More stories

  • in

    Q&A: More-sustainable concrete with machine learning

    As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

    Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

    Q: What applications does concrete have, and what properties make it a preferred building material?

    Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

    Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

    Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

    While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

    Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

    Q: Why is this problem important to address now, at this point in history?

    Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

    Q: How are you planning to address this problem to produce better concrete?

    Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

    Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

    Q: What kinds of AI and computational techniques are you employing for this?

    Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

    Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

    We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

    Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

    Q: How do you balance cost-benefit trade-offs?

    Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

    Q: What kinds of challenges do you face in this work?

    Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

    Q: What do you hope to achieve through this work?

    Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

    Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

    Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

    This research is supported by the MIT-IBM Watson AI Lab. More

  • in

    SMART researchers develop method for early detection of bacterial infection in crops

    Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) ofSingapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and their local collaborators from Temasek Life Sciences Laboratory (TLL), have developed a rapid Raman spectroscopy-based method for detecting and quantifying early bacterial infection in crops. The Raman spectral biomarkers and diagnostic algorithm enable the noninvasive and early diagnosis of bacterial infections in crop plants, which can be critical for the progress of plant disease management and agricultural productivity.

    Due to the increasing demand for global food supply and security, there is a growing need to improve agricultural production systems and increase crop productivity. Globally, bacterial pathogen infection in crop plants is one of the major contributors to agricultural yield losses. Climate change also adds to the problem by accelerating the spread of plant diseases. Hence, developing methods for rapid and early detection of pathogen-infected crops is important to improve plant disease management and reduce crop loss.

    The breakthrough by SMART and TLL researchers offers a faster and more accurate method to detect bacterial infection in crop plants at an earlier stage, as compared to existing techniques. The new results appear in a paper titled “Rapid detection and quantification of plant innate immunity response using Raman spectroscopy” published in the journal Frontiers in Plant Science.

    “The early detection of pathogen-infected crop plants is a significant step to improve plant disease management,” says Chua Nam Hai, DiSTAP co-lead principal investigator, professor, TLL deputy chair, and co-corresponding author. “It will allow the fast and selective removal of pathogen load and curb the further spread of disease to other neighboring crops.”

    Traditionally, plant disease diagnosis involves a simple visual inspection of plants for disease symptoms and severity. “Visual inspection methods are often ineffective, as disease symptoms usually manifest only at relatively later stages of infection, when the pathogen load is already high and reparative measures are limited. Hence, new methods are required for rapid and early detection of bacterial infection. The idea would be akin to having medical tests to identify human diseases at an early stage, instead of waiting for visual symptoms to show, so that early intervention or treatment can be applied,” says MIT Professor Rajeev Ram, who is a DiSTAP principal investigator and co-corresponding author on the paper.

    While existing techniques, such as current molecular detection methods, can detect bacterial infection in plants, they are often limited in their use. Molecular detection methods largely depend on the availability of pathogen-specific gene sequences or antibodies to identify bacterial infection in crops; the implementation is also time-consuming and nonadaptable for on-site field application due to the high cost and bulky equipment required, making it impractical for use in agricultural farms.

    “At DiSTAP, we have developed a quantitative Raman spectroscopy-based algorithm that can help farmers to identify bacterial infection rapidly. The developed diagnostic algorithm makes use of Raman spectral biomarkers and can be easily implemented in cloud-based computing and prediction platforms. It is more effective than existing techniques as it enables accurate identification and early detection of bacterial infection, both of which are crucial to saving crop plants that would otherwise be destroyed,” explains Gajendra Pratap Singh, scientific director and principal investigator at DiSTAP and co-lead author.

    A portable Raman system can be used on farms and provides farmers with an accurate and simple yes-or-no response when used to test for the presence of bacterial infections in crops. The development of this rapid and noninvasive method could improve plant disease management and have a transformative impact on agricultural farms by efficiently reducing agricultural yield loss and increasing productivity.

    “Using the diagnostic algorithm method, we experimented on several edible plants such as choy sum,” says DiSTAP and TLL principal investigator and co-corresponding author Rajani Sarojam. “The results showed that the Raman spectroscopy-based method can swiftly detect and quantify innate immunity response in plants infected with bacterial pathogens. We believe that this technology will be beneficial for agricultural farms to increase their productivity by reducing their yield loss due to plant diseases.”

