Alzheimer’s disease studies at UW-Madison rely on university buildings and services funded by threatened NIH “indirect cost” awards, researchers said.

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Wisconsin researchers said federal funding delays have lingering effects, including fewer graduate trainees arriving on campus this fall.

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The U.S. Capitol in Washington, D.C., on Thursday, April 18, 2024. (Photo by Jennifer Shutt/States Newsroom)

WASHINGTON — Republicans and backers of President Donald Trump’s Make America Great Again platform support the party’s “big, beautiful bill” as passed by the U.S. House, though Americans overall view the legislation unfavorably, according to a poll released Tuesday by the nonpartisan health research organization KFF.

The survey shows that nearly two-thirds of those polled, or 64%, don’t support the tax policy changes and spending cuts Republicans have included in the sweeping House version of the bill that the Senate plans to take up this month.

When broken down by political affiliation, just 13% of Democrats and 27% of independents view the legislation favorably. Those numbers are in sharp contrast to Republicans, with 61% supporting the bill and 72% of those who identify as MAGA supporters.

But those views fluctuated when the people surveyed were asked specific questions about certain elements of the package and the real-world impacts of the legislation:

• The overall percentage of those surveyed with an unfavorable view of the bill increased from 64% to 67% when they were told it would lower federal spending on Medicaid by more than $700 billion, an estimate by the nonpartisan Congressional Budget Office.
• Dislike of the legislation rose to 74% when those polled were told policy changes would lead to 10 million people losing their health insurance coverage, another estimate from the CBO analysis.
• Opposition rose to 79% when people were told the legislation would reduce funding for local hospitals.

“The public hasn’t had much time to digest what’s in the big, beautiful, but almost incomprehensible bill as it races through Congress, and many don’t have a lot of information about it,” KFF President and CEO Drew Altman wrote in a statement. “Our poll shows that views toward the bill and its health-care provisions can shift when presented with more information and arguments about its effects, even among MAGA supporters.”

Senators wrestling with what to do

The House voted mostly along party lines to approve its 11-bill package in late May, sending the legislation to the Senate.

GOP senators have spent weeks internally debating which parts of the House legislation to keep, which to change and which to remove, while also conducting closed-door meetings with the parliamentarian to determine which parts of the bill comply with the rules for the complex reconciliation process.

Senate Majority Leader John Thune, R-S.D., plans to bring his chamber’s version of the package to the floor next week, though that timeline could slip. Before the Senate can approve the rewritten bill, lawmakers will spend hours voting on dozens of amendments during what’s known as a vote-a-rama.

Significant bipartisan support for Medicaid

The KFF poll released Tuesday shows that 83% of Americans support Medicaid, slated for an overhaul and spending reductions by GOP lawmakers.

That support remains high across political parties, with 93% of Democrats, 83% of independents and 74% of Republicans holding a favorable opinion of the state-federal health program for lower-income people and some with disabilities.

Those surveyed appeared supportive of a provision in the House bill that would require some people on Medicaid to work, participate in community service, or attend an educational program at least 80 hours a month.

The change is supported by about two-thirds of those surveyed, though the numbers shift depending on how the question is asked.

For example, when told that most adults on Medicaid already work and that not being able to complete the paperwork associated with the new requirement could cause some to lose coverage, 64% of those polled opposed the new requirement. 

Planned Parenthood

There was also broad opposition, 67% overall, to language in the House bill that would block any Medicaid funding from going to Planned Parenthood for routine health care. There is a long-standing prohibition on federal funding from going toward abortion with exceptions for rape, incest, or the life of the pregnant patient.

Opposition to the Planned Parenthood provision increased to 80% when those polled were told that no federal payments to Planned Parenthood go directly toward abortion and that ending all Medicaid payments to the organization would make it more challenging for lower-income women to access birth control, cancer screenings and STD testing.

Republicans are more supportive of that change, with 54% backing the policy and 46% opposing the new block on Medicaid patients going to Planned Parenthood. But 78% of independent women and 51% of Republican women oppose the change.

Food assistance program

Those surveyed also had concerns about how changes to the Supplemental Nutrition Assistance Program, or SNAP, would impact lower-income people’s ability to afford food, with 70% saying they were either very or somewhat concerned.

Democrats held the highest level of concern at 92%, followed by independents at 74% and Republicans at 47%.

