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    Small, inexpensive hydrophone boosts undersea signals

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    Researchers at MIT Lincoln Laboratory have developed a first-of-its-kind hydrophone built around a simple, commercially available microphone. The device, leveraging a common microfabrication process known as microelectromechanical systems (MEMS), is significantly smaller and less expensive than current hydrophones, yet has equal or exceeding sensitivity. The hydrophone could have applications for the U.S. Navy, as well as industry and the scientific research community.”Given the broad interest from the Navy in low-cost hydrophones, we were surprised that this design had not been pursued before,” says Daniel Freeman, who leads this work in the Advanced Materials and Microsystems Group. “Hydrophones are critical for undersea sensing in a variety of applications and platforms. Our goal was to demonstrate that we could develop a device at reduced size and cost without sacrificing performance.”Essentially an underwater microphone, a hydrophone is an instrument that converts sound waves into electrical signals, allowing us to “hear” and record sounds in the ocean and other bodies of water. These signals can later be analyzed and interpreted, providing valuable information about the underwater environment.MEMS devices are incredibly small systems — ranging from a few millimeters down to microns (smaller than a human hair) — with tiny moving parts. They are used in a variety of sensors, including microphones, gyroscopes, and accelerometers. The small size of MEMS sensors has made them crucial in various applications, from smartphones to medical devices. Currently, no commercially available hydrophones utilize MEMS technology, so the team set out to understand whether such a design was possible.With funding from the Office of the Under Secretary of War for Research and Engineering to develop a novel hydrophone, the team first planned to use microfabrication, an area of expertise at the laboratory, to develop their device. However, that approach proved to be too costly and involved to pursue. This obstacle led the team to pivot and build their hydrophone around a commercially available MEMS microphone. “We had to come up with an inexpensive alternative without giving up performance, and this is what led us to build the design around a microphone, which to our knowledge is a novel approach,” Freeman explains.In collaboration with researchers at Tufts University, as well as industry partners SeaLandAire Technologies and Navmar Applied Sciences Corp., the team made the hydrophone by encapsulating the MEMS microphone in a polymer with low permeability to water while leaving an air cavity around the microphone’s diaphragm (the component of the microphone that vibrates in response to sound waves). One key challenge that they faced was the possibility of losing too much signal to the packaging and the air cavity around the MEMS microphone. After a substantial amount of simulation, design iterations, and testing, the team found that the signal lost from incorporating air into the device was compensated for by the very high sensitivity of the MEMS microphone itself. As a result, the device was able to perform at a sensitivity comparable to high-end hydrophones at depths down to 400 feet and temperatures as low as 40 degrees Fahrenheit. To date, the collaborative effort has involved computational modeling, system electronics design and fabrication, prototype unit manufacturing, and calibrator and pool testing.In July, eight researchers traveled to Seneca Lake in New York to test a variety of devices. The hydrophones were lowered to increasing depths in the water — 100 feet at first, then incrementally lower down to 400 feet. At each depth, acoustic signals of varying frequencies were transmitted for the instrument to record. The transmitted signals were calibrated to a known level so they could then measure the actual sensitivity of the hydrophones across different frequencies. When the sound hits the hydrophone’s diaphragm, it generates an electrical signal that is amplified, digitized, and transmitted to a recording device at the surface for post-test data analysis. The team utilized both commercial underwater cables as well as Lincoln Laboratory’s fiber-based sensing arrays.”This was our first field test in deep water, and therefore it was an important milestone in demonstrating the ability to operate in a realistic environment, rather than the water chambers that we’d been using,” Freeman says. “Our hope was that the performance of our device would match what we’ve seen in our water tank, where we tested at high hydrostatic pressure across a range of frequencies. In other words, we hoped this test would provide results that confirm our predictions based on lab-based testing.”The test results were excellent, showing that the sensitivity and the signal-to-noise was within a few decibels of the quietest ocean state, known as sea state zero. Moreover, this performance was achieved in deep water, at 400 feet, and with very low temperatures, around 40 degrees Fahrenheit.The prototype hydrophone has applications across a wide variety of commercial and military use-cases owing to its small size, efficient power draw, and low cost.”We’re in discussion with the Department of War about transitioning this technology to the U.S. government and industry,” says Freeman. “There is still some room for optimizing the design, but we think we’ve demonstrated that this hydrophone has the key benefits of being robust, high performance, and very low cost.” More

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      Lincoln Lab unveils the most powerful AI supercomputer at any US university