    The researchers are currently working on the development of high-throughput, custom-made portable or hand-held Raman spectrometers that will allow Raman spectral analysis to be quickly and easily performed on field-grown crops.

    SMART and TLL developed and discovered the diagnostic algorithm and Raman spectral biomarkers. TLL also confirmed and validated the detection method through mutant plants. The research is carried out by SMART and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

    SMART was established by MIT and the NRF in 2007. The first entity in CREATE developed by NRF, SMART serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Center and five IRGs: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, DiSTAP, Future Urban Mobility, and Low Energy Electronic Systems. SMART research is funded by the NRF under the CREATE program.

    Led by Professor Michael Strano of MIT and Professor Chua Nam Hai of Temasek Lifesciences Laboratory, the DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic, and biomaterial technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients. Scientists from MIT, TTL, Nanyang Technological University, and National University of Singapore are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high-yield density production, and drought and pathogen resistance; and applying these technologies to improve urban farming. More

  • in

    Design’s new frontier

    In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

    “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

    Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

    In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

    Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

    Computational design

    Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

    “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

    For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

    To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

    Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

    Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

    “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

    Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

    Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

    “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

    Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

    Personal fabrication

    Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

    “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

    Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

    First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

    To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

    “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

    Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

    Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

    “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

    Computational fluid dynamics and participatory design

    Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

    As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

    “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

    After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

    The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

    Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

    “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

    These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

    Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

    Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

    The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

    Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

    “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

    While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

    Designing intelligent systems

    When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

    Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

    “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

    For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

    To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

    “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

    Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

    “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

    To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

    In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

    “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

    Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

    Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

    “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

    Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

  • in

    Research collaboration puts climate-resilient crops in sight

    Any houseplant owner knows that changes in the amount of water or sunlight a plant receives can put it under immense stress. A dying plant brings certain disappointment to anyone with a green thumb. 

    But for farmers who make their living by successfully growing plants, and whose crops may nourish hundreds or thousands of people, the devastation of failing flora is that much greater. As climate change is poised to cause increasingly unpredictable weather patterns globally, crops may be subject to more extreme environmental conditions like droughts, fluctuating temperatures, floods, and wildfire. 

    Climate scientists and food systems researchers worry about the stress climate change may put on crops, and on global food security. In an ambitious interdisciplinary project funded by the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), David Des Marais, the Gale Assistant Professor in the Department of Civil and Environmental Engineering at MIT, and Caroline Uhler, an associate professor in the MIT Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society, are investigating how plant genes communicate with one another under stress. Their research results can be used to breed plants more resilient to climate change.

    Crops in trouble

    Governing plants’ responses to environmental stress are gene regulatory networks, or GRNs, which guide the development and behaviors of living things. A GRN may be comprised of thousands of genes and proteins that all communicate with one another. GRNs help a particular cell, tissue, or organism respond to environmental changes by signaling certain genes to turn their expression on or off.

    Even seemingly minor or short-term changes in weather patterns can have large effects on crop yield and food security. An environmental trigger, like a lack of water during a crucial phase of plant development, can turn a gene on or off, and is likely to affect many others in the GRN. For example, without water, a gene enabling photosynthesis may switch off. This can create a domino effect, where the genes that rely on those regulating photosynthesis are silenced, and the cycle continues. As a result, when photosynthesis is halted, the plant may experience other detrimental side effects, like no longer being able to reproduce or defend against pathogens. The chain reaction could even kill a plant before it has the chance to be revived by a big rain.

    Des Marais says he wishes there was a way to stop those genes from completely shutting off in such a situation. To do that, scientists would need to better understand how exactly gene networks respond to different environmental triggers. Bringing light to this molecular process is exactly what he aims to do in this collaborative research effort.

    Solving complex problems across disciplines

    Despite their crucial importance, GRNs are difficult to study because of how complex and interconnected they are. Usually, to understand how a particular gene is affecting others, biologists must silence one gene and see how the others in the network respond. 

    For years, scientists have aspired to an algorithm that could synthesize the massive amount of information contained in GRNs to “identify correct regulatory relationships among genes,” according to a 2019 article in the Encyclopedia of Bioinformatics and Computational Biology. 