Overall, Republicans hold the highest share of people polled who believe the dozens of GOP policy changes in the “big, beautiful bill” will help them or their family.

A total of 32% of Republicans surveyed believe the legislation will benefit them, while 47% said it will not make much of a difference and 21% said it will hurt them or their family.

Thirteen percent of independents expect the legislation will help them, while 39% said it likely won’t make a difference and 47% expect it will harm them or their family.

Of Democrats polled, just 6% said they expect the GOP mega-bill to help them, while 26% said it wouldn’t matter much and 66% expected it to hurt them or their family.

When asked whether the bill would help, not make much of a difference, or hurt certain groups of people, the largest percentage of those polled expect it to help wealthy people.

Fifty-one percent of those surveyed said they expect wealthy people will benefit from the bill, 21% believe it will help people with lower incomes and 20% said they think middle-class families will benefit.

Seventeen percent think it will help immigrants, 14% expect it to help people who buy their own health insurance, 13% believe it will help people on Medicaid, 13% think it will help people on SNAP and 8% expect it will benefit undocumented immigrants.

KFF conducted the poll June 4 – 8, both online and by telephone, among a nationally representative sample of 1,321 U.S. adults. The margin of error is plus or minus 3 percentage points for the full sample size. 

National Institutes of Health Director Jayanta Bhattacharya speaks at his confirmation hearing before the Senate Committee on Health, Education, Labor, and Pensions on Capitol Hill on March 5, 2025 in Washington, DC. (Photo by Andrew Harnik/Getty Images)

WASHINGTON — National Institutes of Health Director Jay Bhattacharya testified Tuesday that he will work with Congress to potentially reverse a steep cut to the agency’s funding the White House proposed earlier this year in its budget request.

Bhattacharya told highly critical Republicans and Democrats on the Senate panel that writes the NIH’s annual spending bill that he’s “happy” to work with lawmakers to find a funding level that everyone can support in the months ahead.

“This is my first time through this budget fight and so I’m still learning. But I’ll tell you, what I understand is that the budget is a collaborative effort between Congress and the administration,” Bhattacharya said. “I look forward to working with you all to advance the real health needs — not just the folks here in the room who represent Alzheimer’s patients, but also the health needs of all Americans.”

Senate Appropriations Chair Susan Collins, R-Maine, said it was “disturbing” that the president’s budget request suggested lawmakers cut NIH funding by about $18 billion, or 40%, in the upcoming spending bill.

“It would undo years of congressional investment in NIH,” Collins said. “And it would delay or stop effective treatments and cures from being developed for diseases like Alzheimer’s, cancer, type one diabetes. I could go on and on.

“We also risk falling behind China and other countries that are increasing their investment in biomedical research.”

Collins said the committee planned to work with Bhattacharya “to remedy these problems and the deficiencies” in the budget request in the months ahead as the committee writes the annual government funding bills.

Collins also rebuked Bhattacharya for seeking to reduce how much the NIH pays grantees for facilities and administrative costs, which go toward paying bills that aren’t directly associated with just one research project.

NIH efforts to cap those indirect costs at 15% are on hold as lawsuits from Democratic attorneys general, the Association of American Medical Colleges and the Association of American Universities work through the federal court system.

“This proposed cap is so poorly conceived,” Collins said. “And I have seen firsthand how harmful it is. It is leading to scientists leaving the United States for opportunities in other countries. It’s causing clinical trials to be halted and promising medical research to be abandoned. It’s also against federal law. Since 2018, we in Congress have specifically included language to prevent NIH from arbitrarily imposing such a cap.”

Collins told Bhattacharya to talk with Kelvin K. Droegemeier, who worked as President Donald Trump’s science adviser during his first term. Droegemeier is chairman of a group put together by the Association of Public and Land Grant Universities to propose changes to the indirect costs model.

‘Frankly, catastrophic’

Washington state Democratic Sen. Patty Murray, ranking member on the Appropriations Committee, pressed Bhattacharya to defend the actions he’s taken so far and those proposed in the budget request.

“What the Trump administration is doing to NIH right now is, frankly, catastrophic,” Murray said. “Over the past few months, this administration has fired and pushed out nearly 5,000 critical employees across NIH, prevented nearly $3 billion in grant funding from being awarded and terminated nearly 2,500 grants totaling almost $5 billion for life-saving research that is ongoing that includes clinical trials for HIV and Alzheimer’s disease.”