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      The new TX-Generative AI Next (TX-GAIN) computing system at the Lincoln Laboratory Supercomputing Center  (LLSC) is the most powerful AI supercomputer at any U.S. university. With its recent ranking from  TOP500, which biannually publishes a list of the top supercomputers in various categories, TX-GAIN joins the ranks of other powerful systems at the LLSC, all supporting research and development at Lincoln Laboratory and across the MIT campus. “TX-GAIN will enable our researchers to achieve scientific and engineering breakthroughs. The system will play a large role in supporting generative AI, physical simulation, and data analysis across all research areas,” says Lincoln Laboratory Fellow Jeremy Kepner, who heads the LLSC. The LLSC is a key resource for accelerating innovation at Lincoln Laboratory. Thousands of researchers tap into the LLSC to analyze data, train models, and run simulations for federally funded research projects. The supercomputers have been used, for example, to simulate billions of aircraft encounters to develop collision-avoidance systems for the Federal Aviation Administration, and to train models in the complex tasks of autonomous navigation for the Department of Defense. Over the years, LLSC capabilities have been essential to numerous award-winning technologies, including those that have improved  airline safety,  prevented the spread of new diseases, and  aided in hurricane responses. As its name suggests, TX-GAIN is especially equipped for developing and applying generative AI. Whereas traditional AI focuses on categorization tasks, like identifying whether a photo depicts a dog or cat, generative AI produces entirely new outputs. Kepner describes it as a mathematical combination of interpolation (filling in the gaps between known data points) and extrapolation (extending data beyond known points). Today, generative AI is widely known for its use of large language models to create human-like responses to user prompts. At Lincoln Laboratory, teams are applying generative AI to various domains beyond large language models. They are using the technology, for instance, to evaluate radar signatures, supplement weather data where coverage is missing, root out anomalies in network traffic, and explore chemical interactions to design new medicines and materials.To enable such intense computations, TX-GAIN is powered by more than 600 NVIDIA graphics processing unit accelerators specially designed for AI operations, in addition to traditional high-performance computing hardware. With a peak performance of two AI exaflops (two quintillion floating-point operations per second), TX-GAIN is the top AI system at a university, and in the Northeast. Since TX-GAIN came online this summer, researchers have taken notice. “TX-GAIN is allowing us to model not only significantly more protein interactions than ever before, but also much larger proteins with more atoms. This new computational capability is a game-changer for protein characterization efforts in biological defense,” says Rafael Jaimes, a researcher in Lincoln Laboratory’s Counter–Weapons of Mass Destruction Systems Group. The LLSC’s focus on interactive supercomputing makes it especially useful to researchers. For years, the LLSC has pioneered software that lets users access its powerful systems without needing to be experts in configuring algorithms for parallel processing.  “The LLSC has always tried to make supercomputing feel like working on your laptop,” Kepner says. “The amount of data and the sophistication of analysis methods needed to be competitive today are well beyond what can be done on a laptop. But with our user-friendly approach, people can run their model and get answers quickly from their workspace.”Beyond supporting programs solely at Lincoln Laboratory, TX-GAIN is enhancing research collaborations with MIT’s campus. Such collaborations include the Haystack Observatory, Center for Quantum Engineering, Beaver Works, and Department of Air Force–MIT AI Accelerator. The latter initiative is rapidly prototyping, scaling, and applying AI technologies for the U.S. Air Force and Space Force, optimizing flight scheduling for global operations as one fielded example.The LLSC systems are housed in an energy-efficient data center and facility in Holyoke, Massachusetts. Research staff in the LLSC are also tackling the immense energy needs of AI and leading research into various power-reduction methods. One software tool they developed can reduce the energy of training an AI model by as much as 80 percent.”The LLSC provides the capabilities needed to do leading-edge research, while in a cost-effective and energy-efficient manner,” Kepner says.All of the supercomputers at the LLSC use the “TX” nomenclature in homage to Lincoln Laboratory’s Transistorized Experimental Computer Zero (TX-0) of 1956. TX-0 was one of the world’s first transistor-based machines, and its 1958 successor, TX-2, is storied for its role in pioneering human-computer interaction and AI. With TX-GAIN, the LLSC continues this legacy. More