    “A GRN can be seen as a large causal network, and understanding the effects that silencing one gene has on all other genes requires understanding the causal relationships among the genes,” says Uhler. “These are exactly the kinds of algorithms my group develops.”

    Des Marais and Uhler’s project aims to unravel these complex communication networks and discover how to breed crops that are more resilient to the increased droughts, flooding, and erratic weather patterns that climate change is already causing globally.

    In addition to climate change, by 2050, the world will demand 70 percent more food to feed a booming population. “Food systems challenges cannot be addressed individually in disciplinary or topic area silos,” says Greg Sixt, J-WAFS’ research manager for climate and food systems. “They must be addressed in a systems context that reflects the interconnected nature of the food system.”

    Des Marais’ background is in biology, and Uhler’s in statistics. “Dave’s project with Caroline was essentially experimental,” says Renee J. Robins, J-WAFS’ executive director. “This kind of exploratory research is exactly what the J-WAFS seed grant program is for.”

    Getting inside gene regulatory networks

    Des Marais and Uhler’s work begins in a windowless basement on MIT’s campus, where 300 genetically identical Brachypodium distachyon plants grow in large, temperature-controlled chambers. The plant, which contains more than 30,000 genes, is a good model for studying important cereal crops like wheat, barley, maize, and millet. For three weeks, all plants receive the same temperature, humidity, light, and water. Then, half are slowly tapered off water, simulating drought-like conditions.

    Six days into the forced drought, the plants are clearly suffering. Des Marais’ PhD student Jie Yun takes tissues from 50 hydrated and 50 dry plants, freezes them in liquid nitrogen to immediately halt metabolic activity, grinds them up into a fine powder, and chemically separates the genetic material. The genes from all 100 samples are then sequenced at a lab across the street.

    The team is left with a spreadsheet listing the 30,000 genes found in each of the 100 plants at the moment they were frozen, and how many copies there were. Uhler’s PhD student Anastasiya Belyaeva inputs the massive spreadsheet into the computer program she developed and runs her novel algorithm. Within a few hours, the group can see which genes were most active in one condition over another, how the genes were communicating, and which were causing changes in others. 

    The methodology captures important subtleties that could allow researchers to eventually alter gene pathways and breed more resilient crops. “When you expose a plant to drought stress, it’s not like there’s some canonical response,” Des Marais says. “There’s lots of things going on. It’s turning this physiologic process up, this one down, this one didn’t exist before, and now suddenly is turned on.” 

    In addition to Des Marais and Uhler’s research, J-WAFS has funded projects in food and water from researchers in 29 departments across all five MIT schools as well as the MIT Schwarzman College of Computing. J-WAFS seed grants typically fund seven to eight new projects every year.

    “The grants are really aimed at catalyzing new ideas, providing the sort of support [for MIT researchers] to be pushing boundaries, and also bringing in faculty who may have some interesting ideas that they haven’t yet applied to water or food concerns,” Robins says. “It’s an avenue for researchers all over the Institute to apply their ideas to water and food.”

    Alison Gold is a student in MIT’s Graduate Program in Science Writing. More

  • in

    J-WAFS announces 2021 Solutions Grants for commercializing water and food technologies

    The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) recently announced the 2021 J-WAFS Solutions grant recipients. The J-WAFS Solutions program aims to propel MIT water- and food-related research toward commercialization. Grant recipients receive one year of financial support, as well as mentorship, networking, and guidance from industry experts, to begin their journey into the commercial world — whether that be in the form of bringing innovative products to market or launching cutting-edge startup companies. 

    This year, three projects will receive funding across water, food, and agriculture spaces. The winning projects will advance nascent technologies for off-grid refrigeration, portable water filtration, and dairy waste recycling. Each provides an efficient, accessible solution to the respective challenge being addressed.

    Since the start of the J-WAFS Solutions program in 2015, grants have provided instrumental support in creating a number of key MIT startups that focus on major water and food challenges. A 2015-16 grant helped the team behind Via Separations develop their business plan to massively decarbonize industrial separations processes. Other successful J-WAFS Solutions alumni include researchers who created a low-cost water filter made from tree branches and the team that launched the startup Xibus Systems, which is developing a handheld food safety sensor.