Murray added that no one in America wants less research into treatments and cures for cancer, or Alzheimer’s disease.

West Virginia Republican Sen. Shelley Moore Capito, chairwoman of the Labor-HHS-Education Appropriations subcommittee that held the hearing, expressed concern about NIH’s proposed budget cuts, saying they have raised alarms at numerous research universities.

“These institutions are the reason America has kept the edge in biomedical research and innovation,” Capito said. “As with many changes in leadership, there seems to be a heightened set of concern and confusion that diverting resources from research will result in a less healthy America.”

Capito emphasized she expects NIH to continue to focus research efforts on Alzheimer’s, a disease that affects more than 7 million Americans.

“For almost a decade, this committee has supported research towards the goals of finding treatments and a cure for Alzheimer’s disease,” Capito said. “This goal is very personal to me, as you know, since both of my parents lived with and eventually succumbed to this disease. And I could look out behind you and see in the audience that many folks here are extremely interested in that area of research.”

Delayed funding in the states

Wisconsin Democratic Sen. Tammy Baldwin, ranking member on the subcommittee, also reprimanded Bhattacharya for cutting off funding for projects looking into Alzheimer’s and several other illnesses.

“NIH has delayed $65 million in funding for 14 Alzheimer’s disease research centers in nine states, including at the University of Wisconsin-Madison,” Baldwin said. “It has delayed $47 million in cancer center support grants at nine cancer centers in eight states, it has delayed $55 million for 11 rare disease clinical research network grants in eight states.

“Let that sink in: This administration is making a conscious choice not to fund research into Alzheimer’s disease, cancer and rare diseases. And NIH has terminated grants for maternal morbidity and mortality centers, a grant developing new digital imaging techniques for cervical cancer screening and a clinical trial studying a potential cure for infants born with HIV, just to name a few.”

Alabama Republican Sen. Katie Britt called on Bhattacharya to ensure NIH refocuses on maternal health research and ways to decrease the country’s high maternal mortality rate.

“Look, far too many women in this country are dying from pregnancy-related causes,” Britt said. “You look at Alabama, we have one of the highest maternal mortality rates in the nation. It disproportionately affects Black women, Native American women, those women in rural areas.”

As tick season returns to Wisconsin, the Marshfield Clinic Research Institute is once again calling on residents to take part in a statewide project that could lead to a better understanding of tick-borne diseases — and help prevent them in the future.

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Hydrogen has the potential to be a climate-friendly fuel since it doesn’t release carbon dioxide when used as an energy source. Currently, however, most methods for producing hydrogen involve fossil fuels, making hydrogen less of a “green” fuel over its entire life cycle.

A new process developed by MIT engineers could significantly shrink the carbon footprint associated with making hydrogen.

Last year, the team reported that they could produce hydrogen gas by combining seawater, recycled soda cans, and caffeine. The question then was whether the benchtop process could be applied at an industrial scale, and at what environmental cost.

Now, the researchers have carried out a “cradle-to-grave” life cycle assessment, taking into account every step in the process at an industrial scale. For instance, the team calculated the carbon emissions associated with acquiring and processing aluminum, reacting it with seawater to produce hydrogen, and transporting the fuel to gas stations, where drivers could tap into hydrogen tanks to power engines or fuel cell cars. They found that, from end to end, the new process could generate a fraction of the carbon emissions that is associated with conventional hydrogen production.

In a study appearing today in Cell Reports Sustainability, the team reports that for every kilogram of hydrogen produced, the process would generate 1.45 kilograms of carbon dioxide over its entire life cycle. In comparison, fossil-fuel-based processes emit 11 kilograms of carbon dioxide per kilogram of hydrogen generated.

The low-carbon footprint is on par with other proposed “green hydrogen” technologies, such as those powered by solar and wind energy.

“We’re in the ballpark of green hydrogen,” says lead author Aly Kombargi PhD ’25, who graduated this spring from MIT with a doctorate in mechanical engineering. “This work highlights aluminum’s potential as a clean energy source and offers a scalable pathway for low-emission hydrogen deployment in transportation and remote energy systems.”

