 138 Shares199 Views

      in Energy

      An interdisciplinary approach to fighting climate change through clean energy solutions

      by

      In early 2021, the U.S. government set an ambitious goal: to decarbonize its power grid, the system that generates and transmits electricity throughout the country, by 2035. It’s an important goal in the fight against climate change, and will require a switch from current, greenhouse-gas producing energy sources (such as coal and natural gas), to predominantly renewable ones (such as wind and solar).

      Getting the power grid to zero carbon will be a challenging undertaking, as Audun Botterud, a principal research scientist at the MIT Laboratory for Information and Decision Systems (LIDS) who has long been interested in the problem, knows well. It will require building lots of renewable energy generators and new infrastructure; designing better technology to capture, store, and carry electricity; creating the right regulatory and economic incentives; and more. Decarbonizing the grid also presents many computational challenges, which is where Botterud’s focus lies. Botterud has modeled different aspects of the grid — the mechanics of energy supply, demand, and storage, and electricity markets — where economic factors can have a huge effect on how quickly renewable solutions get adopted.

      On again, off again

      A major challenge of decarbonization is that the grid must be designed and operated to reliably meet demand. Using renewable energy sources complicates this, as wind and solar power depend on an infamously volatile system: the weather. A sunny day becomes gray and blustery, and wind turbines get a boost but solar farms go idle. This will make the grid’s energy supply variable and hard to predict. Additional resources, including batteries and backup power generators, will need to be incorporated to regulate supply. Extreme weather events, which are becoming more common with climate change, can further strain both supply and demand. Managing a renewables-driven grid will require algorithms that can minimize uncertainty in the face of constant, sometimes random fluctuations to make better predictions of supply and demand, guide how resources are added to the grid, and inform how those resources are committed and dispatched across the entire United States.

      “The problem of managing supply and demand in the grid has to happen every second throughout the year, and given how much we rely on electricity in society, we need to get this right,” Botterud says. “You cannot let the reliability drop as you increase the amount of renewables, especially because I think that will lead to resistance towards adopting renewables.”

      That is why Botterud feels fortunate to be working on the decarbonization problem at LIDS — even though a career here is not something he had originally planned. Botterud’s first experience with MIT came during his time as a graduate student in his home country of Norway, when he spent a year as a visiting student with what is now called the MIT Energy Initiative. He might never have returned, except that while at MIT, Botterud met his future wife, Bilge Yildiz. The pair both ended up working at the Argonne National Laboratory outside of Chicago, with Botterud focusing on challenges related to power systems and electricity markets. Then Yildiz got a faculty position at MIT, where she is a professor of nuclear and materials science and engineering. Botterud moved back to the Cambridge area with her and continued to work for Argonne remotely, but he also kept an eye on local opportunities. Eventually, a position at LIDS became available, and Botterud took it, while maintaining his connections to Argonne.

      “At first glance, it may not be an obvious fit,” Botterud says. “My work is very focused on a specific application, power system challenges, and LIDS tends to be more focused on fundamental methods to use across many different application areas. However, being at LIDS, my lab [the Energy Analytics Group] has access to the most recent advances in these fundamental methods, and we can apply them to power and energy problems. Other people at LIDS are working on energy too, so there is growing momentum to address these important problems.”

      Weather, space, and time

      Much of Botterud’s research involves optimization, using mathematical programming to compare alternatives and find the best solution. Common computational challenges include dealing with large geographical areas that contain regions with different weather, different types and quantities of renewable energy available, and different infrastructure and consumer needs — such as the entire United States. Another challenge is the need for granular time resolution, sometimes even down to the sub-second level, to account for changes in energy supply and demand.

      Often, Botterud’s group will use decomposition to solve such large problems piecemeal and then stitch together solutions. However, it’s also important to consider systems as a whole. For example, in a recent paper, Botterud’s lab looked at the effect of building new transmission lines as part of national decarbonization. They modeled solutions assuming coordination at the state, regional, or national level, and found that the more regions coordinate to build transmission infrastructure and distribute electricity, the less they will need to spend to reach zero carbon.

      In other projects, Botterud uses game theory approaches to study strategic interactions in electricity markets. For example, he has designed agent-based models to analyze electricity markets. These assume each actor will make strategic decisions in their own best interest and then simulate interactions between them. Interested parties can use the models to see what would happen under different conditions and market rules, which may lead companies to make different investment decisions, or governing bodies to issue different regulations and incentives. These choices can shape how quickly the grid gets decarbonized.