    “New technological advances are being made at MIT every day, and J-WAFS Solutions grants provide critical resources and support for these technologies to make it to market so that they can transform our local and global water and food systems,” says J-WAFS Executive Director Renee Robins. “This year’s grant recipients offer innovative tools that will provide more accessible food storage for smallholder farmers in places like Africa, safer drinking water, and a new approach to recycling food waste,” Robins notes. She adds, “J-WAFS is excited to work with these teams, and we look forward to seeing their impact on the water and food sectors.”

    The J-WAFS Solutions program is implemented in collaboration with Community Jameel, the global philanthropic organization founded by Mohammed Jameel ’78, and is supported by the MIT Venture Mentoring Service and the iCorps New England Regional Innovation Node at MIT.

    Mobile evaporative cooling rooms for vegetable preservation

    Food waste is a persistent problem across food systems supply chains, as 30-50 percent of food produced is lost before it reaches the table. The problem is compounded in areas without access to the refrigeration necessary to store food after it is harvested. Hot and dry climates in particular struggle to preserve food before it reaches consumers. A team led by Daniel Frey, faculty director for research at MIT D-Lab and professor of mechanical engineering, has pioneered a new approach to enable farmers to better preserve their produce and improve access to nutritious food in the community. The team includes Leon Glicksman, professor of building technology and mechanical engineering, and Eric Verploegen, a research engineer in MIT D-Lab.

    Instead of relying on traditional refrigeration with high energy and cost requirements, the team is utilizing forced-air evaporative cooling chambers. Their design, based on retrofitting shipping containers, will provide a lower-cost, better-performing solution enabling farmers to chill their produce without access to power. The research team was previously funded by J-WAFS through two different grants in 2019 to develop the off-grid technology in collaboration with researchers at the University of Nairobi and the Collectives for Integrated Livelihood Initiatives (CInI), Jamshedpur. Now, the cooling rooms are ready for pilot testing, which the MIT team will conduct with rural farmers in Kenya and India. The MIT team will deploy and test the storage chambers through collaborations with two Kenyan social enterprises and a nongovernmental organization in Gujarat, India. 

    Off-grid portable ion concentration polarization desalination unit

    Shrinking aquifers, polluted rivers, and increased drought are making fresh drinking water increasingly scarce, driving the need for improved desalination technologies. The water purifiers market, which was $45 billion in 2019, is expected to grow to $90.1 billion in 2025. However, current products on the market are limited in scope, in that they are designed to treat water that is already relatively low in salinity, and do not account for lead contamination or other technical challenges. A better solution is required to ensure access to clean and safe drinking water in the face of water shortages. 

    A team led by Jongyoon Han, professor of biological engineering and electrical engineering at MIT, has developed a portable desalination unit that utilizes an ion concentration polarization process. The compact and lightweight unit has the ability to remove dissolved and suspended solids from brackish water at a rate of one liter per hour, both in installed and remote field settings. The unit was featured in an award-winning video in the 2021 J-WAFS World Water Day Video Competition: MIT Research for a Water Secure Future. The team plans to develop the next-generation prototype of the desalination unit alongside a mass-production strategy and business model.

    Converting dairy industry waste into food and feed ingredients

    One of the trendiest foods in the last decade, Greek yogurt, has a hidden dark side: acid whey. This low-pH, liquid by-product of yogurt production has been a growing problem for producers, as untreated disposal of the whey can pose environmental risks due to its high organic content and acidic odor.

    With an estimated 3 million tons of acid whey generated in the United States each year, MIT researchers saw an opportunity to turn waste into a valuable resource for our food systems. Led by the Willard Henry Dow Professor in Chemical Engineering, Gregory Stephanopoulos, and Anthony J. Sinskey, professor of microbiology, the researchers are utilizing metabolic engineering to turn acid whey into carotenoids, the yellow and orange organic pigments found naturally in carrots, autumn leaves, and salmon. The team is hoping that these carotenoids can be utilized as food supplements or feed additives to make the most of what otherwise would have been wasted. More

  • in

    Climate and sustainability classes expand at MIT

    In fall 2019, a new class, 6.S898/12.S992 (Climate Change Seminar), arrived at MIT. It was, at the time, the only course in the Department of Electrical Engineering and Computer Science (EECS) to tackle the science of climate change. The class covered climate models and simulations alongside atmospheric science, policy, and economics.

