Civil and environmental engineering

Subterms

    More stories

    • 138 Shares169 Views

      in Environment

      MIT ReACT welcomes first Afghan cohort to its largest-yet certificate program

      by

      Through the championing support of the faculty and leadership of the MIT Afghan Working Group convened last September by Provost Martin Schmidt and chaired by Associate Provost for International Activities Richard Lester, MIT has come together to support displaced Afghan learners and scholars in a time of crisis. The MIT Refugee Action Hub (ReACT) has opened opportunities for 25 talented Afghan learners to participate in the hub’s certificate program in computer and data science (CDS), now in its fourth year, welcoming its largest and most diverse cohort to date — 136 learners from 29 countries.

      ”Even in the face of extreme disruption, education and scholarship must continue, and MIT is committed to providing resources and safe forums for displaced scholars,” says Lester. “We greatly appreciate MIT ReACT’s work to create learning opportunities for Afghan students whose lives have been upended by the crisis in their homeland.”

      Currently, more than 3.5 million Afghans are internally displaced, while 2.5 million are registered refugees residing in other parts of the world. With millions in Afghanistan facing famine, poverty, and civil unrest in what has become the world’s largest humanitarian crisis, the United Nations predicts the number of Afghans forced to flee their homes will continue to rise. 

      “Forced displacement is on the rise, fueled not only by constant political, economical, and social turmoil worldwide, but also by the ongoing climate change crisis, which threatens costly disruptions to society and has potential to create unprecedented displacement internationally,” says associate professor of civil and environmental engineering and ReACT’s faculty founder Admir Masic. During the orientation for the new CDS cohort in January, Masic emphasized the great need for educational programs like ReACT’s that address the specific challenges refugees and displaced learners face.

      A former Bosnian refugee, Masic spent his teenage years in Croatia, where educational opportunities were limited for young people with refugee status. His experience motivated him to found ReACT, which launched in 2017. Housed within Open Learning, ReACT is an MIT-wide effort to deliver global education and professional development programs to underserved communities, including refugees and migrants. ReACT’s signature program, CDS is a year-long, online program that combines MITx courses in programming and data science, personal and professional development workshops including MIT Bootcamps, and opportunities for practical experience.

      ReACT’s group of 25 learners from Afghanistan, 52 percent of whom are women, joins the larger CDS cohort in the program. They will receive support from their new colleagues as well as members of ReACT’s mentor and alumni network. While the majority of the group are residing around the world, including in Europe, North America, and neighboring countries, several still remain in Afghanistan. With the support of the Afghan Working Group, ReACT is working to connect with communities from the region to provide safe and inclusive learning environments for the cohort. ​​

      Building community and confidence

      Selected from more than 1,000 applicants, the new CDS cohort reflected on their personal and professional goals during a weeklong orientation.

      “I am here because I want to change my career and learn basics in this field to then obtain networks that I wouldn’t have got if it weren’t for this program,” said Samiullah Ajmal, who is joining the program from Afghanistan.

      Interactive workshops on topics such as leadership development and virtual networking rounded out the week’s events. Members of ReACT’s greater community — which has grown in recent years to include a network of external collaborators including nonprofits, philanthropic supporters, universities, and alumni — helped facilitate these workshops and other orientation activities.

      For instance, Na’amal, a social enterprise that connects refugees to remote work opportunities, introduced the CDS learners to strategies for making career connections remotely. “We build confidence while doing,” says Susan Mulholland, a leadership and development coach with Na’amal who led the networking workshop.

      Along with the CDS program’s cohort-based model, ReACT also uses platforms that encourage regular communication between participants and with the larger ReACT network — making connections a critical component of the program.

      “I not only want to meet new people and make connections for my professional career, but I also want to test my communication and social skills,” says Pablo Andrés Uribe, a learner who lives in Colombia, describing ReACT’s emphasis on community-building. 

      Over the last two years, ReACT has expanded its geographic presence, growing from a hub in Jordan into a robust global community of many hubs, including in Colombia and Uganda. These regional sites connect talented refugees and displaced learners to internships and employment, startup networks and accelerators, and pathways to formal undergraduate and graduate education.