      Botterud is also collaborating with researchers in MIT’s chemical engineering department who are working on improving battery storage technologies. Batteries will help manage variable renewable energy supply by capturing surplus energy during periods of high generation to release during periods of insufficient generation. Botterud’s group models the sort of charge cycles that batteries are likely to experience in the power grid, so that chemical engineers in the lab can test their batteries’ abilities in more realistic scenarios. In turn, this also leads to a more realistic representation of batteries in power system optimization models.

      These are only some of the problems that Botterud works on. He enjoys the challenge of tackling a spectrum of different projects, collaborating with everyone from engineers to architects to economists. He also believes that such collaboration leads to better solutions. The problems created by climate change are myriad and complex, and solving them will require researchers to cooperate and explore.

      “In order to have a real impact on interdisciplinary problems like energy and climate,” Botterud says, “you need to get outside of your research sweet spot and broaden your approach.” More

    • 75 Shares109 Views

      in Security

      Tackling counterfeit seeds with “unclonable” labels

      by

      Average crop yields in Africa are consistently far below those expected, and one significant reason is the prevalence of counterfeit seeds whose germination rates are far lower than those of the genuine ones. The World Bank estimates that as much as half of all seeds sold in some African countries are fake, which could help to account for crop production that is far below potential.

      There have been many attempts to prevent this counterfeiting through tracking labels, but none have proved effective; among other issues, such labels have been vulnerable to hacking because of the deterministic nature of their encoding systems. But now, a team of MIT researchers has come up with a kind of tiny, biodegradable tag that can be applied directly to the seeds themselves, and that provides a unique randomly created code that cannot be duplicated.

      The new system, which uses minuscule dots of silk-based material, each containing a unique combination of different chemical signatures, is described today in the journal Science Advances in a paper by MIT’s dean of engineering Anantha Chandrakasan, professor of civil and environmental engineering Benedetto Marelli, postdoc Hui Sun, and graduate student Saurav Maji.

      The problem of counterfeiting is an enormous one globally, the researchers point out, affecting everything from drugs to luxury goods, and many different systems have been developed to try to combat this. But there has been less attention to the problem in the area of agriculture, even though the consequences can be severe. In sub-Saharan Africa, for example, the World Bank estimates that counterfeit seeds are a significant factor in crop yields that average less than one-fifth of the potential for maize, and less than one-third for rice.

      Marelli explains that a key to the new system is creating a randomly-produced physical object whose exact composition is virtually impossible to duplicate. The labels they create “leverage randomness and uncertainty in the process of application, to generate unique signature features that can be read, and that cannot be replicated,” he says.

      What they’re dealing with, Sun adds, “is the very old job of trying, basically, not to get your stuff stolen. And you can try as much as you can, but eventually somebody is always smart enough to figure out how to do it, so nothing is really unbreakable. But the idea is, it’s almost impossible, if not impossible, to replicate it, or it takes so much effort that it’s not worth it anymore.”

      The idea of an “unclonable” code was originally developed as a way of protecting the authenticity of computer chips, explains Chandrakasan, who is the Vannevar Bush Professor of Electrical Engineering and Computer Science. “In integrated circuits, individual transistors have slightly different properties coined device variations,” he explains, “and you could then use that variability and combine that variability with higher-level circuits to create a unique ID for the device. And once you have that, then you can use that unique ID as a part of a security protocol. Something like transistor variability is hard to replicate from device to device, so that’s what gives it its uniqueness, versus storing a particular fixed ID.” The concept is based on what are known as physically unclonable functions, or PUFs.

      The team decided to try to apply that PUF principle to the problem of fake seeds, and the use of silk proteins was a natural choice because the material is not only harmless to the environment but also classified by the Food and Drug Administration in the “generally recognized as safe” category, so it requires no special approval for use on food products.

      “You could coat it on top of seeds,” Maji says, “and if you synthesize silk in a certain way, it will also have natural random variations. So that’s the idea, that every seed or every bag could have a unique signature.”

      Developing effective secure system solutions has long been one of Chandrakasan’s specialties, while Marelli has spent many years developing systems for applying silk coatings to a variety of fruits, vegetables, and seeds, so their collaboration was a natural for developing such a silk-based coding system toward enhanced security.