The study’s MIT co-authors are Brooke Bao, Enoch Ellis, and professor of mechanical engineering Douglas Hart.

Gas bubble

Dropping an aluminum can in water won’t normally cause much of a chemical reaction. That’s because when aluminum is exposed to oxygen, it instantly forms a shield-like layer. Without this layer, aluminum exists in its pure form and can readily react when mixed with water. The reaction that occurs involves aluminum atoms that efficiently break up molecules of water, producing aluminum oxide and pure hydrogen. And it doesn’t take much of the metal to bubble up a significant amount of the gas.

“One of the main benefits of using aluminum is the energy density per unit volume,” Kombargi says. “With a very small amount of aluminum fuel, you can conceivably supply much of the power for a hydrogen-fueled vehicle.”

Last year, he and Hart developed a recipe for aluminum-based hydrogen production. They found they could puncture aluminum’s natural shield by treating it with a small amount of gallium-indium, which is a rare-metal alloy that effectively scrubs aluminum into its pure form. The researchers then mixed pellets of pure aluminum with seawater and observed that the reaction produced pure hydrogen. What’s more, the salt in the water helped to precipitate gallium-indium, which the team could subsequently recover and reuse to generate more hydrogen, in a cost-saving, sustainable cycle.

“We were explaining the science of this process in conferences, and the questions we would get were, ‘How much does this cost?’ and, ‘What’s its carbon footprint?’” Kombargi says. “So we wanted to look at the process in a comprehensive way.”

A sustainable cycle

For their new study, Kombargi and his colleagues carried out a life cycle assessment to estimate the environmental impact of aluminum-based hydrogen production, at every step of the process, from sourcing the aluminum to transporting the hydrogen after production. They set out to calculate the amount of carbon associated with generating 1 kilogram of hydrogen — an amount that they chose as a practical, consumer-level illustration.

“With a hydrogen fuel cell car using 1 kilogram of hydrogen, you can go between 60 to 100 kilometers, depending on the efficiency of the fuel cell,” Kombargi notes.

They performed the analysis using Earthster — an online life cycle assessment tool that draws data from a large repository of products and processes and their associated carbon emissions. The team considered a number of scenarios to produce hydrogen using aluminum, from starting with “primary” aluminum mined from the Earth, versus “secondary” aluminum that is recycled from soda cans and other products, and using various methods to transport the aluminum and hydrogen.

After running life cycle assessments for about a dozen scenarios, the team identified one scenario with the lowest carbon footprint. This scenario centers on recycled aluminum — a source that saves a significant amount of emissions compared with mining aluminum — and seawater — a natural resource that also saves money by recovering gallium-indium. They found that this scenario, from start to finish, would generate about 1.45 kilograms of carbon dioxide for every kilogram of hydrogen produced. The cost of the fuel produced, they calculated, would be about $9 per kilogram, which is comparable to the price of hydrogen that would be generated with other green technologies such as wind and solar energy.

The researchers envision that if the low-carbon process were ramped up to a commercial scale, it would look something like this: The production chain would start with scrap aluminum sourced from a recycling center. The aluminum would be shredded into pellets and treated with gallium-indium. Then, drivers could transport the pretreated pellets as aluminum “fuel,” rather than directly transporting hydrogen, which is potentially volatile. The pellets would be transported to a fuel station that ideally would be situated near a source of seawater, which could then be mixed with the aluminum, on demand, to produce hydrogen. A consumer could then directly pump the gas into a car with either an internal combustion engine or a fuel cell.

The entire process does produce an aluminum-based byproduct, boehmite, which is a mineral that is commonly used in fabricating semiconductors, electronic elements, and a number of industrial products. Kombargi says that if this byproduct were recovered after hydrogen production, it could be sold to manufacturers, further bringing down the cost of the process as a whole.

“There are a lot of things to consider,” Kombargi says. “But the process works, which is the most exciting part. And we show that it can be environmentally sustainable.”

The group is continuing to develop the process. They recently designed a small reactor, about the size of a water bottle, that takes in aluminum pellets and seawater to generate hydrogen, enough to power an electric bike for several hours. They previously demonstrated that the process can produce enough hydrogen to fuel a small car. The team is also exploring underwater applications, and are designing a hydrogen reactor that would take in surrounding seawater to power a small boat or underwater vehicle.