    Ron Rivest, MIT Institute Professor of Computer Science, was one of the class’s three instructors, with Alan Edelman of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and John Fernández of the Department of Urban Studies and Planning. “Computer scientists have much to contribute to climate science,” Rivest says. “In particular, the modeling and simulation of climate can benefit from advances in computer science.”

    Rivest is one of many MIT faculty members who have been working in recent years to bring topics in climate, sustainability, and the environment to students in a growing variety of fields. And students have said they want this trend to continue.

    “Sustainability is something that touches all disciplines,” says Megan Xu, a rising senior in biological engineering and advisory chair of the Undergraduate Association Sustainability Committee. “As students who have grown up knowing that climate change is real and witnessed climate disaster after disaster, we know this is a huge problem that needs to be addressed by our generation.”

    Expanding the course catalog

    As education program manager at the MIT Environmental Solutions Initiative, Sarah Meyers has repeatedly had a hand in launching new sustainability classes. She has steered grant money to faculty, brought together instructors, and helped design syllabi — all in the service of giving MIT students the same world-class education in climate and sustainability that they get in science and engineering.

    Her work has given Meyers a bird’s-eye view of MIT’s course offerings in this area. By her count, there are now over 120 undergraduate classes, across 23 academic departments, that teach climate, environment, and sustainability principles.

    “Educating the next generation is the most important way that MIT can have an impact on the world’s environmental challenges,” she says. “MIT students are going to be leaders in their fields, whatever they may be. If they really understand sustainable design practices, if they can balance the needs of all stakeholders to make ethical decisions, then that actually changes the way our world operates and can move humanity towards a more sustainable future.”

    Some sustainability classes are established institutions at MIT. Success stories include 2.00A (Fundamentals of Engineering Design: Explore Space, Sea and Earth), a hands-on engineering class popular with first-year students; and 21W.775 (Writing About Nature and Environmental Issues), which has helped undergraduates fulfill their HASS-H (humanities distribution subject) and CI-H (Communication Intensive subject in the Humanities, Arts, and Social Sciences) graduation requirements for 15 years.

    Expanding this list of classes is an institutional priority. In the recently released Climate Action Plan for the Decade, MIT pledged to recruit at least 20 additional faculty members who will teach climate-related classes.

    “I think it’s easy to find classes if you’re looking for sustainability classes to take,” says Naomi Lutz, a senior in mechanical engineering who helped advise the MIT administration on education measures in the Climate Action Plan. “I usually scroll through the titles of the classes in courses 1, 2, 11, and 12 to see if any are of interest. I also have used the Environment & Sustainability Minor class list to look for sustainability-related classes to take.

    “The coming years are critical for the future of our planet, so it’s important that we all learn about sustainability and think about how to address it,” she adds.

    Working with students’ schedules

    Still, despite all this activity, climate and sustainability are not yet mainstream parts of an MIT education. Last year, a survey of over 800 MIT undergraduates, taken by the Undergraduate Association Sustainability Committee, found that only one in four had ever taken a class related to sustainability. But it doesn’t seem to be from lack of interest in the topic. More than half of those surveyed said that sustainability is a factor in their career planning, and almost 80 percent try to practice sustainability in their daily lives.

    “I’ve often had conversations with students who were surprised to learn there are so many classes available,” says Meyers. “We do need to do a better job communicating about them, and making it as easy as possible to enroll.”

    A recurring challenge is helping students fit sustainability into their plans for graduation, which are often tightly mapped-out.

    “We each only have four years — around 32 to 40 classes — to absorb all that we can from this amazing place,” says Xu. “Many of these classes are mandated to be GIRs [General Institute Requirements] and major requirements. Many students recognize that sustainability is important, but might not have the time to devote an entire class to the topic if it would not count toward their requirements.”