      This expansion is thanks to the generous support internally from the MIT Office of the Provost and Associate Provost Richard Lester and external organizations including the Western Union Foundation. ReACT will build new hubs this year in Greece, Uruguay, and Afghanistan, as a result of gifts from the Hatsopoulos family and the Pfeffer family.

      Holding space to learn from each other

      In addition to establishing new global hubs, ReACT plans to expand its network of internship and experiential learning opportunities, increasing outreach to new collaborators such as nongovernmental organizations (NGOs), companies, and universities. Jointly with Na’amal and Paper Airplanes, a nonprofit that connects conflict-affected individuals with personal language tutors, ReACT will host the first Migration Summit. Scheduled for April 2022, the month-long global convening invites a broad range of participants, including displaced learners, universities, companies, nonprofits and NGOs, social enterprises, foundations, philanthropists, researchers, policymakers, employers, and governments, to address the key challenges and opportunities for refugee and migrant communities. The theme of the summit is “Education and Workforce Development in Displacement.”

      “The MIT Migration Summit offers a platform to discuss how new educational models, such as those employed in ReACT, can help solve emerging challenges in providing quality education and career opportunities to forcibly displaced and marginalized people around the world,” says Masic. 

      A key goal of the convening is to center the voices of those most directly impacted by displacement, such as ReACT’s learners from Afghanistan and elsewhere, in solution-making. More

    • 125 Shares129 Views

      in Environment

      3 Questions: What a single car can say about traffic

      by

      Vehicle traffic has long defied description. Once measured roughly through visual inspection and traffic cameras, new smartphone crowdsourcing tools are now quantifying traffic far more precisely. This popular method, however, also presents a problem: Accurate measurements require a lot of data and users.

      Meshkat Botshekan, an MIT PhD student in civil and environmental engineering and research assistant at the MIT Concrete Sustainability Hub, has sought to expand on crowdsourcing methods by looking into the physics of traffic. During his time as a doctoral candidate, he has helped develop Carbin, a smartphone-based roadway crowdsourcing tool created by MIT CSHub and the University of Massachusetts Dartmouth, and used its data to offer more insight into the physics of traffic — from the formation of traffic jams to the inference of traffic phase and driving behavior. Here, he explains how recent findings can allow smartphones to infer traffic properties from the measurements of a single vehicle.  

      Q: Numerous navigation apps already measure traffic. Why do we need alternatives?

      A: Traffic characteristics have always been tough to measure. In the past, visual inspection and cameras were used to produce traffic metrics. So, there’s no denying that today’s navigation tools apps offer a superior alternative. Yet even these modern tools have gaps.

      Chief among them is their dependence on spatially distributed user counts: Essentially, these apps tally up their users on road segments to estimate the density of traffic. While this approach may seem adequate, it is both vulnerable to manipulation, as demonstrated in some viral videos, and requires immense quantities of data for reliable estimates. Processing these data is so time- and resource-intensive that, despite their availability, they can’t be used to quantify traffic effectively across a whole road network. As a result, this immense quantity of traffic data isn’t actually optimal for traffic management.

      Q: How could new technologies improve how we measure traffic?

      A: New alternatives have the potential to offer two improvements over existing methods: First, they can extrapolate far more about traffic with far fewer data. Second, they can cost a fraction of the price while offering a far simpler method of data collection. Just like Waze and Google Maps, they rely on crowdsourcing data from users. Yet, they are grounded in the incorporation of high-level statistical physics into data analysis.

      For instance, the Carbin app, which we are developing in collaboration with UMass Dartmouth, applies principles of statistical physics to existing traffic models to entirely forgo the need for user counts. Instead, it can infer traffic density and driver behavior using the input of a smartphone mounted in single vehicle.

      The method at the heart of the app, which was published last fall in Physical Review E, treats vehicles like particles in a many-body system. Just as the behavior of a closed many-body system can be understood through observing the behavior of an individual particle relying on the ergodic theorem of statistical physics, we can characterize traffic through the fluctuations in speed and position of a single vehicle across a road. As a result, we can infer the behavior and density of traffic on a segment of a road.