This research was supported, in part, by the MIT Portugal Program.

Some state Republican lawmakers hope President Donald Trump’s recent executive order restricting funding for so-called “gain-of-function” research will accomplish their goal of clamping down on what they see as dangerous pathogen studies in Wisconsin. 

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There’s a lot of untapped potential in our homes and vehicles that could be harnessed to reinforce local power grids and make them more resilient to unforeseen outages, a new study shows.

In response to a cyber attack or natural disaster, a backup network of decentralized devices — such as residential solar panels, batteries, electric vehicles, heat pumps, and water heaters — could restore electricity or relieve stress on the grid, MIT engineers say.

Such devices are “grid-edge” resources found close to the consumer rather than near central power plants, substations, or transmission lines. Grid-edge devices can independently generate, store, or tune their consumption of power. In their study, the research team shows how such devices could one day be called upon to either pump power into the grid, or rebalance it by dialing down or delaying their power use.

In a paper appearing this week in the Proceedings of the National Academy of Sciences, the engineers present a blueprint for how grid-edge devices could reinforce the power grid through a “local electricity market.” Owners of grid-edge devices could subscribe to a regional market and essentially loan out their device to be part of a microgrid or a local network of on-call energy resources.

In the event that the main power grid is compromised, an algorithm developed by the researchers would kick in for each local electricity market, to quickly determine which devices in the network are trustworthy. The algorithm would then identify the combination of trustworthy devices that would most effectively mitigate the power failure, by either pumping power into the grid or reducing the power they draw from it, by an amount that the algorithm would calculate and communicate to the relevant subscribers. The subscribers could then be compensated through the market, depending on their participation.

The team illustrated this new framework through a number of grid attack scenarios, in which they considered failures at different levels of a power grid, from various sources such as a cyber attack or a natural disaster. Applying their algorithm, they showed that various networks of grid-edge devices were able to dissolve the various attacks.

The results demonstrate that grid-edge devices such as rooftop solar panels, EV chargers, batteries, and smart thermostats (for HVAC devices or heat pumps) could be tapped to stabilize the power grid in the event of an attack.

“All these small devices can do their little bit in terms of adjusting their consumption,” says study co-author Anu Annaswamy, a research scientist in MIT’s Department of Mechanical Engineering. “If we can harness our smart dishwashers, rooftop panels, and EVs, and put our combined shoulders to the wheel, we can really have a resilient grid.”

The study’s MIT co-authors include lead author Vineet Nair and John Williams, along with collaborators from multiple institutions including the Indian Institute of Technology, the National Renewable Energy Laboratory, and elsewhere.

Power boost

The team’s study is an extension of their broader work in adaptive control theory and designing systems to automatically adapt to changing conditions. Annaswamy, who leads the Active-Adaptive Control Laboratory at MIT, explores ways to boost the reliability of renewable energy sources such as solar power.

“These renewables come with a strong temporal signature, in that we know for sure the sun will set every day, so the solar power will go away,” Annaswamy says. “How do you make up for the shortfall?”

The researchers found the answer could lie in the many grid-edge devices that consumers are increasingly installing in their own homes.

“There are lots of distributed energy resources that are coming up now, closer to the customer rather than near large power plants, and it’s mainly because of individual efforts to decarbonize,” Nair says. “So you have all this capability at the grid edge. Surely we should be able to put them to good use.”

While considering ways to deal with drops in energy from the normal operation of renewable sources, the team also began to look into other causes of power dips, such as from cyber attacks. They wondered, in these malicious instances, whether and how the same grid-edge devices could step in to stabilize the grid following an unforeseen, targeted attack.

Attack mode

In their new work, Annaswamy, Nair, and their colleagues developed a framework for incorporating grid-edge devices, and in particular, internet-of-things (IoT) devices, in a way that would support the larger grid in the event of an attack or disruption. IoT devices are physical objects that contain sensors and software that connect to the internet.

For their new framework, named EUREICA (Efficient, Ultra-REsilient, IoT-Coordinated Assets), the researchers start with the assumption that one day, most grid-edge devices will also be IoT devices, enabling rooftop panels, EV chargers, and smart thermostats to wirelessly connect to a larger network of similarly independent and distributed devices. 