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      in Energy

      Responding to the climate impact of generative AI

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      In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global electricity demand from data centers, which house the computing infrastructure to train and deploy AI models, will more than double by 2030, to around 945 terawatt-hours. While not all operations performed in a data center are AI-related, this total amount is slightly more than the energy consumption of Japan.Moreover, an August 2025 analysis from Goldman Sachs Research forecasts that about 60 percent of the increasing electricity demands from data centers will be met by burning fossil fuels, increasing global carbon emissions by about 220 million tons. In comparison, driving a gas-powered car for 5,000 miles produces about 1 ton of carbon dioxide.These statistics are staggering, but at the same time, scientists and engineers at MIT and around the world are studying innovations and interventions to mitigate AI’s ballooning carbon footprint, from boosting the efficiency of algorithms to rethinking the design of data centers.Considering carbon emissionsTalk of reducing generative AI’s carbon footprint is typically centered on “operational carbon” — the emissions used by the powerful processors, known as GPUs, inside a data center. It often ignores “embodied carbon,” which are emissions created by building the data center in the first place, says Vijay Gadepally, senior scientist at MIT Lincoln Laboratory, who leads research projects in the Lincoln Laboratory Supercomputing Center.Constructing and retrofitting a data center, built from tons of steel and concrete and filled with air conditioning units, computing hardware, and miles of cable, consumes a huge amount of carbon. In fact, the environmental impact of building data centers is one reason companies like Meta and Google are exploring more sustainable building materials. (Cost is another factor.)Plus, data centers are enormous buildings — the world’s largest, the China Telecomm-Inner Mongolia Information Park, engulfs roughly 10 million square feet — with about 10 to 50 times the energy density of a normal office building, Gadepally adds. “The operational side is only part of the story. Some things we are working on to reduce operational emissions may lend themselves to reducing embodied carbon, too, but we need to do more on that front in the future,” he says.Reducing operational carbon emissionsWhen it comes to reducing operational carbon emissions of AI data centers, there are many parallels with home energy-saving measures. For one, we can simply turn down the lights.“Even if you have the worst lightbulbs in your house from an efficiency standpoint, turning them off or dimming them will always use less energy than leaving them running at full blast,” Gadepally says.In the same fashion, research from the Supercomputing Center has shown that “turning down” the GPUs in a data center so they consume about three-tenths the energy has minimal impacts on the performance of AI models, while also making the hardware easier to cool.Another strategy is to use less energy-intensive computing hardware.Demanding generative AI workloads, such as training new reasoning models like GPT-5, usually need many GPUs working simultaneously. The Goldman Sachs analysis estimates that a state-of-the-art system could soon have as many as 576 connected GPUs operating at once.But engineers can sometimes achieve similar results by reducing the precision of computing hardware, perhaps by switching to less powerful processors that have been tuned to handle a specific AI workload.There are also measures that boost the efficiency of training power-hungry deep-learning models before they are deployed.Gadepally’s group found that about half the electricity used for training an AI model is spent to get the last 2 or 3 percentage points in accuracy. Stopping the training process early can save a lot of that energy.“There might be cases where 70 percent accuracy is good enough for one particular application, like a recommender system for e-commerce,” he says.Researchers can also take advantage of efficiency-boosting measures.For instance, a postdoc in the Supercomputing Center realized the group might run a thousand simulations during the training process to pick the two or three best AI models for their project.By building a tool that allowed them to avoid about 80 percent of those wasted computing cycles, they dramatically reduced the energy demands of training with no reduction in model accuracy, Gadepally says.Leveraging efficiency improvementsConstant innovation in computing hardware, such as denser arrays of transistors on semiconductor chips, is still enabling dramatic improvements in the energy efficiency of AI models.Even though energy efficiency improvements have been slowing for most chips since about 2005, the amount of computation that GPUs can do per joule of energy has been improving by 50 to 60 percent each year, says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory and a principal investigator at MIT’s Initiative on the Digital Economy.“The still-ongoing ‘Moore’s Law’ trend of getting more and more transistors on chip still matters for a lot of these AI systems, since running operations in parallel is still very valuable for improving efficiency,” says Thomspon.Even more significant, his group’s research indicates that efficiency gains from new model architectures that can solve complex problems faster, consuming less energy to achieve the same or better results, is doubling every eight or nine months.Thompson coined the term “negaflop” to describe this effect. The same way a “negawatt” represents electricity saved due to energy-saving measures, a “negaflop” is a computing operation that doesn’t need to be performed due to algorithmic improvements.These could be things like “pruning” away unnecessary components of a neural network or employing compression techniques that enable users to do more with less computation.“If you need to use a really powerful model today to complete your task, in just a few years, you might be able to use a significantly smaller model to do the same thing, which would carry much less environmental burden. Making these models more efficient is the single-most important thing you can do to reduce the environmental costs of AI,” Thompson says.Maximizing energy savingsWhile reducing the overall energy use of AI algorithms and computing hardware will cut greenhouse gas emissions, not all energy is the same, Gadepally adds.“The amount of carbon emissions in 1 kilowatt hour varies quite significantly, even just during the day, as well as over the month and year,” he says.Engineers can take advantage of these variations by leveraging the flexibility of AI workloads and data center operations to maximize emissions reductions. For instance, some generative AI workloads don’t need to be performed in their entirety at the same time.Splitting computing operations so some are performed later, when more of the electricity fed into the grid is from renewable sources like solar and wind, can go a long way toward reducing a data center’s carbon footprint, says Deepjyoti Deka, a research scientist in the MIT Energy Initiative.Deka and his team are also studying “smarter” data centers where the AI workloads of multiple companies using the same computing equipment are flexibly adjusted to improve energy efficiency.“By looking at the system as a whole, our hope is to minimize energy use as well as dependence on fossil fuels, while still maintaining reliability standards for AI companies and users,” Deka says.He and others at MITEI are building a flexibility model of a data center that considers the differing energy demands of training a deep-learning model versus deploying that model. Their hope is to uncover the best strategies for scheduling and streamlining computing operations to improve energy efficiency.The researchers are also exploring the use of long-duration energy storage units at data centers, which store excess energy for times when it is needed.With these systems in place, a data center could use stored energy that was generated by renewable sources during a high-demand period, or avoid the use of diesel backup generators if there are fluctuations in the grid.“Long-duration energy storage could be a game-changer here because we can design operations that really change the emission mix of the system to rely more on renewable energy,” Deka says.In addition, researchers at MIT and Princeton University are developing a software tool for investment planning in the power sector, called GenX, which could be used to help companies determine the ideal place to locate a data center to minimize environmental impacts and costs.Location can have a big impact on reducing a data center’s carbon footprint. For instance, Meta operates a data center in Lulea, a city on the coast of northern Sweden where cooler temperatures reduce the amount of electricity needed to cool computing hardware.Thinking farther outside the box (way farther), some governments are even exploring the construction of data centers on the moon where they could potentially be operated with nearly all renewable energy.AI-based solutionsCurrently, the expansion of renewable energy generation here on Earth isn’t keeping pace with the rapid growth of AI, which is one major roadblock to reducing its carbon footprint, says Jennifer Turliuk MBA ’25, a short-term lecturer, former Sloan Fellow, and former practice leader of climate and energy AI at the Martin Trust Center for MIT Entrepreneurship.The local, state, and federal review processes required for a new renewable energy projects can take years.Researchers at MIT and elsewhere are exploring the use of AI to speed up the process of connecting new renewable energy systems to the power grid.For instance, a generative AI model could streamline interconnection studies that determine how a new project will impact the power grid, a step that often takes years to complete.And when it comes to accelerating the development and implementation of clean energy technologies, AI could play a major role.“Machine learning is great for tackling complex situations, and the electrical grid is said to be one of the largest and most complex machines in the world,” Turliuk adds.For instance, AI could help optimize the prediction of solar and wind energy generation or identify ideal locations for new facilities.It could also be used to perform predictive maintenance and fault detection for solar panels or other green energy infrastructure, or to monitor the capacity of transmission wires to maximize efficiency.By helping researchers gather and analyze huge amounts of data, AI could also inform targeted policy interventions aimed at getting the biggest “bang for the buck” from areas such as renewable energy, Turliuk says.To help policymakers, scientists, and enterprises consider the multifaceted costs and benefits of AI systems, she and her collaborators developed the Net Climate Impact Score.The score is a framework that can be used to help determine the net climate impact of AI projects, considering emissions and other environmental costs along with potential environmental benefits in the future.At the end of the day, the most effective solutions will likely result from collaborations among companies, regulators, and researchers, with academia leading the way, Turliuk adds.“Every day counts. We are on a path where the effects of climate change won’t be fully known until it is too late to do anything about it. This is a once-in-a-lifetime opportunity to innovate and make AI systems less carbon-intense,” she says. More

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      in Environment

      Q&A: David Whelihan on the challenges of operating in the Arctic

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      To most, the Arctic can feel like an abstract place, difficult to imagine beyond images of ice and polar bears. But researcher David Whelihan of MIT Lincoln Laboratory’s Advanced Undersea Systems and Technology Group is no stranger to the Arctic. Through Operation Ice Camp, a U.S. Navy–sponsored biennial mission to assess operational readiness in the Arctic region, he has traveled to this vast and remote wilderness twice over the past few years to test low-cost sensor nodes developed by the group to monitor loss in Arctic sea ice extent and thickness. The research team envisions establishing a network of such sensors across the Arctic that will persistently detect ice-fracturing events and correlate these events with environmental conditions to provide insights into why the sea ice is breaking up. Whelihan shared his perspectives on why the Arctic matters and what operating there is like.Q: Why do we need to be able to operate in the Arctic?A: Spanning approximately 5.5 million square miles, the Arctic is huge, and one of its salient features is that the ice covering much of the Arctic Ocean is decreasing in volume with every passing year. Melting ice opens up previously impassable areas, resulting in increasing interest from potential adversaries and allies alike for activities such as military operations, commercial shipping, and natural resource extraction. Through Alaska, the United States has approximately 1,060 miles of Arctic coastline that is becoming much more accessible because of reduced ice cover. So, U.S. operation in the Arctic is a matter of national security.  Q: What are the technological limitations to Arctic operations?A: The Arctic is an incredibly harsh environment. The cold kills battery life, so collecting sensor data at high rates over long periods of time is very difficult. The ice is dynamic and can easily swallow or crush sensors. In addition, most deployments involve “boots-on-the-ice,” which is expensive and at times dangerous. One of the technological limitations is how to deploy sensors while keeping humans alive.