      As far less data is required, this method is more rapid and makes data management more manageable. But most importantly, it also has the potential to make traffic data less expensive and accessible to those that need it.

      Q: Who are some of the parties that would benefit from new technologies?

      A: More accessible and sophisticated traffic data would benefit more than just drivers seeking smoother, faster routes. It would also enable state and city departments of transportation (DOTs) to make local and collective interventions that advance the critical transportation objectives of equity, safety, and sustainability.

      As a safety solution, new data collection technologies could pinpoint dangerous driving conditions on a much finer scale to inform improved traffic calming measures. And since socially vulnerable communities experience traffic violence disproportionately, these interventions would have the added benefit of addressing pressing equity concerns. 

      There would also be an environmental benefit. DOTs could mitigate vehicle emissions by identifying minute deviations in traffic flow. This would present them with more opportunities to mitigate the idling and congestion that generate excess fuel consumption.  

      As we’ve seen, these three challenges have become increasingly acute, especially in urban areas. Yet, the data needed to address them exists already — and is being gathered by smartphones and telematics devices all over the world. So, to ensure a safer, more sustainable road network, it will be crucial to incorporate these data collection methods into our decision-making. More

    • 150 Shares169 Views

      in Environment

      A dirt cheap solution? Common clay materials may help curb methane emissions

      by

      Methane is a far more potent greenhouse gas than carbon dioxide, and it has a pronounced effect within first two decades of its presence in the atmosphere. In the recent international climate negotiations in Glasgow, abatement of methane emissions was identified as a major priority in attempts to curb global climate change quickly.

      Now, a team of researchers at MIT has come up with a promising approach to controlling methane emissions and removing it from the air, using an inexpensive and abundant type of clay called zeolite. The findings are described in the journal ACS Environment Au, in a paper by doctoral student Rebecca Brenneis, Associate Professor Desiree Plata, and two others.

      Although many people associate atmospheric methane with drilling and fracking for oil and natural gas, those sources only account for about 18 percent of global methane emissions, Plata says. The vast majority of emitted methane comes from such sources as slash-and-burn agriculture, dairy farming, coal and ore mining, wetlands, and melting permafrost. “A lot of the methane that comes into the atmosphere is from distributed and diffuse sources, so we started to think about how you could take that out of the atmosphere,” she says.

      The answer the researchers found was something dirt cheap — in fact, a special kind of “dirt,” or clay. They used zeolite clays, a material so inexpensive that it is currently used to make cat litter. Treating the zeolite with a small amount of copper, the team found, makes the material very effective at absorbing methane from the air, even at extremely low concentrations.

      The system is simple in concept, though much work remains on the engineering details. In their lab tests, tiny particles of the copper-enhanced zeolite material, similar to cat litter, were packed into a reaction tube, which was then heated from the outside as the stream of gas, with methane levels ranging from just 2 parts per million up to 2 percent concentration, flowed through the tube. That range covers everything that might exist in the atmosphere, down to subflammable levels that cannot be burned or flared directly.

      The process has several advantages over other approaches to removing methane from air, Plata says. Other methods tend to use expensive catalysts such as platinum or palladium, require high temperatures of at least 600 degrees Celsius, and tend to require complex cycling between methane-rich and oxygen-rich streams, making the devices both more complicated and more risky, as methane and oxygen are highly combustible on their own and in combination.

      “The 600 degrees where they run these reactors makes it almost dangerous to be around the methane,” as well as the pure oxygen, Brenneis says. “They’re solving the problem by just creating a situation where there’s going to be an explosion.” Other engineering complications also arise from the high operating temperatures. Unsurprisingly, such systems have not found much use.

      As for the new process, “I think we’re still surprised at how well it works,” says Plata, who is the Gilbert W. Winslow Associate Professor of Civil and Environmental Engineering. The process seems to have its peak effectiveness at about 300 degrees Celsius, which requires far less energy for heating than other methane capture processes. It also can work at concentrations of methane lower than other methods can address, even small fractions of 1 percent, which most methods cannot remove, and does so in air rather than pure oxygen, a major advantage for real-world deployment.