The team envisions that for a given region, such as a community of 1,000 homes, there exists a certain number of IoT devices that could potentially be enlisted in the region’s local network, or microgrid. Such a network would be managed by an operator, who would be able to communicate with operators of other nearby microgrids.

If the main power grid is compromised or attacked, operators would run the researchers’ decision-making algorithm to determine trustworthy devices within the network that can pitch in to help mitigate the attack.

The team tested the algorithm on a number of scenarios, such as a cyber attack in which all smart thermostats made by a certain manufacturer are hacked to raise their setpoints simultaneously to a degree that dramatically alters a region’s energy load and destabilizes the grid. The researchers also considered attacks and weather events that would shut off the transmission of energy at various levels and nodes throughout a power grid.

“In our attacks we consider between 5 and 40 percent of the power being lost. We assume some nodes are attacked, and some are still available and have some IoT resources, whether a battery with energy available or an EV or HVAC device that’s controllable,” Nair explains. “So, our algorithm decides which of those houses can step in to either provide extra power generation to inject into the grid or reduce their demand to meet the shortfall.”

In every scenario that they tested, the team found that the algorithm was able to successfully restabilize the grid and mitigate the attack or power failure. They acknowledge that to put in place such a network of grid-edge devices will require buy-in from customers, policymakers, and local officials, as well as innovations such as advanced power inverters that enable EVs to inject power back into the grid.

“This is just the first of many steps that have to happen in quick succession for this idea of local electricity markets to be implemented and expanded upon,” Annaswamy says. “But we believe it’s a good start.”

This work was supported, in part, by the U.S. Department of Energy and the MIT Energy Initiative.

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

As a major contributor to global carbon dioxide (CO₂) emissions, the transportation sector has immense potential to advance decarbonization. However, a zero-emissions global supply chain requires re-imagining reliance on a heavy-duty trucking industry that emits 810,000 tons of CO₂, or 6 percent of the United States’ greenhouse gas emissions, and consumes 29 billion gallons of diesel annually in the U.S. alone.

A new study by MIT researchers, presented at the recent American Society of Mechanical Engineers 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue. The multivariable model outlined in the paper allows fleet owners and operators to better understand the design choices that impact the economic feasibility of battery-electric and hydrogen fuel cell heavy-duty trucks for commercial application, equipping stakeholders to make informed fleet transition decisions.

“The whole issue [of decarbonizing trucking] is like a very big, messy pie. One of the things we can do, from an academic standpoint, is quantify some of those pieces of pie with modeling, based on information and experience we’ve learned from industry stakeholders,” says ZhiYi Liang, PhD student on the renewable hydrogen team at the MIT K. Lisa Yang Global Engineering and Research Center (GEAR) and lead author of the study. Co-authored by Bryony DuPont, visiting scholar at GEAR, and Amos Winter, the Germeshausen Professor in the MIT Department of Mechanical Engineering, the paper elucidates operational and socioeconomic factors that need to be considered in efforts to decarbonize heavy-duty vehicles (HDVs).

Operational and infrastructure challenges

The team’s model shows that a technical challenge lies in the amount of energy that needs to be stored on the truck to meet the range and towing performance needs of commercial trucking applications. Due to the high energy density and low cost of diesel, existing diesel drivetrains remain more competitive than alternative lithium battery-electric vehicle (Li-BEV) and hydrogen fuel-cell-electric vehicle (H2 FCEV) drivetrains. Although Li-BEV drivetrains have the highest energy efficiency of all three, they are limited to short-to-medium range routes (under 500 miles) with low freight capacity, due to the weight and volume of the onboard energy storage needed. In addition, the authors note that existing electric grid infrastructure will need significant upgrades to support large-scale deployment of Li-BEV HDVs.

While the hydrogen-powered drivetrain has a significant weight advantage that enables higher cargo capacity and routes over 750 miles, the current state of hydrogen fuel networks limits economic viability, especially once operational cost and projected revenue are taken into account. Deployment will most likely require government intervention in the form of incentives and subsidies to reduce the price of hydrogen by more than half, as well as continued investment by corporations to ensure a stable supply. Also, as H2-FCEVs are still a relatively new technology, the ongoing design of conformal onboard hydrogen storage systems — one of which is the subject of Liang’s PhD — is crucial to successful adoption into the HDV market.