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      David Whelihan details the difficulties of engineering technologies that can survive in the harsh conditions of the Arctic.

      Q: How does the group’s sensor node R&D work seek to support Arctic operations?A: A lot of the work we put into our sensors pertains to deployability. Our ultimate goal is to free researchers from going onto the ice to deploy sensors. This goal will become increasingly necessary as the shrinking ice pack becomes more dynamic, unstable, and unpredictable. At the last Operation Ice Camp (OIC) in March 2024, we built and rapidly tested deployable and recoverable sensors, as well as novel concepts such as using UAVs (uncrewed aerial vehicles), or drones, as “data mules” that can fly out to and interrogate the sensors to see what they captured. We also built a prototype wearable system that cues automatic download of sensor data over Wi-Fi so that operators don’t have to take off their gloves.Q: The Arctic Circle is the northernmost region on Earth. How do you reach this remote place?A: We usually fly on commercial airlines from Boston to Seattle to Anchorage to Prudhoe Bay on the North Slope of Alaska. From there, the Navy flies us on small prop planes, like Single and Twin Otters, about 200 miles north and lands us on an ice runway built by the Navy’s Arctic Submarine Lab (ASL). The runway is part of a temporary camp that ASL establishes on floating sea ice for their operational readiness exercises conducted during OIC.Q: Think back to the first time you stepped foot in the Arctic. Can you paint a picture of what you experienced?A: My first experience was at Prudhoe Bay, coming out of the airport, which is a corrugated metal building with a single gate. Before you open the door to the outside, a sign warns you to be on the lookout for polar bears. Walking out into the sheer desolation and blinding whiteness of everything made me realize I was experiencing something very new.When I flew out onto the ice and stepped out of the plane, I was amazed that the area could somehow be even more desolate. Bright white snowy ice goes in every direction, broken up by pressure ridges that form when ice sheets collide. The sun is low, and seems to move horizontally only. It is very hard to tell the time. The air temperature is really variable. On our first trip in 2022, it really wasn’t (relatively) that cold — only around minus 5 or 10 degrees during the day. On our second trip in 2024, we were hit by minus 30 almost every day, and with winds of 20 to 25 miles per hour. The last night we were on the ice that year, it warmed up a bit to minus 10 to 20, but the winds kicked up and started blowing snow onto the heaters attached to our tents. Those heaters started failing one by one as the blowing snow covered them, blocking airflow. After our heater failed, I asked myself, while warm in my bed, whether I wanted to go outside to the command tent for help or try to make it until dawn in my thick sleeping bag. I picked the first option, but mostly because the heater control was beeping loudly right next to my bunk, so I couldn’t sleep anyway. Shout-out to the ASL staff who ran around fixing heaters all night!Q: How do you survive in a place generally inhospitable to humans?A: In partnership with the native population, ASL brings a lot of gear — from insulated, heated tents and communications equipment to large snowblowers to keep the runway clear. A few months before OIC, participants attend training on what conditions you will be exposed to and how to protect yourself through appropriate clothing, and how to use survival gear in case of an emergency.Q: Do you have plans to return to the Arctic?  A: We are hoping to go back this winter as part of OIC 2026! We plan to test a through-ice communication device. Communicating through 4 to 12 feet of ice is pretty tricky but could allow us to connect underwater drones and stationary sensors under the ice to the rest of the world. To support the through-ice communication system, we will repurpose our sensor-node boxes deployed during OIC 2024. If this setup works, those same boxes could be used as control centers for all sorts of undersea systems and relay information about the under-ice world back home via satellite.Q: What lessons learned will you bring to your upcoming trip, and any potential future trips?A: After the first trip, I had a visceral understanding of how hard operating there is. Prototyping of systems becomes a different game. Prototypes are often fragile, but fragility doesn’t go over too well on the ice. So, there is a robustification step, which can take some time.On this last trip, I realized that you have to really be careful with your energy expenditure and pace yourself. While the average adult may require about 2,000 calories a day, an Arctic explorer may burn several times more than that exerting themselves (we do a lot of walking around camp) and keeping warm. Usually, we live on the same freeze-dried food that you would take on camping trips. Each package only has so many calories, so you find yourself eating multiple of those and supplementing with lots of snacks such as Clif Bars or, my favorite, Babybel cheeses (which I bring myself). You also have to be really careful of dehydration. Your body’s reaction to extreme cold is to reduce blood flow to your skin, which generally results in less liquid in your body. We have to drink constantly — water, cocoa, and coffee — to avoid dehydration.We only have access to the ice every two years with the Navy, so we try to make the most of our time. In the several-day lead-up to our field expedition, my research partner Ben and I were really pushing ourselves to ready our sensor nodes for deployment and probably not eating and drinking as regularly as we should. When we ventured to our sensor deployment site about 5 kilometers outside of camp, I had to learn to slow down so I didn’t sweat under my gear, as sweating in the extremely cold conditions can quickly lead to hypothermia. I also learned to pay more attention to exposed places on my face, as I got a bit of frostnip around my goggles.Operating in the Arctic is a fine balance: you can’t spend too much time out there, but you also can’t rush. More