      “The challenge was what type of form factor to give to silk,” Sun says, “so that it can be fabricated very easily.” They developed a simple drop-casting approach that produces tags that are less than one-tenth of an inch in diameter. The second challenge was to develop “a way where we can read the uniqueness, in also a very high throughput and easy way.”

      For the unique silk-based codes, Marelli says, “eventually we found a way to add a color to these microparticles so that they assemble in random structures.” The resulting unique patterns can be read out not only by a spectrograph or a portable microscope, but even by an ordinary cellphone camera with a macro lens. This image can be processed locally to generate the PUF code and then sent to the cloud and compared with a secure database to ensure the authenticity of the product. “It’s random so that people cannot easily replicate it,” says Sun. “People cannot predict it without measuring it.”

      And the number of possible permutations that could result from the way they mix four basic types of colored silk nanoparticles is astronomical. “We were able to show that with a minimal amount of silk, we were able to generate 128 random bits of security,” Maji says. “So this gives rise to 2 to the power 128 possible combinations, which is extremely difficult to crack given the computational capabilities of the state-of-the-art computing systems.”

      Marelli says that “for us, it’s a good test bed in order to think out-of-the-box, and how we can have a path that somehow is more democratic.” In this case, that means “something that you can literally read with your phone, and you can fabricate by simply drop casting a solution, without using any advanced manufacturing technique, without going in a clean room.”

      Some additional work will be needed to make this a practical commercial product, Chandrakasan says. “There will have to be a development for at-scale reading” via smartphones. “So, that’s clearly a future opportunity.” But the principle now shows a clear path to the day when “a farmer could at least, maybe not every seed, but could maybe take some random seeds in a particular batch and verify them,” he says.

      The research was partially supported by the U.S. Office of Naval research and the National Science Foundation, Analog Devices Inc., an EECS Mathworks fellowship, and a Paul M. Cook Career Development Professorship. More

    • 150 Shares169 Views

      in Environment

      Detailed images from space offer clearer picture of drought effects on plants

      by

      “MIT is a place where dreams come true,” says César Terrer, an assistant professor in the Department of Civil and Environmental Engineering. Here at MIT, Terrer says he’s given the resources needed to explore ideas he finds most exciting, and at the top of his list is climate science. In particular, he is interested in plant-soil interactions, and how the two can mitigate impacts of climate change. In 2022, Terrer received seed grant funding from the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) to produce drought monitoring systems for farmers. The project is leveraging a new generation of remote sensing devices to provide high-resolution plant water stress at regional to global scales.

      Growing up in Granada, Spain, Terrer always had an aptitude and passion for science. He studied environmental science at the University of Murcia, where he interned in the Department of Ecology. Using computational analysis tools, he worked on modeling species distribution in response to human development. Early on in his undergraduate experience, Terrer says he regarded his professors as “superheroes” with a kind of scholarly prowess. He knew he wanted to follow in their footsteps by one day working as a faculty member in academia. Of course, there would be many steps along the way before achieving that dream. 

      Upon completing his undergraduate studies, Terrer set his sights on exciting and adventurous research roles. He thought perhaps he would conduct field work in the Amazon, engaging with native communities. But when the opportunity arose to work in Australia on a state-of-the-art climate change experiment that simulates future levels of carbon dioxide, he headed south to study how plants react to CO2 in a biome of native Australian eucalyptus trees. It was during this experience that Terrer started to take a keen interest in the carbon cycle and the capacity of ecosystems to buffer rising levels of CO2 caused by human activity.

      Around 2014, he began to delve deeper into the carbon cycle as he began his doctoral studies at Imperial College London. The primary question Terrer sought to answer during his PhD was “will plants be able to absorb predicted future levels of CO2 in the atmosphere?” To answer the question, Terrer became an early adopter of artificial intelligence, machine learning, and remote sensing to analyze data from real-life, global climate change experiments. His findings from these “ground truth” values and observations resulted in a paper in the journal Science. In it, he claimed that climate models most likely overestimated how much carbon plants will be able to absorb by the end of the century, by a factor of three. 

      After postdoctoral positions at Stanford University and the Universitat Autonoma de Barcelona, followed by a prestigious Lawrence Fellowship, Terrer says he had “too many ideas and not enough time to accomplish all those ideas.” He knew it was time to lead his own group. Not long after applying for faculty positions, he landed at MIT. 