    This was a central focus for the students who were involved in forming education recommendations for the Climate Action Plan. “We propose that more sustainability-related courses or tracks are offered in the most common majors, especially in Course 6 [EECS],” says Lutz. “If students can fulfill major requirements while taking courses that address environmental problems, we believe more students will pursue research and careers related to sustainability.”

    She also recommends that students look into the dozens of climate and sustainability classes that fulfill GIRs. “It’s really easy to take sustainability-related courses that fulfill HASS [Humanities, Arts, and Social Sciences] requirements,” she says. For example, students can meet their HASS-S (social sciences sistribution subject) requirement by taking 21H.185 (Environment and History), or fulfill their HASS-A requirement with CMS.374 (Transmedia Art, Extraction and Environmental Justice).

    Classes with impact

    For those students who do seek out sustainability classes early in their MIT careers, the experience can shape their whole education.

    “My first semester at MIT, I took Environment and History, co-taught by professors Susan Solomon and Harriet Ritvo,” says Xu. “It taught me that there is so much more involved than just science and hard facts to solving problems in sustainability and climate. I learned to look at problems with more of a focus on people, which has informed much of the extracurricular work that I’ve gone on to do at MIT.”

    And the faculty, too, sometimes find that teaching in this area opens new doors for them. Rivest, who taught the climate change seminar in Course 6, is now working to build a simplified climate model with his co-instructor Alan Edelman, their teaching assistant Henri Drake, and Professor John Deutch of the Department of Chemistry, who joined the class as a guest lecturer. “I very much enjoyed meeting new colleagues from all around MIT,” Rivest says. “Teaching a class like this fosters connections between computer scientists and climate scientists.”

    Which is why Meyers will continue helping to get these classes off the ground. “We know students think climate is a huge issue for their futures. We know faculty agree with them,” she says. “Everybody wants this to be part of an MIT education. The next step is to really reach out to students and departments to fill the classrooms. That’s the start of a virtuous cycle where enrollment drives more sustainability instruction in every part of MIT.” More

  • in

    MIT Solar Electric Vehicle Team wins 2021 American Solar Challenge

    After three years of hard work, the MIT Solar Electric Vehicle Team took first place at the 2021 American Solar Challenge (ASC) on August 7 in the Single Occupancy Vehicle (SOV) category. During the five-day race, their solar car, Nimbus — designed and built entirely by students — beat eight other SOVs from schools across the country, traversing 1,109 miles and maintaining an average speed of 38.4 miles per hour.

    Held every two years, the ASC has traditionally been a timed event. This year, however, the race was based on the total distance traveled. Each team followed the same prescribed route, from Independence, Missouri, to Las Vegas, New Mexico. But teams could drive additional miles within each of the three stages — if their battery had enough juice to continue. Nimbus surpassed the closest runner-up, the University of Kentucky, by over 100 miles.

    “It’s still a little surreal,” says SEVT captain Aditya Mehrotra, a rising senior in electrical engineering and computer science. “We were all hopeful, but I don’t think you ever go into racing like, ‘We got this.’ It’s more like, ‘We’re going to do our best and see how we fare.’ In this case, we were fortunate enough to do really well. The car worked beautifully, and — more importantly — the team worked beautifully and we learned a lot.”

    Team work makes the dream work

    Two weeks before the ASC race, each solar car was put through its paces in the Formula Sun Grand Prix at Heartland Motorsports Park in Topeka, Kansas. First, vehicles had to perform a series of qualifying challenges, called “scrutineering.” Cars that passed could participate in a track race in hopes of qualifying for ASC. Nimbus placed second, completing a total of 239 laps around the track over three days (equivalent to 597.5 miles).

    In the process, SEVT member and rising junior in mechanical engineering Cameron Kokesh tied the Illinois State driver for the fastest single lap time around the track, clocking in at three minutes and 19 seconds. She’s not one to rest on her laurels, though. “It would be fun to see if we could beat that time at the next race,” she says with a smile.

    Nimbus’s performance at the Formula Sun Grand Prix and ASC is a manifestation of team’s proficiency in not only designing and building a superior solar vehicle, but other skills, as well, including managing logistics, communications, and teamwork. “It’s a huge operation,” says Mehrotra. “It’s not like we drive the car straight down the highway during the race.”