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      in Energy

      Lidar helps gas industry find methane leaks and avoid costly losses

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      Each year, the U.S. energy industry loses an estimated 3 percent of its natural gas production, valued at $1 billion in revenue, to leaky infrastructure. Escaping invisibly into the air, these methane gas plumes can now be detected, imaged, and measured using a specialized lidar flown on small aircraft.This lidar is a product of Bridger Photonics, a leading methane-sensing company based in Bozeman, Montana. MIT Lincoln Laboratory developed the lidar’s optical-power amplifier, a key component of the system, by advancing its existing slab-coupled optical waveguide amplifier (SCOWA) technology. The methane-detecting lidar is 10 to 50 times more capable than other airborne remote sensors on the market.”This drone-capable sensor for imaging methane is a great example of Lincoln Laboratory technology at work, matched with an impactful commercial application,” says Paul Juodawlkis, who pioneered the SCOWA technology with Jason Plant in the Advanced Technology Division and collaborated with Bridger Photonics to enable its commercial application.Today, the product is being adopted widely, including by nine of the top 10 natural gas producers in the United States. “Keeping gas in the pipe is good for everyone — it helps companies bring the gas to market, improves safety, and protects the outdoors,” says Pete Roos, founder and chief innovation officer at Bridger. “The challenge with methane is that you can’t see it. We solved a fundamental problem with Lincoln Laboratory.”A laser source “miracle”In 2014, the Advanced Research Projects Agency-Energy (ARPA-E) was seeking a cost-effective and precise way to detect methane leaks. Highly flammable and a potent pollutant, methane gas (the primary constituent of natural gas) moves through the country via a vast and intricate pipeline network. Bridger submitted a research proposal in response to ARPA-E’s call and was awarded funding to develop a small, sensitive aerial lidar.Aerial lidar sends laser light down to the ground and measures the light that reflects back to the sensor. Such lidar is often used for producing detailed topography maps. Bridger’s idea was to merge topography mapping with gas measurements. Methane absorbs light at the infrared wavelength of 1.65 microns. Operating a laser at that wavelength could allow a lidar to sense the invisible plumes and measure leak rates.”This laser source was one of the hardest parts to get right. It’s a key element,” Roos says. His team needed a laser source with specific characteristics to emit powerfully enough at a wavelength of 1.65 microns to work from useful altitudes. Roos recalled the ARPA-E program manager saying they needed a “miracle” to pull it off.Through mutual connections, Bridger was introduced to a Lincoln Laboratory technology for optically amplifying laser signals: the SCOWA. When Bridger contacted Juodawlkis and Plant, they had been working on SCOWAs for a decade. Although they had never investigated SCOWAs at 1.65 microns, they thought that the fundamental technology could be extended to operate at that wavelength. Lincoln Laboratory received ARPA-E funding to develop 1.65-micron SCOWAs and provide prototype units to Bridger for incorporation into their gas-mapping lidar systems.”That was the miracle we needed,” Roos says.A legacy in laser innovationLincoln Laboratory has long been a leader in semiconductor laser and optical emitter technology. In 1962, the laboratory was among the first to demonstrate the diode laser, which is now the most widespread laser used globally. Several spinout companies, such as Lasertron and TeraDiode, have commercialized innovations stemming from the laboratory’s laser research, including those for fiber-optic telecommunications and metal-cutting applications.In the early 2000s, Juodawlkis, Plant, and others at the laboratory recognized a need for a stable, powerful, and bright single-mode semiconductor optical amplifier, which could enhance lidar and optical communications. They developed the SCOWA (slab-coupled optical waveguide amplifier) concept by extending earlier work on slab-coupled optical waveguide lasers (SCOWLs). The initial SCOWA was funded under the laboratory’s internal technology investment portfolio, a pool of R&D funding provided by the undersecretary of defense for research and engineering to seed new technology ideas. These ideas often mature into sponsored programs or lead to commercialized technology.”Soon, we developed a semiconductor optical amplifier that was 10 times better than anything that had ever been demonstrated before,” Plant says. Like other semiconductor optical amplifiers, the SCOWA guides laser light through semiconductor material. This process increases optical power as the laser light interacts with electrons, causing them to shed photons at the same wavelength as the input laser. The SCOWA’s unique light-guiding design enables it to reach much higher output powers, creating a powerful and efficient beam. They demonstrated SCOWAs at various wavelengths and applied the technology to projects for the Department of Defense.When Bridger Photonics reached out to Lincoln Laboratory, the most impactful application of the device yet emerged. Working iteratively through the ARPA-E funding and a Cooperative Research and Development Agreement (CRADA), the team increased Bridger’s laser power by more than tenfold. This power boost enabled them to extend the range of the lidar to elevations over 1,000 feet.”Lincoln Laboratory had the knowledge of what goes on inside the optical amplifier — they could take our input, adjust the recipe, and make a device that worked very well for us,” Roos says.The Gas Mapping Lidar was commercially released in 2019. That same year, the product won an R&D 100 Award, recognizing it as a revolutionary advancement in the marketplace.A technology transfer takes offToday, the United States is the world’s largest natural gas supplier, driving growth in the methane-sensing market. Bridger Photonics deploys its Gas Mapping Lidar for customers nationwide, attaching the sensor to planes and drones and pinpointing leaks across the entire supply chain, from where gas is extracted, piped through the country, and delivered to businesses and homes. Customers buy the data from these scans to efficiently locate and repair leaks in their gas infrastructure. In January 2025, the Environmental Protection Agency provided regulatory approval for the technology.According to Bruce Niemeyer, president of Chevron’s shale and tight operations, the lidar capability has been game-changing: “Our goal is simple — keep methane in the pipe. This technology helps us assure we are doing that … It can find leaks that are 10 times smaller than other commercial providers are capable of spotting.”At Lincoln Laboratory, researchers continue to innovate new devices in the national interest. The SCOWA is one of many technologies in the toolkit of the laboratory’s Microsystems Prototyping Foundry, which will soon be expanded to include a new Compound Semiconductor Laboratory – Microsystem Integration Facility. Government, industry, and academia can access these facilities through government-funded projects, CRADAs, test agreements, and other mechanisms.At the direction of the U.S. government, the laboratory is also seeking industry transfer partners for a technology that couples SCOWA with a photonic integrated circuit platform. Such a platform could advance quantum computing and sensing, among other applications.”Lincoln Laboratory is a national resource for semiconductor optical emitter technology,” Juodawlkis says. More