      The method converts the methane into carbon dioxide. That might sound like a bad thing, given the worldwide efforts to combat carbon dioxide emissions. “A lot of people hear ‘carbon dioxide’ and they panic; they say ‘that’s bad,’” Plata says. But she points out that carbon dioxide is much less impactful in the atmosphere than methane, which is about 80 times stronger as a greenhouse gas over the first 20 years, and about 25 times stronger for the first century. This effect arises from that fact that methane turns into carbon dioxide naturally over time in the atmosphere. By accelerating that process, this method would drastically reduce the near-term climate impact, she says. And, even converting half of the atmosphere’s methane to carbon dioxide would increase levels of the latter by less than 1 part per million (about 0.2 percent of today’s atmospheric carbon dioxide) while saving about 16 percent of total radiative warming.

      The ideal location for such systems, the team concluded, would be in places where there is a relatively concentrated source of methane, such as dairy barns and coal mines. These sources already tend to have powerful air-handling systems in place, since a buildup of methane can be a fire, health, and explosion hazard. To surmount the outstanding engineering details, the team has just been awarded a $2 million grant from the U.S. Department of Energy to continue to develop specific equipment for methane removal in these types of locations.

      “The key advantage of mining air is that we move a lot of it,” she says. “You have to pull fresh air in to enable miners to breathe, and to reduce explosion risks from enriched methane pockets. So, the volumes of air that are moved in mines are enormous.” The concentration of methane is too low to ignite, but it’s in the catalysts’ sweet spot, she says.

      Adapting the technology to specific sites should be relatively straightforward. The lab setup the team used in their tests consisted of  “only a few components, and the technology you would put in a cow barn could be pretty simple as well,” Plata says. However, large volumes of gas do not flow that easily through clay, so the next phase of the research will focus on ways of structuring the clay material in a multiscale, hierarchical configuration that will aid air flow.

      “We need new technologies for oxidizing methane at concentrations below those used in flares and thermal oxidizers,” says Rob Jackson, a professor of earth systems science at Stanford University, who was not involved in this work. “There isn’t a cost-effective technology today for oxidizing methane at concentrations below about 2,000 parts per million.”

      Jackson adds, “Many questions remain for scaling this and all similar work: How quickly will the catalyst foul under field conditions? Can we get the required temperatures closer to ambient conditions? How scaleable will such technologies be when processing large volumes of air?”

      One potential major advantage of the new system is that the chemical process involved releases heat. By catalytically oxidizing the methane, in effect the process is a flame-free form of combustion. If the methane concentration is above 0.5 percent, the heat released is greater than the heat used to get the process started, and this heat could be used to generate electricity.

      The team’s calculations show that “at coal mines, you could potentially generate enough heat to generate electricity at the power plant scale, which is remarkable because it means that the device could pay for itself,” Plata says. “Most air-capture solutions cost a lot of money and would never be profitable. Our technology may one day be a counterexample.”

      Using the new grant money, she says, “over the next 18 months we’re aiming to demonstrate a proof of concept that this can work in the field,” where conditions can be more challenging than in the lab. Ultimately, they hope to be able to make devices that would be compatible with existing air-handling systems and could simply be an extra component added in place. “The coal mining application is meant to be at a stage that you could hand to a commercial builder or user three years from now,” Plata says.

      In addition to Plata and Brenneis, the team included Yale University PhD student Eric Johnson and former MIT postdoc Wenbo Shi. The work was supported by the Gerstner Philanthropies, Vanguard Charitable Trust, the Betty Moore Inventor Fellows Program, and MIT’s Research Support Committee. More

    • 75 Shares119 Views

      in Environment

      Q&A: More-sustainable concrete with machine learning

      by

      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

    • 150 Shares129 Views

      in Energy

      Timber or steel? Study helps builders reduce carbon footprint of truss structures

      by

      Buildings are a big contributor to global warming, not just in their ongoing operations but in the materials used in their construction. Truss structures — those crisscross arrays of diagonal struts used throughout modern construction, in everything from antenna towers to support beams for large buildings — are typically made of steel or wood or a combination of both. But little quantitative research has been done on how to pick the right materials to minimize these structures’ contribution global warming.