    Indeed, Nimbus travels with an impressive caravan of seven vehicles manned by about two dozen SEVT members. A scout vehicle is at the front, monitoring road and weather conditions, followed by a lead car that oversees navigation. Nimbus is third in the caravan, trailed by a chase vehicle, in which the strategy team manages tasks like monitoring telemetry data, calculating how much power the solar panels are generating and the remaining travel distance, and setting target speeds. Bringing up the rear are the transport truck and trailer, a media car, and “Cupcake,” a support vehicle with food, supplies, and camping gear.

    Leading up to the three-week event, the team devoted three years to designing, building, refining, and testing Nimbus. (The ASC was scheduled for 2020, but it was postponed until this year due to the Covid-19 pandemic.) They spent countless hours in the MIT Edgerton Center’s machine shop in Building N51, making, building, and iterating. They drove the car in the greater-Boston area, up to Salem, Massachusetts, and to Cape Cod. In the spring, they traveled to Palmer Motorsports Park in Palmer, Massachusetts, to practice various components of the race. They performed scrutineering tasks like the slalom test and figure eight test, conducted team operations training to optimize the caravan’s performance, and, of course, the “shakedown.” 

    “Shakedown is just, you drive the car around the track and you basically see what falls off and then you know what you need to fix,” Mehrotra explains. “Hopefully nothing too major falls off!”

    The road ahead

    At the conclusion of the race, Mehotra officially stepped down and handed SEVT’s reins to its new leaders: Kotesh will take the helm as team captain, and rising sophomore Sydney Kim, an ocean engineering major, will serve as vice-captain. The long drive back from the Midwest gave them time to reflect on the win and future plans.

    Although Nimbus performed well, there were a few instructive glitches here and there, mostly during scrutineering. But there was nothing the team couldn’t handle. For example, the canopy latch didn’t always hold, so the clear acrylic bubble covering the driver would pop open. (A little spring adjustment and tape did the trick.) In addition, Nimbus had a tendency to skid when the driver slammed on the brakes. (Driver training, and letting some air out of the tires, improved the traction.)

    Then there were the unpredictable variables, beyond the team’s control. On one day, with little sun, Nimbus had to chug along the highway at a mere 15 miles per hour. And there was the time that the Kansas State Police pulled the entire caravan over. “They didn’t realize we were coming through,” Mehrotra explains.

    Kim thinks one of the keys to the team’s success is that Nimbus is quite reliable. “We didn’t have wheels falling off on the road. Once we got the car rolling, things didn’t go wrong mechanically or electrically. Also, it’s very energy efficient because it’s lightweight and the shape of the vehicle is very aerodynamic. On a nice sunny day, it allows us to drive 40 miles per hour energy-neutral — the battery stays at the same amount of charge as we drive,” she says.

    The next ASC will take place in 2022, so this year the team will focus on refining Nimbus to race it again next summer. Also, they’ve set their sights on building a car to enter in the Multiple Occupancy Vehicle (MOV) class in the 2024 race — something the team has never done. “It will definitely take the three years to build a good car to compete,” Kotesh muses. “But it’s a really good transition period, after doing so well on this race, so our team is excited about it.”

    “It will be challenging for them, but I wouldn’t put it anything past them,” says Patrick McAtamney, the Edgerton Center technical instructor and shop manager who works with all the student clubs and teams, from solar vehicles to Formula race cars to rockets. He attended ASC, too, and has the utmost admiration for SEVT. “It’s totally student-run. They do all the designing and machining themselves. I always tell people that sometimes I feel like my only job is to make sure they have 10 fingers when they leave the shop.”

    In the meantime, before the school year begins, SEVT has another challenge: deciding where to put the trophy. “It’s huge,” McAtamney says. “It’s about the size of the Stanley Cup!” More

  • in

    Amy Watterson: Model engineer

    “I love that we are doing something that no one else is doing.”

    Amy Watterson is excited when she talks about SPARC, the pilot fusion plant being developed by MIT spinoff Commonwealth Fusion Systems (CSF). Since being hired as a mechanical engineer at the Plasma Science and Fusion Center (PSFC) two years ago, Watterson has found her skills stretching to accommodate the multiple needs of the project.