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      in Energy

      Confronting the AI/energy conundrum

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      The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power.“We’re at a cusp of potentially gigantic change throughout the economy,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, at MITEI’s Spring Symposium, “AI and energy: Peril and promise,” held on May 13. The event brought together experts from industry, academia, and government to explore solutions to what Green described as both “local problems with electric supply and meeting our clean energy targets” while seeking to “reap the benefits of AI without some of the harms.” The challenge of data center energy demand and potential benefits of AI to the energy transition is a research priority for MITEI.AI’s startling energy demandsFrom the start, the symposium highlighted sobering statistics about AI’s appetite for electricity. After decades of flat electricity demand in the United States, computing centers now consume approximately 4 percent of the nation’s electricity. Although there is great uncertainty, some projections suggest this demand could rise to 12-15 percent by 2030, largely driven by artificial intelligence applications.Vijay Gadepally, senior scientist at MIT’s Lincoln Laboratory, emphasized the scale of AI’s consumption. “The power required for sustaining some of these large models is doubling almost every three months,” he noted. “A single ChatGPT conversation uses as much electricity as charging your phone, and generating an image consumes about a bottle of water for cooling.”Facilities requiring 50 to 100 megawatts of power are emerging rapidly across the United States and globally, driven both by casual and institutional research needs relying on large language programs such as ChatGPT and Gemini. Gadepally cited congressional testimony by Sam Altman, CEO of OpenAI, highlighting how fundamental this relationship has become: “The cost of intelligence, the cost of AI, will converge to the cost of energy.”“The energy demands of AI are a significant challenge, but we also have an opportunity to harness these vast computational capabilities to contribute to climate change solutions,” said Evelyn Wang, MIT vice president for energy and climate and the former director at the Advanced Research Projects Agency-Energy (ARPA-E) at the U.S. Department of Energy.Wang also noted that innovations developed for AI and data centers — such as efficiency, cooling technologies, and clean-power solutions — could have broad applications beyond computing facilities themselves.Strategies for clean energy solutionsThe symposium explored multiple pathways to address the AI-energy challenge. Some panelists presented models suggesting that while artificial intelligence may increase emissions in the short term, its optimization capabilities could enable substantial emissions reductions after 2030 through more efficient power systems and accelerated clean technology development.Research shows regional variations in the cost of powering computing centers with clean electricity, according to Emre Gençer, co-founder and CEO of Sesame Sustainability and former MITEI principal research scientist. Gençer’s analysis revealed that the central United States offers considerably lower costs due to complementary solar and wind resources. However, achieving zero-emission power would require massive battery deployments — five to 10 times more than moderate carbon scenarios — driving costs two to three times higher.“If we want to do zero emissions with reliable power, we need technologies other than renewables and batteries, which will be too expensive,” Gençer said. He pointed to “long-duration storage technologies, small modular reactors, geothermal, or hybrid approaches” as necessary complements.Because of data center energy demand, there is renewed interest in nuclear power, noted Kathryn Biegel, manager of R&D and corporate strategy at Constellation Energy, adding that her company is restarting the reactor at the former Three Mile Island site, now called the “Crane Clean Energy Center,” to meet this demand. “The data center space has become a major, major priority for Constellation,” she said, emphasizing how their needs for both reliability and carbon-free electricity are reshaping the power industry.Can AI accelerate the energy transition?Artificial intelligence could dramatically improve power systems, according to Priya Donti, assistant professor and the Silverman Family Career Development Professor in MIT’s Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. She showcased how AI can accelerate power grid optimization by embedding physics-based constraints into neural networks, potentially solving complex power flow problems at “10 times, or even greater, speed compared to your traditional models.”AI is already reducing carbon emissions, according to examples shared by Antonia Gawel, global director of sustainability and partnerships at Google. Google Maps’ fuel-efficient routing feature has “helped to prevent more than 2.9 million metric tons of GHG [greenhouse gas] emissions reductions since launch, which is the equivalent of taking 650,000 fuel-based cars off the road for a year,” she said. Another Google research project uses artificial intelligence to help pilots avoid creating contrails, which represent about 1 percent of global warming impact.AI’s potential to speed materials discovery for power applications was highlighted by Rafael Gómez-Bombarelli, the Paul M. Cook Career Development Associate Professor in the MIT Department of Materials Science and Engineering. “AI-supervised models can be trained to go from structure to property,” he noted, enabling the development of materials crucial for both computing and efficiency.Securing growth with sustainabilityThroughout the symposium, participants grappled with balancing rapid AI deployment against environmental impacts. While AI training receives most attention, Dustin Demetriou, senior technical staff member in sustainability and data center innovation at IBM, quoted a World Economic Forum article that suggested that “80 percent of the environmental footprint is estimated to be due to inferencing.” Demetriou emphasized the need for efficiency across all artificial intelligence applications.Jevons’ paradox, where “efficiency gains tend to increase overall resource consumption rather than decrease it” is another factor to consider, cautioned Emma Strubell, the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Strubell advocated for viewing computing center electricity as a limited resource requiring thoughtful allocation across different applications.Several presenters discussed novel approaches for integrating renewable sources with existing grid infrastructure, including potential hybrid solutions that combine clean installations with existing natural gas plants that have valuable grid connections already in place. These approaches could provide substantial clean capacity across the United States at reasonable costs while minimizing reliability impacts.Navigating the AI-energy paradoxThe symposium highlighted MIT’s central role in developing solutions to the AI-electricity challenge.Green spoke of a new MITEI program on computing centers, power, and computation that will operate alongside the comprehensive spread of MIT Climate Project research. “We’re going to try to tackle a very complicated problem all the way from the power sources through the actual algorithms that deliver value to the customers — in a way that’s going to be acceptable to all the stakeholders and really meet all the needs,” Green said.Participants in the symposium were polled about priorities for MIT’s research by Randall Field, MITEI director of research. The real-time results ranked “data center and grid integration issues” as the top priority, followed by “AI for accelerated discovery of advanced materials for energy.”In addition, attendees revealed that most view AI’s potential regarding power as a “promise,” rather than a “peril,” although a considerable portion remain uncertain about the ultimate impact. When asked about priorities in power supply for computing facilities, half of the respondents selected carbon intensity as their top concern, with reliability and cost following. More