      The “embodied carbon” in a construction material includes the fuel used in the material’s production (for mining and smelting steel, for example, or for felling and processing trees) and in transporting the materials to a site. It also includes the equipment used for the construction itself.

      Now, researchers at MIT have done a detailed analysis and created a set of computational tools to enable architects and engineers to design truss structures in a way that can minimize their embodied carbon while maintaining all needed properties for a given building application. While in general wood produces a much lower carbon footprint, using steel in places where its properties can provide maximum benefit can provide an optimized result, they say.

      The analysis is described in a paper published today in the journal Engineering Structures, by graduate student Ernest Ching and MIT assistant professor of civil and environmental engineering Josephine Carstensen.

      “Construction is a huge greenhouse gas emitter that has kind of been flying under the radar for the past decades,” says Carstensen. But in recent years building designers “are starting to be more focused on how to not just reduce the operating energy associated with building use, but also the important carbon associated with the structure itself.” And that’s where this new analysis comes in.

      The two main options in reducing the carbon emissions associated with truss structures, she says, are substituting materials or changing the structure. However, there has been “surprisingly little work” on tools to help designers figure out emissions-minimizing strategies for a given situation, she says.

      The new system makes use of a technique called topology optimization, which allows for the input of basic parameters, such as the amount of load to be supported and the dimensions of the structure, and can be used to produce designs optimized for different characteristics, such as weight, cost, or, in this case, global warming impact.

      Wood performs very well under forces of compression, but not as well as steel when it comes to tension — that is, a tendency to pull the structure apart. Carstensen says that in general, wood is far better than steel in terms of embedded carbon, so “especially if you have a structure that doesn’t have any tension, then you should definitely only use timber” in order to minimize emissions. One tradeoff is that “the weight of the structure is going to be bigger than it would be with steel,” she says.

      The tools they developed, which were the basis for Ching’s master’s thesis, can be applied at different stages, either in the early planning phase of a structure, or later on in the final stages of a design.

      As an exercise, the team developed a proposal for reengineering several trusses using these optimization tools, and demonstrated that a significant savings in embodied greenhouse gas emissions could be achieved with no loss of performance. While they have shown improvements of at least 10 percent can be achieved, she says those estimates are “not exactly apples to apples” and likely savings could actually be two to three times that.

      “It’s about choosing materials more smartly,” she says, for the specifics of a given application. Often in existing buildings “you will have timber where there’s compression, and where that makes sense, and then it will have really skinny steel members, in tension, where that makes sense. And that’s also what we see in our design solutions that are suggested, but perhaps we can see it even more clearly.” The tools are not ready for commercial use though, she says, because they haven’t yet added a user interface.

      Carstensen sees a trend to increasing use of timber in large construction, which represents an important potential for reducing the world’s overall carbon emissions. “There’s a big interest in the construction industry in mass timber structures, and this speaks right into that area. So, the hope is that this would make inroads into the construction business and actually make a dent in that very large contribution to greenhouse gas emissions.” More

    • 150 Shares189 Views

      in Energy

      MIT Energy Initiative awards seven Seed Fund grants for early-stage energy research

      by

      The MIT Energy Initiative (MITEI) has awarded seven Seed Fund grants to support novel, early-stage energy research by faculty and researchers at MIT. The awardees hail from a range of disciplines, but all strive to bring their backgrounds and expertise to address the global climate crisis by improving the efficiency, scalability, and adoption of clean energy technologies.

      “Solving climate change is truly an interdisciplinary challenge,” says MITEI Director Robert C. Armstrong. “The Seed Fund grants foster collaboration and innovation from across all five of MIT’s schools and one college, encouraging an ‘all hands on deck approach’ to developing the energy solutions that will prove critical in combatting this global crisis.”

      This year, MITEI’s Seed Fund grant program received 70 proposals from 86 different principal investigators (PIs) across 25 departments, labs, and centers. Of these proposals, 31 involved collaborations between two or more PIs, including 24 that involved multiple departments.

      The winning projects reflect this collaborative nature with topics addressing the optimization of low-energy thermal cooling in buildings; the design of safe, robust, and resilient distributed power systems; and how to design and site wind farms with consideration of wind resource uncertainty due to climate change.