      New ways to monitor drought

      Terrer is employing similar methods to those he used during his PhD to analyze data from all over the world for his J-WAFS project. He and postdoc Wenzhe Jiao collect data from remote sensing satellites and field experiments and use machine learning to come up with new ways to monitor drought. Terrer says Jiao is a “remote sensing wizard,” who fuses data from different satellite products to understand the water cycle. With Jiao’s hydrology expertise and Terrer’s knowledge of plants, soil, and the carbon cycle, the duo is a formidable team to tackle this project.

      According to the U.N. World Meteorological Organization, the number and duration of droughts has increased by 29 percent since 2000, as compared to the two previous decades. From the Horn of Africa to the Western United States, drought is devastating vegetation and severely stressing water supplies, compromising food production and spiking food insecurity. Drought monitoring can offer fundamental information on drought location, frequency, and severity, but assessing the impact of drought on vegetation is extremely challenging. This is because plants’ sensitivity to water deficits varies across species and ecosystems. 

      Terrer and Jiao are able to obtain a clearer picture of how drought is affecting plants by employing the latest generation of remote sensing observations, which offer images of the planet with incredible spatial and temporal resolution. Satellite products such as Sentinel, Landsat, and Planet can provide daily images from space with such high resolution that individual trees can be discerned. Along with the images and datasets from satellites, the team is using ground-based observations from meteorological data. They are also using the MIT SuperCloud at MIT Lincoln Laboratory to process and analyze all of the data sets. The J-WAFS project is among one of the first to leverage high-resolution data to quantitatively measure plant drought impacts in the United States with the hopes of expanding to a global assessment in the future.

      Assisting farmers and resource managers 

      Every week, the U.S. Drought Monitor provides a map of drought conditions in the United States. The map has zero resolution and is more of a drought recap or summary, unable to predict future drought scenarios. The lack of a comprehensive spatiotemporal evaluation of historic and future drought impacts on global vegetation productivity is detrimental to farmers both in the United States and worldwide.  

      Terrer and Jiao plan to generate metrics for plant water stress at an unprecedented resolution of 10-30 meters. This means that they will be able to provide drought monitoring maps at the scale of a typical U.S. farm, giving farmers more precise, useful data every one to two days. The team will use the information from the satellites to monitor plant growth and soil moisture, as well as the time lag of plant growth response to soil moisture. In this way, Terrer and Jiao say they will eventually be able to create a kind of “plant water stress forecast” that may be able to predict adverse impacts of drought four weeks in advance. “According to the current soil moisture and lagged response time, we hope to predict plant water stress in the future,” says Jiao. 

      The expected outcomes of this project will give farmers, land and water resource managers, and decision-makers more accurate data at the farm-specific level, allowing for better drought preparation, mitigation, and adaptation. “We expect to make our data open-access online, after we finish the project, so that farmers and other stakeholders can use the maps as tools,” says Jiao. 

      Terrer adds that the project “has the potential to help us better understand the future states of climate systems, and also identify the regional hot spots more likely to experience water crises at the national, state, local, and tribal government scales.” He also expects the project will enhance our understanding of global carbon-water-energy cycle responses to drought, with applications in determining climate change impacts on natural ecosystems as a whole. More

    • 88 Shares149 Views

      in Energy

      Integrating humans with AI in structural design

      by

      Modern fabrication tools such as 3D printers can make structural materials in shapes that would have been difficult or impossible using conventional tools. Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device.

      But such “black box” automated systems often fall short of producing designs that are fully optimized for their purpose, such as providing the greatest strength in proportion to weight or minimizing the amount of material needed to support a given load. Fully manual design, on the other hand, is time-consuming and labor-intensive.

      Now, researchers at MIT have found a way to achieve some of the best of both of these approaches. They used an automated design system but stopped the process periodically to allow human engineers to evaluate the work in progress and make tweaks or adjustments before letting the computer resume its design process. Introducing a few of these iterations produced results that performed better than those designed by the automated system alone, and the process was completed more quickly compared to the fully manual approach.

      The results are reported this week in the journal Structural and Multidisciplinary Optimization, in a paper by MIT doctoral student Dat Ha and assistant professor of civil and environmental engineering Josephine Carstensen.

      The basic approach can be applied to a broad range of scales and applications, Carstensen explains, for the design of everything from biomedical devices to nanoscale materials to structural support members of a skyscraper. Already, automated design systems have found many applications. “If we can make things in a better way, if we can make whatever we want, why not make it better?” she asks.