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      in Energy

      Q&A: The climate impact of generative AI

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      Vijay Gadepally, a senior staff member at MIT Lincoln Laboratory, leads a number of projects at the Lincoln Laboratory Supercomputing Center (LLSC) to make computing platforms, and the artificial intelligence systems that run on them, more efficient. Here, Gadepally discusses the increasing use of generative AI in everyday tools, its hidden environmental impact, and some of the ways that Lincoln Laboratory and the greater AI community can reduce emissions for a greener future.Q: What trends are you seeing in terms of how generative AI is being used in computing?A: Generative AI uses machine learning (ML) to create new content, like images and text, based on data that is inputted into the ML system. At the LLSC we design and build some of the largest academic computing platforms in the world, and over the past few years we’ve seen an explosion in the number of projects that need access to high-performance computing for generative AI. We’re also seeing how generative AI is changing all sorts of fields and domains — for example, ChatGPT is already influencing the classroom and the workplace faster than regulations can seem to keep up.We can imagine all sorts of uses for generative AI within the next decade or so, like powering highly capable virtual assistants, developing new drugs and materials, and even improving our understanding of basic science. We can’t predict everything that generative AI will be used for, but I can certainly say that with more and more complex algorithms, their compute, energy, and climate impact will continue to grow very quickly.Q: What strategies is the LLSC using to mitigate this climate impact?A: We’re always looking for ways to make computing more efficient, as doing so helps our data center make the most of its resources and allows our scientific colleagues to push their fields forward in as efficient a manner as possible.As one example, we’ve been reducing the amount of power our hardware consumes by making simple changes, similar to dimming or turning off lights when you leave a room. In one experiment, we reduced the energy consumption of a group of graphics processing units by 20 percent to 30 percent, with minimal impact on their performance, by enforcing a power cap. This technique also lowered the hardware operating temperatures, making the GPUs easier to cool and longer lasting.Another strategy is changing our behavior to be more climate-aware. At home, some of us might choose to use renewable energy sources or intelligent scheduling. We are using similar techniques at the LLSC — such as training AI models when temperatures are cooler, or when local grid energy demand is low.We also realized that a lot of the energy spent on computing is often wasted, like how a water leak increases your bill but without any benefits to your home. We developed some new techniques that allow us to monitor computing workloads as they are running and then terminate those that are unlikely to yield good results. Surprisingly, in a number of cases we found that the majority of computations could be terminated early without compromising the end result.Q: What’s an example of a project you’ve done that reduces the energy output of a generative AI program?A: We recently built a climate-aware computer vision tool. Computer vision is a domain that’s focused on applying AI to images; so, differentiating between cats and dogs in an image, correctly labeling objects within an image, or looking for components of interest within an image.In our tool, we included real-time carbon telemetry, which produces information about how much carbon is being emitted by our local grid as a model is running. Depending on this information, our system will automatically switch to a more energy-efficient version of the model, which typically has fewer parameters, in times of high carbon intensity, or a much higher-fidelity version of the model in times of low carbon intensity.By doing this, we saw a nearly 80 percent reduction in carbon emissions over a one- to two-day period. We recently extended this idea to other generative AI tasks such as text summarization and found the same results. Interestingly, the performance sometimes improved after using our technique!Q: What can we do as consumers of generative AI to help mitigate its climate impact?A: As consumers, we can ask our AI providers to offer greater transparency. For example, on Google Flights, I can see a variety of options that indicate a specific flight’s carbon footprint. We should be getting similar kinds of measurements from generative AI tools so that we can make a conscious decision on which product or platform to use based on our priorities.We can also make an effort to be more educated on generative AI emissions in general. Many of us are familiar with vehicle emissions, and it can help to talk about generative AI emissions in comparative terms. People may be surprised to know, for example, that one image-generation task is roughly equivalent to driving four miles in a gas car, or that it takes the same amount of energy to charge an electric car as it does to generate about 1,500 text summarizations.There are many cases where customers would be happy to make a trade-off if they knew the trade-off’s impact.Q: What do you see for the future?A: Mitigating the climate impact of generative AI is one of those problems that people all over the world are working on, and with a similar goal. We’re doing a lot of work here at Lincoln Laboratory, but its only scratching at the surface. In the long term, data centers, AI developers, and energy grids will need to work together to provide “energy audits” to uncover other unique ways that we can improve computing efficiencies. We need more partnerships and more collaboration in order to forge ahead.If you’re interested in learning more, or collaborating with Lincoln Laboratory on these efforts, please contact Vijay Gadepally.

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      in Energy

      How hard is it to prevent recurring blackouts in Puerto Rico?