The current efficiency of diesel systems is a result of technological developments and manufacturing processes established over many decades, a precedent that suggests similar strides can be made with alternative drivetrains. However, interactions with fleet owners, automotive manufacturers, and refueling network providers reveal another major hurdle in the way that each “slice of the pie” is interrelated — issues must be addressed simultaneously because of how they affect each other, from renewable fuel infrastructure to technological readiness and capital cost of new fleets, among other considerations. And first steps into an uncertain future, where no one sector is fully in control of potential outcomes, is inherently risky. 

“Besides infrastructure limitations, we only have prototypes [of alternative HDVs] for fleet operator use, so the cost of procuring them is high, which means there isn’t demand for automakers to build manufacturing lines up to a scale that would make them economical to produce,” says Liang, describing just one step of a vicious cycle that is difficult to disrupt, especially for industry stakeholders trying to be competitive in a free market. 

Quantifying a path to feasibility

“Folks in the industry know that some kind of energy transition needs to happen, but they may not necessarily know for certain what the most viable path forward is,” says Liang. Although there is no singular avenue to zero emissions, the new model provides a way to further quantify and assess at least one slice of pie to aid decision-making.

Other MIT-led efforts aimed at helping industry stakeholders navigate decarbonization include an interactive mapping tool developed by Danika MacDonell, Impact Fellow at the MIT Climate and Sustainability Consortium (MCSC); alongside Florian Allroggen, executive director of MITs Zero Impact Aviation Alliance; and undergraduate researchers Micah Borrero, Helena De Figueiredo Valente, and Brooke Bao. The MCSC’s Geospatial Decision Support Tool supports strategic decision-making for fleet operators by allowing them to visualize regional freight flow densities, costs, emissions, planned and available infrastructure, and relevant regulations and incentives by region.

While current limitations reveal the need for joint problem-solving across sectors, the authors believe that stakeholders are motivated and ready to tackle climate problems together. Once-competing businesses already appear to be embracing a culture shift toward collaboration, with the recent agreement between General Motors and Hyundai to explore “future collaboration across key strategic areas,” including clean energy. 

Liang believes that transitioning the transportation sector to zero emissions is just one part of an “energy revolution” that will require all sectors to work together, because “everything is connected. In order for the whole thing to make sense, we need to consider ourselves part of that pie, and the entire system needs to change,” says Liang. “You can’t make a revolution succeed by yourself.” 

The authors acknowledge the MIT Climate and Sustainability Consortium for connecting them with industry members in the HDV ecosystem; and the MIT K. Lisa Yang Global Engineering and Research Center and MIT Morningside Academy for Design for financial support.

Charging stations for electric vehicles are essential for cleaning up the transportation sector. A new study by MIT researchers suggests they’re good for business, too.

The study found that, in California, opening a charging station boosted annual spending at each nearby business by an average of about $1,500 in 2019 and about $400 between January 2021 and June 2023. The spending bump amounts to thousands of extra dollars annually for nearby businesses, with the increase particularly pronounced for businesses in underresourced areas.

The study’s authors hope the research paints a more holistic picture of the benefits of EV charging stations, beyond environmental factors.

“These increases are equal to a significant chunk of the cost of installing an EV charger, and I hope this study sheds light on these economic benefits,” says lead author Yunhan Zheng MCP ’21, SM ’21, PhD ’24, a postdoc at the Singapore-MIT Alliance for Research and Technology (SMART). “The findings could also diversify the income stream for charger providers and site hosts, and lead to more informed business models for EV charging stations.”

Zheng’s co-authors on the paper, which was published today in Nature Communications, are David Keith, a senior lecturer at the MIT Sloan School of Management; Jinhua Zhao, an MIT professor of cities and transportation; and alumni Shenhao Wang MCP ’17, SM ’17, PhD ’20 and Mi Diao MCP ’06, PhD ’10.

Understanding the EV effect

Increasing the number of electric vehicle charging stations is seen as a key prerequisite for the transition to a cleaner, electrified transportation sector. As such, the 2021 U.S. Infrastructure Investment and Jobs Act committed $7.5 billion to build a national network of public electric vehicle chargers across the U.S.

But a large amount of private investment will also be needed to make charging stations ubiquitous.

“The U.S. is investing a lot in EV chargers and really encouraging EV adoption, but many EV charging providers can’t make enough money at this stage, and getting to profitability is a major challenge,” Zheng says.