      Increasing public support for low-carbon technologies

      One winning team aims to leverage work done in the behavioral sciences to motivate sustainable behaviors and promote the adoption of clean energy technologies.

      “Objections to scalable low-carbon technologies such as nuclear energy and carbon sequestration have made it difficult to adopt these technologies and reduce greenhouse gas emissions,” says Howard Herzog, a senior research scientist at MITEI and co-PI. “These objections tend to neglect the sheer scale of energy generation required and the inability to meet this demand solely with other renewable energy technologies.”

      This interdisciplinary team — which includes researchers from MITEI, the Department of Nuclear Science and Engineering, and the MIT Sloan School of Management — plans to convene industry professionals and academics, as well as behavioral scientists, to identify common objections, design messaging to overcome them, and prove that these messaging campaigns have long-lasting impacts on attitudes toward scalable low-carbon technologies.

      “Our aim is to provide a foundation for shifting the public and policymakers’ views about these low-carbon technologies from something they, at best, tolerate, to something they actually welcome,” says co-PI David Rand, the Erwin H. Schell Professor and professor of management science and brain and cognitive sciences at MIT Sloan School of Management.

      Siting and designing wind farms

      Michael Howland, an assistant professor of civil and environmental engineering, will use his Seed Fund grant to develop a foundational methodology for wind farm siting and design that accounts for the uncertainty of wind resources resulting from climate change.

      “The optimal wind farm design and its resulting cost of energy is inherently dependent on the wind resource at the location of the farm,” says Howland. “But wind farms are currently sited and designed based on short-term climate records that do not account for the future effects of climate change on wind patterns.”

      Wind farms are capital-intensive infrastructure that cannot be relocated and often have lifespans exceeding 20 years — all of which make it especially important that developers choose the right locations and designs based not only on wind patterns in the historical climate record, but also based on future predictions. The new siting and design methodology has the potential to replace current industry standards to enable a more accurate risk analysis of wind farm development and energy grid expansion under climate change-driven energy resource uncertainty.

      Membraneless electrolyzers for hydrogen production

      Producing hydrogen from renewable energy-powered water electrolyzers is central to realizing a sustainable and low-carbon hydrogen economy, says Kripa Varanasi, a professor of mechanical engineering and a Seed Fund award recipient. The idea of using hydrogen as a fuel has existed for decades, but it has yet to be widely realized at a considerable scale. Varanasi hopes to change that with his Seed Fund grant.

      “The critical economic hurdle for successful electrolyzers to overcome is the minimization of the capital costs associated with their deployment,” says Varanasi. “So, an immediate task at hand to enable electrochemical hydrogen production at scale will be to maximize the effectiveness of the most mature, least complex, and least expensive water electrolyzer technologies.”

      To do this, he aims to combine the advantages of existing low-temperature alkaline electrolyzer designs with a novel membraneless electrolyzer technology that harnesses a gas management system architecture to minimize complexity and costs, while also improving efficiency. Varanasi hopes his project will demonstrate scalable concepts for cost-effective electrolyzer technology design to help realize a decarbonized hydrogen economy.

      Since its establishment in 2008, the MITEI Seed Fund Program has supported 194 energy-focused seed projects through grants totaling more than $26 million. This funding comes primarily from MITEI’s founding and sustaining members, supplemented by gifts from generous donors.

      Recipients of the 2021 MITEI Seed Fund grants are:

      “Design automation of safe, robust, and resilient distributed power systems” — Chuchu Fan of the Department of Aeronautics and Astronautics
      “Advanced MHD topping cycles: For fission, fusion, solar power plants” — Jeffrey Freidberg of the Department of Nuclear Science and Engineering and Dennis Whyte of the Plasma Science and Fusion Center
      “Robust wind farm siting and design under climate-change‐driven wind resource uncertainty” — Michael Howland of the Department of Civil and Environmental Engineering
      “Low-energy thermal comfort for buildings in the Global South: Optimal design of integrated structural-thermal systems” — Leslie Norford of the Department of Architecture and Caitlin Mueller of the departments of Architecture and Civil and Environmental Engineering
      “New low-cost, high energy-density boron-based redox electrolytes for nonaqueous flow batteries” — Alexander Radosevich of the Department of Chemistry
      “Increasing public support for scalable low-carbon energy technologies using behavorial science insights” — David Rand of the MIT Sloan School of Management, Koroush Shirvan of the Department of Nuclear Science and Engineering, Howard Herzog of the MIT Energy Initiative, and Jacopo Buongiorno of the Department of Nuclear Science and Engineering
      “Membraneless electrolyzers for efficient hydrogen production using nanoengineered 3D gas capture electrode architectures” — Kripa Varanasi of the Department of Mechanical Engineering More

    • 63 Shares129 Views

      in Environment

      How marsh grass protects shorelines

      by

      Marsh plants, which are ubiquitous along the world’s shorelines, can play a major role in mitigating the damage to coastlines as sea levels rise and storm surges increase. Now, a new MIT study provides greater detail about how these protective benefits work under real-world conditions shaped by waves and currents.

      The study combined laboratory experiments using simulated plants in a large wave tank along with mathematical modeling. It appears in the journal Physical Review — Fluids, in a paper by former MIT visiting doctoral student Xiaoxia Zhang, now a postdoc at Dalian University of Technology, and professor of civil and environmental engineering Heidi Nepf.

      It’s already clear that coastal marsh plants provide significant protection from surges and devastating  storms. For example, it has been estimated that the damage caused by Hurricane Sandy was reduced by $625 million thanks to the damping of wave energy provided by extensive areas of marsh along the affected coasts. But the new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      This level of detail could enable coastal restoration planners to determine the area of marsh needed to mitigate expected amounts of storm surge or sea-level rise, and to decide which types of plants to introduce to maximize protection.

      “When you go to a marsh, you often will see that the plants are arranged in zones,” says Nepf, who is the Donald and Martha Harleman Professor of Civil and Environmental Engineering. “Along the edge, you tend to have plants that are more flexible, because they are using their flexibility to reduce the wave forces they feel. In the next zone, the plants are a little more rigid and have a bit more leaves.”

      As the zones progress, the plants become stiffer, leafier, and more effective at absorbing wave energy thanks to their greater leaf area. The new modeling done in this research, which incorporated work with simulated plants in the 24-meter-long wave tank at MIT’s Parsons Lab, can enable coastal planners to take these kinds of details into account when planning protection, mitigation, or restoration projects.

      “If you put the stiffest plants at the edge, they might not survive, because they’re feeling very high wave forces. By describing why Mother Nature organizes plants in this way, we can hopefully design a more sustainable restoration,” Nepf says.

      Once established, the marsh plants provide a positive feedback cycle that helps to not only stabilize but also build up these delicate coastal lands, Zhang says. “After a few years, the marsh grasses start to trap and hold the sediment, and the elevation gets higher and higher, which might keep up with sea level rise,” she says.

      The new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      Awareness of the protective effects of marshland has been growing, Nepf says. For example, the Netherlands has been restoring lost marshland outside the dikes that surround much of the nation’s agricultural land, finding that the marsh can protect the dikes from erosion; the marsh and dikes work together much more effectively than the dikes alone at preventing flooding.

      But most such efforts so far have been largely empirical, trial-and-error plans, Nepf says. Now, they could take advantage of this modeling to know just how much marshland with what types of plants would be needed to provide the desired level of protection.

      It also provides a more quantitative way to estimate the value provided by marshes, she says. “It could allow you to more accurately say, ‘40 meters of marsh will reduce waves this much and therefore will reduce overtopping of your levee by this much.’ Someone could use that to say, ‘I’m going to save this much money over the next 10 years if I reduce flooding by maintaining this marsh.’ It might help generate some political motivation for restoration efforts.”

      Nepf herself is already trying to get some of these findings included in coastal planning processes. She serves on a practitioner panel led by Chris Esposito of the Water Institute of the Gulf, which serves the storm-battered Louisiana coastline. “We’d like to get this work into the coatal simulations that are used for large-scale restoration and coastal planning,” she says.