      “It’s a way to take advantage of how we can make things in much more complex ways than we could in the past,” says Ha, adding that automated design systems have already begun to be widely used over the last decade in automotive and aerospace industries, where reducing weight while maintaining structural strength is a key need.

      “You can take a lot of weight out of components, and in these two industries, everything is driven by weight,” he says. In some cases, such as internal components that aren’t visible, appearance is irrelevant, but for other structures aesthetics may be important as well. The new system makes it possible to optimize designs for visual as well as mechanical properties, and in such decisions the human touch is essential.

      As a demonstration of their process in action, the researchers designed a number of structural load-bearing beams, such as might be used in a building or a bridge. In their iterations, they saw that the design has an area that could fail prematurely, so they selected that feature and required the program to address it. The computer system then revised the design accordingly, removing the highlighted strut and strengthening some other struts to compensate, and leading to an improved final design.

      The process, which they call Human-Informed Topology Optimization, begins by setting out the needed specifications — for example, a beam needs to be this length, supported on two points at its ends, and must support this much of a load. “As we’re seeing the structure evolve on the computer screen in response to initial specification,” Carstensen says, “we interrupt the design and ask the user to judge it. The user can select, say, ‘I’m not a fan of this region, I’d like you to beef up or beef down this feature size requirement.’ And then the algorithm takes into account the user input.”

      While the result is not as ideal as what might be produced by a fully rigorous yet significantly slower design algorithm that considers the underlying physics, she says it can be much better than a result generated by a rapid automated design system alone. “You don’t get something that’s quite as good, but that was not necessarily the goal. What we can show is that instead of using several hours to get something, we can use 10 minutes and get something much better than where we started off.”

      The system can be used to optimize a design based on any desired properties, not just strength and weight. For example, it can be used to minimize fracture or buckling, or to reduce stresses in the material by softening corners.

      Carstensen says, “We’re not looking to replace the seven-hour solution. If you have all the time and all the resources in the world, obviously you can run these and it’s going to give you the best solution.” But for many situations, such as designing replacement parts for equipment in a war zone or a disaster-relief area with limited computational power available, “then this kind of solution that catered directly to your needs would prevail.”

      Similarly, for smaller companies manufacturing equipment in essentially “mom and pop” businesses, such a simplified system might be just the ticket. The new system they developed is not only simple and efficient to run on smaller computers, but it also requires far less training to produce useful results, Carstensen says. A basic two-dimensional version of the software, suitable for designing basic beams and structural parts, is freely available now online, she says, as the team continues to develop a full 3D version.

      “The potential applications of Prof Carstensen’s research and tools are quite extraordinary,” says Christian Málaga-Chuquitaype, a professor of civil and environmental engineering at Imperial College London, who was not associated with this work. “With this work, her group is paving the way toward a truly synergistic human-machine design interaction.”

      “By integrating engineering ‘intuition’ (or engineering ‘judgement’) into a rigorous yet computationally efficient topology optimization process, the human engineer is offered the possibility of guiding the creation of optimal structural configurations in a way that was not available to us before,” he adds. “Her findings have the potential to change the way engineers tackle ‘day-to-day’ design tasks.” More

    • 100 Shares129 Views

      in Energy

      Machine learning facilitates “turbulence tracking” in fusion reactors

      by

      Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.

      A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions.

      Monitoring the formation and movements of these structures, called filaments or “blobs,” is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics. Individual blob information must be tracked by marking them manually in video data. 

      The researchers built a synthetic video dataset of plasma turbulence to make this process more effective and efficient. They used it to train four computer vision models, each of which identifies and tracks blobs. They trained the models to pinpoint blobs in the same ways that humans would.

      When the researchers tested the trained models using real video clips, the models could identify blobs with high accuracy — more than 80 percent in some cases. The models were also able to effectively estimate the size of blobs and the speeds at which they moved.

      Because millions of video frames are captured during just one fusion experiment, using machine-learning models to track blobs could give scientists much more detailed information.

      “Before, we could get a macroscopic picture of what these structures are doing on average. Now, we have a microscope and the computational power to analyze one event at a time. If we take a step back, what this reveals is the power available from these machine-learning techniques, and ways to use these computational resources to make progress,” says Theodore Golfinopoulos, a research scientist at the MIT Plasma Science and Fusion Center and co-author of a paper detailing these approaches.