EV advocates have long argued that the presence of charging stations brings economic benefits to surrounding communities, but Zheng says previous studies on their impact relied on surveys or were small-scale. Her team of collaborators wanted to make advocates’ claims more empirical.

For their study, the researchers collected data from over 4,000 charging stations in California and 140,000 businesses, relying on anonymized credit and debit card transactions to measure changes in consumer spending. The researchers used data from 2019 through June of 2023, skipping the year 2020 to minimize the impact of the pandemic.

To judge whether charging stations caused customer spending increases, the researchers compared data from businesses within 500 meters of new charging stations before and after their installation. They also analyzed transactions from similar businesses in the same time frame that weren’t near charging stations.

Supercharging nearby businesses

The researchers found that installing a charging station boosted annual spending at nearby establishments by an average of 1.4 percent in 2019 and 0.8 percent from January 2021 to June 2023.

While that might sound like a small amount per business, it amounts to thousands of dollars in overall consumer spending increases. Specifically, those percentages translate to almost $23,000 in cumulative spending increases in 2019 and about $3,400 per year from 2021 through June 2023.

Zheng says the decline in spending increases over the two time periods might be due to a saturation of EV chargers, leading to lower utilization, as well as an overall decrease in spending per business after the Covid-19 pandemic and a reduced number of businesses served by each EV charging station in the second period. Despite this decline, the annual impact of a charging station on all its surrounding businesses would still cover approximately 11.2 percent of the average infrastructure and installation cost of a standard charging station.

Through both time frames, the spending increases were highest for businesses within about a football field’s distance from the new stations. They were also significant for businesses in disadvantaged and low-income areas, as designated by California and the Justice40 Initiative.

“The positive impacts of EV charging stations on businesses are not constrained solely to some high-income neighborhoods,” Wang says. “It highlights the importance for policymakers to develop EV charging stations in marginalized areas, because they not only foster a cleaner environment, but also serve as a catalyst for enhancing economic vitality.”

Zheng believes the findings hold a lesson for charging station developers seeking to improve the profitability of their projects.

“The joint gas station and convenience store business model could also be adopted to EV charging stations,” Zheng says. “Traditionally, many gas stations are affiliated with retail store chains, which enables owners to both sell fuel and attract customers to diversify their revenue stream. EV charging providers could consider a similar approach to internalize the positive impact of EV charging stations.”

Zheng also says the findings could support the creation of new funding models for charging stations, such as multiple businesses sharing the costs of construction so they can all benefit from the added spending.

Those changes could accelerate the creation of charging networks, but Zheng cautions that further research is needed to understand how much the study’s findings can be extrapolated to other areas. She encourages other researchers to study the economic effects of charging stations and hopes future research includes states beyond California and even other countries.

“A huge number of studies have focused on retail sales effects from traditional transportation infrastructure, such as rail and subway stations, bus stops, and street configurations,” Zhao says. “This research provides evidence for an important, emerging piece of transportation infrastructure and shows a consistently positive effect on local businesses, paving the way for future research in this area.”

The research was supported, in part, by the Singapore-MIT Alliance for Research and Technology (SMART) and the Singapore National Research Foundation. Diao was partially supported by the Natural Science Foundation of Shanghai and the Fundamental Research Funds for the Central Universities of China.

Although critical to the nation’s food security, farm work is potentially hazardous, farmworkers receive lower wages when compared with nonsupervisory workers outside agriculture, and many hired farm workers lack legal work authorization and access to basic public services. For the United States to remain competitive as a producer of fruit, vegetables, and other labor-intensive commodities both private and government institutions will need to accelerate adaptation to a changing landscape of farm labor.

An upcoming conference on farm labor seeks to strengthen ongoing farm labor research by convening and developing a network of researchers and stakeholders. The Changing Landscape of Farm Labor Conditions in the United States: What the Future Holds and How to Prepare for It conference will take place September 17 to 19, 2024, in Santa Cruz, California.

The conference is presented by the USDA Economic Research Service and Farm Foundation. It will cover four key themes: trends in the farm labor force, including worker migration and the H-2A Temporary Agricultural Program; labor costs, farm worker conditions, and workforce development.

Visit https://farmfoundation.swoogo.com/farmlabor for more information and to register.

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