    Fusion, which fuels the sun and stars, has long been sought as a carbon-free energy source for the world. For decades researchers have pursued the “tokamak,” a doughnut-shaped vacuum chamber where hot plasma can be contained by magnetic fields and heated to the point where fusion occurs. Sustaining the fusion reactions long enough to draw energy from them has been a challenge.

    Watterson is intimately aware of this difficulty. Much of her life she has heard the quip, “Fusion is 50 years away and always will be.” The daughter of PSFC research scientist Catherine Fiore, who headed the PSFC’s Office of Environment, Safety and Health, and Reich Watterson, an optical engineer working at the center, she had watched her parents devote years to making fusion a reality. She determined before entering Rensselaer Polytechnic Institute that she could forgo any attempt to follow her parents into a field that might not produce results during her career.

    Working on SPARC has changed her mindset. Taking advantage of a novel high-temperature superconducting tape, SPARC’s magnets will be compact while generating magnetic fields stronger than would be possible from other mid-sized tokamaks, and producing more fusion power. It suggests a high-field device that produces net fusion gain is not 50 years away. SPARC is scheduled to be begin operation in 2025.

    An education in modeling

    Watterson’s current excitement, and focus, is due to an approaching milestone for SPARC: a test of the Toroidal Field Magnet Coil (TFMC), a scaled prototype for the HTS magnets that will surround SPARC’s toroidal vacuum chamber. Its design and manufacture have been shaped by computer models and simulations. As part of a large research team, Waterson has received an education in modeling over the past two years.

    Computer models move scientific experiments forward by allowing researchers to predict what will happen to an experiment — or its materials — if a parameter is changed. Modeling a component of the TFMC, for example, researchers can test how it is affected by varying amounts of current, different temperatures or different materials. With this information they can make choices that will improve the success of the experiment.

    In preparation for the magnet testing, Watterson has modeled aspects of the cryogenic system that will circulate helium gas around the TFMC to keep it cold enough to remain superconducting. Taking into consideration the amount of cooling entering the system, the flow rate of the helium, the resistance created by valves and transfer lines and other parameters, she can model how much helium flow will be necessary to guarantee the magnet stays cold enough. Adjusting a parameter can make the difference between a magnet remaining superconducting and becoming overheated or even damaged.

    Watterson and her teammates have also modeled pressures and stress on the inside of the TFMC. Pumping helium through the coil to cool it down will add 20 atmospheres of pressure, which could create a degree of flex in elements of the magnet that are welded down. Modeling can help determine how much pressure a weld can sustain.

    “How thick does a weld need to be, and where should you put the weld so that it doesn’t break — that’s something you don’t want to leave until you’re finally assembling it,” says Watterson.

    Modeling the behavior of helium is particularly challenging because its properties change significantly as the pressure and temperature change.

    “A few degrees or a little pressure will affect the fluid’s viscosity, density, thermal conductivity, and heat capacity,” says Watterson. “The flow has different pressures and temperatures at different places in the cryogenic loop. You end up with a set of equations that are very dependent on each other, which makes it a challenge to solve.”

    Role model

    Watterson notes that her modeling depends on the contributions of colleagues at the PSFC, and praises the collaborative spirit among researchers and engineers, a community that now feels like family. Her teammates have been her mentors. “I’ve learned so much more on the job in two years than I did in four years at school,” she says.

    She realizes that having her mother as a role model in her own family has always made it easier for her to imagine becoming a scientist or engineer. Tracing her early passion for engineering to a middle school Lego robotics tournament, her eyes widen as she talks about the need for more female engineers, and the importance of encouraging girls to believe they are equal to the challenge.

    “I want to be a role model and tell them ‘I’m a successful engineer, you can be too.’ Something I run into a lot is that little girls will say, ‘I can’t be an engineer, I’m not cut out for that.’ And I say, ‘Well that’s not true. Let me show you. If you can make this Lego robot, then you can be an engineer.’ And it turns out they usually can.”

    Then, as if making an adjustment to one of her computer models, she continues.

    “Actually, they always can.” More