      His fellow co-authors include lead author Woonghee “Harry” Han, a physics PhD candidate; senior author Iddo Drori, a visiting professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL), faculty associate professor at Boston University, and adjunct at Columbia University; as well as others from the MIT Plasma Science and Fusion Center, the MIT Department of Civil and Environmental Engineering, and the Swiss Federal Institute of Technology at Lausanne in Switzerland. The research appears today in Nature Scientific Reports.

      Heating things up

      For more than 70 years, scientists have sought to use controlled thermonuclear fusion reactions to develop an energy source. To reach the conditions necessary for a fusion reaction, fuel must be heated to temperatures above 100 million degrees Celsius. (The core of the sun is about 15 million degrees Celsius.)

      A common method for containing this super-hot fuel, called plasma, is to use a tokamak. These devices utilize extremely powerful magnetic fields to hold the plasma in place and control the interaction between the exhaust heat from the plasma and the reactor walls.

      However, blobs appear like filaments falling out of the plasma at the very edge, between the plasma and the reactor walls. These random, turbulent structures affect how energy flows between the plasma and the reactor.

      “Knowing what the blobs are doing strongly constrains the engineering performance that your tokamak power plant needs at the edge,” adds Golfinopoulos.

      Researchers use a unique imaging technique to capture video of the plasma’s turbulent edge during experiments. An experimental campaign may last months; a typical day will produce about 30 seconds of data, corresponding to roughly 60 million video frames, with thousands of blobs appearing each second. This makes it impossible to track all blobs manually, so researchers rely on average sampling techniques that only provide broad characteristics of blob size, speed, and frequency.

      “On the other hand, machine learning provides a solution to this by blob-by-blob tracking for every frame, not just average quantities. This gives us much more knowledge about what is happening at the boundary of the plasma,” Han says.

      He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.

      Simulating blobs

      To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.

      “Sometimes they change direction or speed, sometimes multiple blobs merge, or they split apart. These kinds of events were not considered before with traditional approaches, but we could freely simulate those behaviors in the synthetic data,” Han says.

      Creating synthetic data also allowed them to label each blob, which made the training process more effective, Drori adds.

      Using these synthetic data, they trained the models to draw boundaries around blobs, teaching them to closely mimic what a human scientist would draw.

      Then they tested the models using real video data from experiments. First, they measured how closely the boundaries the models drew matched up with actual blob contours.

      But they also wanted to see if the models predicted objects that humans would identify. They asked three human experts to pinpoint the centers of blobs in video frames and checked to see if the models predicted blobs in those same locations.

      The models were able to draw accurate blob boundaries, overlapping with brightness contours which are considered ground-truth, about 80 percent of the time. Their evaluations were similar to those of human experts, and successfully predicted the theory-defined regime of the blob, which agrees with the results from a traditional method.

      Now that they have shown the success of using synthetic data and computer vision models for tracking blobs, the researchers plan to apply these techniques to other problems in fusion research, such as estimating particle transport at the boundary of a plasma, Han says.

      They also made the dataset and models publicly available, and look forward to seeing how other research groups apply these tools to study the dynamics of blobs, says Drori.

      “Prior to this, there was a barrier to entry that mostly the only people working on this problem were plasma physicists, who had the datasets and were using their methods. There is a huge machine-learning and computer-vision community. One goal of this work is to encourage participation in fusion research from the broader machine-learning community toward the broader goal of helping solve the critical problem of climate change,” he adds.

      This research is supported, in part, by the U.S. Department of Energy and the Swiss National Science Foundation. More

    • 113 Shares189 Views

      in Security

      Two first-year students named Rise Global Winners for 2022

      by

      In 2019, former Google CEO Eric Schmidt and his wife, Wendy, launched a $1 billion philanthropic commitment to identify global talent. Part of that effort is the Rise initiative, which selects 100 young scholars, ages 15-17, from around the world who show unusual promise and a drive to serve others. This year’s cohort of 100 Rise Global Winners includes two MIT first-year students, Jacqueline Prawira and Safiya Sankari.

      Rise intentionally targets younger-aged students and focuses on identifying what the program terms “hidden brilliance” in any form, anywhere in the world, whether it be in a high school or a refugee camp. Another defining aspect of the program is that Rise winners receive sustained support — not just in secondary school, but throughout their lives.