      “Understanding the wave damping process in real vegetation wetlands is of critical value, as it is needed in the assessment of the coastal defense value of these wetlands,” says Zhan Hu, an associate professor of marine sciences at Sun Yat-Sen University, who was not associated with this work. “The challenge, however, lies in the quantitative representation of the wave damping process, in which many factors are at play, such as plant flexibility, morphology, and coexisting currents.”

      The new study, Hu says, “neatly combines experimental findings and analytical modeling to reveal the impact of each factor in the wave damping process. … Overall, this work is a solid step forward toward a more accurate assessment of wave damping capacity of real coastal wetlands, which is needed for science-based design and management of nature-based coastal protection.”

      The work was partly supported by the National Science Foundation and the China Scholarship Council.  More

    • 63 Shares189 Views

      in Environment

      Making roadway spending more sustainable

      by

      The share of federal spending on infrastructure has reached an all-time low, falling from 30 percent in 1960 to just 12 percent in 2018.

      While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more sustainable and higher performing roads are still possible even with today’s limited budgets.

      The research, conducted by a team of current and former MIT Concrete Sustainability Hub (MIT CSHub) scientists and published in Transportation Research D, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase.

      The paper presents a novel budget allocation tool and pairs it with three innovative strategies for managing pavement networks: a mix of paving materials, a mix of short- and long-term paving actions, and a long evaluation period for those actions.

      This novel approach offers numerous benefits. When applied to a 30-year case study of the Iowa U.S. Route network, the MIT CSHub model and management strategies cut emissions by 20 percent while sustaining current levels of road quality. Achieving this with a conventional planning approach would require the state to spend 32 percent more than it does today. The key to its success is the consideration of a fundamental — but fraught — aspect of pavement asset management: uncertainty.

      Predicting unpredictability

      The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may fluctuate, budgets may tighten, and traffic levels may intensify. Climate (and climate change), too, can hasten unexpected repairs.

      Managing these uncertainties effectively means looking long into the future and anticipating possible changes.

      “Capturing the impacts of uncertainty is essential for making effective paving decisions,” explains Fengdi Guo, the paper’s lead author and a departing CSHub research assistant.

      “Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance — often resulting in suboptimal spending and outcomes.”

      To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management strategies.

      The planning approach, known as Probabilistic Treatment Path Dependence (PTPD), is based on machine learning and was devised by Guo.

      “Our PTPD model is composed of four steps,” he explains. “These steps are, in order, pavement damage prediction; treatment cost prediction; budget allocation; and pavement network condition evaluation.”

      The model begins by investigating every segment in an entire pavement network and predicting future possibilities for pavement deterioration, cost, and traffic.

      “We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions,” says Guo. “The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network.”

      The PTPD model not only seeks to minimize costs to agencies but also to users — in this case, drivers. These user costs can come primarily in the form of excess fuel consumption due to poor road quality.

      “One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions,” Randolph Kirchain, a principal research scientist at MIT CSHub and MIT Materials Research Laboratory (MRL) and one of the paper’s co-authors. “Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption.”

      After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model.

      A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.

      The MIT team selected two key metrics to compare the effectiveness of various treatment paths: pavement quality and greenhouse gas emissions. These metrics were then calculated for all pavement segments in the Iowa network.

      Improvement through variation

       The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered.

      Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using a mix of paving materials, a variety of long- and short-term paving repair actions (treatments), and longer time periods on which to base paving decisions.

      They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a five-year period for evaluating the outcomes of paving actions.

      With these two approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.

      Their case study found that the MIT approach offered substantial benefits. Pavement-related greenhouse gas emissions would fall by around 20 percent across the network over the whole period. Pavement performance improved as well. To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32 percent greater budget.

      “It’s worth noting,” says Guo, “that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.”

      Much of the improvement derived from the precision of the CSHub planning model. But the three treatment strategies also play a key role.

      “We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” says Kirchain.

      It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.

      The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.

      It’s not surprising that paving decisions are daunting to make; their impacts on the environment, driver safety, and budget levels are long-lasting. But rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.

      This research was supported through the MIT Concrete Sustainability Hub by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More