      “We believe that the answers to the world’s toughest problems lie in the imagination of the world’s brightest minds,” says Eric Braverman, CEO of Schmidt Futures, which manages Rise along with the Rhodes Trust. “Rise is an integral part of our mission to create the best, largest, and most enduring pipeline of exceptional talent globally and match it to opportunities to serve others for life.”

      The Rise program creates this enduring pipeline by providing a lifetime of benefits, including funding, programming, and mentoring opportunities. These resources can be tailored to each person as they evolve throughout their career. In addition to a four-year college scholarship, winners receive mentoring and career services; networking opportunities with other Rise recipients and partner organizations; technical equipment such as laptops or tablets; courses on topics like leadership and human-centered design; and opportunities to apply for graduate scholarships and for funding throughout their careers to support their innovative ideas, such as grants or seed money to start a social enterprise.

      Prawira and Sankari’s winning service projects focus on global sustainability and global medical access, respectively. Prawira invented a way to use upcycled fish-scale waste to absorb heavy metals in wastewater. She first started experimenting with fish-scale waste in middle school to try to find a bio-based alternative to plastic. More recently, she discovered that the calcium salts and collagen in fish scales can absorb up to 82 percent of heavy metals from water, and 91 percent if an electric current is passed through the water. Her work has global implications for treating contaminated water at wastewater plants and in developing countries.

      Prawiri published her research in 2021 and has won awards from the U.S. Environmental Protection Agency and several other organizations. She’s planning to major in Course 3 (materials science and engineering), perhaps with an environmentally related minor. “I believe that sustainability and solving environmental problems requires a multifaced approach,” she says. “Creating greener materials for use in our daily lives will have a major impact in solving current environmental issues.”

      For Sankari’s service project, she developed an algorithm to analyze data from electronic nano-sensor devices, or e-noses, which can detect certain diseases from a patient’s breath. The devices are calibrated to detect volatile organic compound biosignatures that are indicative of diseases like diabetes and cancer. “E-nose disease detection is much faster and cheaper than traditional methods of diagnosis, making medical care more accessible to many,” she explains. The Python-based algorithm she created can translate raw data from e-noses into a result that the user can read.

      Sankari is a lifetime member of the American Junior Academy of Science and has been a finalist in several prestigious science competitions. She is considering a major in Course 6-7 (computer science and molecular biology) at MIT and hopes to continue to explore the intersection between nanotechnology and medicine.

      While the 2022 Rise recipients share a desire to tackle some of the world’s most intractable problems, their ideas and interests, as reflected by their service projects, are broad, innovative, and diverse. A winner from Belarus used bioinformatics to predict the molecular effect of a potential Alzheimer’s drug. A Romanian student created a magazine that aims to promote acceptance of transgender bodies. A Vietnamese teen created a prototype of a toothbrush that uses a nano chip to detect cancerous cells in saliva. And a recipient from the United States designed modular, tiny homes for the unhoused that are affordable and sustainable, as an alternative to homeless shelters.

      This year’s winners were selected from over 13,000 applicants from 47 countries, from Azerbaijan and Burkina Faso to Lebanon and Paraguay. The selection process includes group interviews, peer and expert review of each applicant’s service project, and formal talent assessments. More

    • 75 Shares149 Views

      in Energy

      Simulating neutron behavior in nuclear reactors

      by

      Amelia Trainer applied to MIT because she lost a bet.

      As part of what the fourth-year nuclear science and engineering (NSE) doctoral student labels her “teenage rebellious phase,” Trainer was quite convinced she would just be wasting the application fee were she to submit an application. She wasn’t even “super sure” she wanted to go to college. But a high-school friend was convinced Trainer would get into a “top school” if she only applied. A bet followed: If Trainer lost, she would have to apply to MIT. Trainer lost — and is glad she did.

      Growing up in Daytona Beach, Florida, good grades were Trainer’s thing. Seeing friends participate in interschool math competitions, Trainer decided she would tag along and soon found she loved them. She remembers being adept at reading the room: If teams were especially struggling over a problem, Trainer figured the answer had to be something easy, like zero or one. “The hardest problems would usually have the most goofball answers,” she laughs.

      Simulating neutron behavior

      As a doctoral student, hard problems in math, specifically computational reactor physics, continue to be Trainer’s forte.

      Her research, under the guidance of Professor Benoit Forget in MIT NSE’s Computational Reactor Physics Group (CRPG), focuses on modeling complicated neutron behavior in reactors. Simulation helps forecast the behavior of reactors before millions of dollars sink into development of a potentially uneconomical unit. Using simulations, Trainer can see “where the neutrons are going, how much heat is being produced, and how much power the reactor can generate.” Her research helps form the foundation for the next generation of nuclear power plants.

      To simulate neutron behavior inside of a nuclear reactor, you first need to know how neutrons will interact with the various materials inside the system. These neutrons can have wildly different energies, thereby making them susceptible to different physical phenomena. For the entirety of her graduate studies, Trainer has been primarily interested in the physics regarding slow-moving neutrons and their scattering behavior.

      When a slow neutron scatters off of a material, it can induce or cancel out molecular vibrations between the material’s atoms. The effect that material vibrations can have on neutron energies, and thereby on reactor behavior, has been heavily approximated over the years. Trainer is primarily interested in chipping away at these approximations by creating scattering data for materials that have historically been misrepresented and by exploring new techniques for preparing slow-neutron scattering data.

      Trainer remembers waiting for a simulation to complete in the early days of the Covid-19 pandemic, when she discovered a way to predict neutron behavior with limited input data. Traditionally, “people have to store large tables of what neutrons will do under specific circumstances,” she says. “I’m really happy about it because it’s this really cool method of sampling what your neutron does from very little information,” Trainer says.

      Amelia Trainer — Modeling complicated neutron behavior in nuclear reactors

      As part of her research, Trainer often works closely with two software packages: OpenMC and NJOY. OpenMC is a Monte Carlo neutron transport simulation code that was developed in the CRPG and is used to simulate neutron behavior in reactor systems. NJOY is a nuclear data processing tool, and is used to create, augment, and prepare material data that is fed into tools like OpenMC. By editing both these codes to her specifications, Trainer is able to observe the effect that “upstream” material data has on the “downstream” reactor calculations. Through this, she hopes to identify additional problems: approximations that could lead to a noticeable misrepresentation of the physics.

      A love of geometry and poetry

      Trainer discovered the coolness of science as a child. Her mother, who cares for indoor plants and runs multiple greenhouses, and her father, a blacksmith and farrier, who explored materials science through his craft, were self-taught inspirations.

      Trainer’s father urged his daughter to learn and pursue any topics that she found exciting and encouraged her to read poems from “Calvin and Hobbes” out loud when she struggled with a speech impediment in early childhood. Reading the same passages every day helped her memorize them. “The natural manifestation of that extended into [a love of] poetry,” Trainer says.

      A love of poetry, combined with Trainer’s propensity for fun, led her to compose an ode to pi as part of an MIT-sponsored event for alumni. “I was really only in it for the cupcake,” she laughs. (Participants received an indulgent treat).

      Play video

      MIT Matters: A Love Poem to Pi

      Computations and nuclear science

      After being accepted at MIT, Trainer knew she wanted to study in a field that would take her skills at the levels they were at — “my math skills were pretty underdeveloped in the grand scheme of things,” she says. An open-house weekend at MIT, where she met with faculty from the NSE department, and the opportunity to contribute to a discipline working toward clean energy, cemented Trainer’s decision to join NSE.

      As a high schooler, Trainer won a scholarship to Embry-Riddle Aeronautical University to learn computer coding and knew computational physics might be more aligned with her interests. After she joined MIT as an undergraduate student in 2014, she realized that the CRPG, with its focus on coding and modeling, might be a good fit. Fortunately, a graduate student from Forget’s team welcomed Trainer’s enthusiasm for research even as an undergraduate first-year. She has stayed with the lab ever since. 

      Research internships at Los Alamos National Laboratory, the creators of NJOY, have furthered Trainer’s enthusiasm for modeling and computational physics. She met a Los Alamos scientist after he presented a talk at MIT and it snowballed into a collaboration where she could work on parts of the NJOY code. “It became a really cool collaboration which led me into a deep dive into physics and data preparation techniques, which was just so fulfilling,” Trainer says. As for what’s next, Trainer was awarded the Rickover fellowship in nuclear engineering by the the Department of Energy’s Naval Reactors Division and will join the program in Pittsburgh after she graduates.

      For many years, Trainer’s cats, Jacques and Monster, have been a constant companion. “Neutrons, computers, and cats, that’s my personality,” she laughs. Work continues to fuel her passion. To borrow a favorite phrase from Spaceman Spiff, Trainer’s favorite “Calvin” avatar, Trainer’s approach to research has invariably been: “Another day, another mind-boggling adventure.” More