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      Researchers at MIT’s Laboratory for Information and Decision Systems (LIDS) have shown that using decision-making software and dynamic monitoring of weather and energy use can significantly improve resiliency in the face of weather-related outages, and can also help to efficiently integrate renewable energy sources into the grid.The researchers point out that the system they suggest might have prevented or at least lessened the kind of widespread power outage that Puerto Rico experienced last week by providing analysis to guide rerouting of power through different lines and thus limit the spread of the outage.The computer platform, which the researchers describe as DyMonDS, for Dynamic Monitoring and Decision Systems, can be used to enhance the existing operating and planning practices used in the electric industry. The platform supports interactive information exchange and decision-making between the grid operators and grid-edge users — all the distributed power sources, storage systems and software that contribute to the grid. It also supports optimization of available resources and controllable grid equipment as system conditions vary. It further lends itself to implementing cooperative decision-making by different utility- and non-utility-owned electric power grid users, including portfolios of mixed resources, users, and storage. Operating and planning the interactions of the end-to-end high-voltage transmission grid with local distribution grids and microgrids represents another major potential use of this platform.This general approach was illustrated using a set of publicly-available data on both meteorology and details of electricity production and distribution in Puerto Rico. An extended AC Optimal Power Flow software developed by SmartGridz Inc. is used for system-level optimization of controllable equipment. This provides real-time guidance for deciding how much power, and through which transmission lines, should be channeled by adjusting plant dispatch and voltage-related set points, and in extreme cases, where to reduce or cut power in order to maintain physically-implementable service for as many customers as possible. The team found that the use of such a system can help to ensure that the greatest number of critical services maintain power even during a hurricane, and at the same time can lead to a substantial decrease in the need for construction of new power plants thanks to more efficient use of existing resources.The findings are described in a paper in the journal Foundations and Trends in Electric Energy Systems, by MIT LIDS researchers Marija Ilic and Laurentiu Anton, along with recent alumna Ramapathi Jaddivada.“Using this software,” Ilic says, they show that “even during bad weather, if you predict equipment failures, and by using that information exchange, you can localize the effect of equipment failures and still serve a lot of customers, 50 percent of customers, when otherwise things would black out.”Anton says that “the way many grids today are operated is sub-optimal.” As a result, “we showed how much better they could do even under normal conditions, without any failures, by utilizing this software.” The savings resulting from this optimization, under everyday conditions, could be in the tens of percents, they say.The way utility systems plan currently, Ilic says, “usually the standard is that they have to build enough capacity and operate in real time so that if one large piece of equipment fails, like a large generator or transmission line, you still serve customers in an uninterrupted way. That’s what’s called N-minus-1.” Under this policy, if one major component of the system fails, they should be able to maintain service for at least 30 minutes. That system allows utilities to plan for how much reserve generating capacity they need to have on hand. That’s expensive, Ilic points out, because it means maintaining this reserve capacity all the time, even under normal operating conditions when it’s not needed.In addition, “right now there are no criteria for what I call N-minus-K,” she says. If bad weather causes five pieces of equipment to fail at once, “there is no software to help utilities decide what to schedule” in terms of keeping the most customers, and the most important services such as hospitals and emergency services, provided with power. They showed that even with 50 percent of the infrastructure out of commission, it would still be possible to keep power flowing to a large proportion of customers.Their work on analyzing the power situation in Puerto Rico started after the island had been devastated by hurricanes Irma and Maria. Most of the electric generation capacity is in the south, yet the largest loads are in San Juan, in the north, and Mayaguez in the west. When transmission lines get knocked down, a lot of rerouting of power needs to happen quickly.With the new systems, “the software finds the optimal adjustments for set points,” for example, changing voltages can allow for power to be redirected through less-congested lines, or can be increased to lessen power losses, Anton says.The software also helps in the long-term planning for the grid. As many fossil-fuel power plants are scheduled to be decommissioned soon in Puerto Rico, as they are in many other places, planning for how to replace that power without having to resort to greenhouse gas-emitting sources is a key to achieving carbon-reduction goals. And by analyzing usage patterns, the software can guide the placement of new renewable power sources where they can most efficiently provide power where and when it’s needed.As plants are retired or as components are affected by weather, “We wanted to ensure the dispatchability of power when the load changes,” Anton says, “but also when crucial components are lost, to ensure the robustness at each step of the retirement schedule.”One thing they found was that “if you look at how much generating capacity exists, it’s more than the peak load, even after you retire a few fossil plants,” Ilic says. “But it’s hard to deliver.” Strategic planning of new distribution lines could make a big difference.Jaddivada, director of innovation at SmartGridz, says that “we evaluated different possible architectures in Puerto Rico, and we showed the ability of this software to ensure uninterrupted electricity service. This is the most important challenge utilities have today. They have to go through a computationally tedious process to make sure the grid functions for any possible outage in the system. And that can be done in a much more efficient way through the software that the company  developed.”The project was a collaborative effort between the MIT LIDS researchers and others at MIT Lincoln Laboratory, the Pacific Northwest National Laboratory, with overall help of SmartGridz software.  More

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      in Environment

      Surface-based sonar system could rapidly map the ocean floor at high resolution

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      On June 18, 2023, the Titan submersible was about an hour-and-a-half into its two-hour descent to the Titanic wreckage at the bottom of the Atlantic Ocean when it lost contact with its support ship. This cease in communication set off a frantic search for the tourist submersible and five passengers onboard, located about two miles below the ocean’s surface.Deep-ocean search and recovery is one of the many missions of military services like the U.S. Coast Guard Office of Search and Rescue and the U.S. Navy Supervisor of Salvage and Diving. For this mission, the longest delays come from transporting search-and-rescue equipment via ship to the area of interest and comprehensively surveying that area. A search operation on the scale of that for Titan — which was conducted 420 nautical miles from the nearest port and covered 13,000 square kilometers, an area roughly twice the size of Connecticut — could take weeks to complete. The search area for Titan is considered relatively small, focused on the immediate vicinity of the Titanic. When the area is less known, operations could take months. (A remotely operated underwater vehicle deployed by a Canadian vessel ended up finding the debris field of Titan on the seafloor, four days after the submersible had gone missing.)A research team from MIT Lincoln Laboratory and the MIT Department of Mechanical Engineering’s Ocean Science and Engineering lab is developing a surface-based sonar system that could accelerate the timeline for small- and large-scale search operations to days. Called the Autonomous Sparse-Aperture Multibeam Echo Sounder, the system scans at surface-ship rates while providing sufficient resolution to find objects and features in the deep ocean, without the time and expense of deploying underwater vehicles. The echo sounder — which features a large sonar array using a small set of autonomous surface vehicles (ASVs) that can be deployed via aircraft into the ocean — holds the potential to map the seabed at 50 times the coverage rate of an underwater vehicle and 100 times the resolution of a surface vessel.

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      Autonomous Sparse-Aperture Multibeam Echo SounderVideo: MIT Lincoln Laboratory

      “Our array provides the best of both worlds: the high resolution of underwater vehicles and the high coverage rate of surface ships,” says co–principal investigator Andrew March, assistant leader of the laboratory’s Advanced Undersea Systems and Technology Group. “Though large surface-based sonar systems at low frequency have the potential to determine the materials and profiles of the seabed, they typically do so at the expense of resolution, particularly with increasing ocean depth. Our array can likely determine this information, too, but at significantly enhanced resolution in the deep ocean.”Underwater unknownOceans cover 71 percent of Earth’s surface, yet more than 80 percent of this underwater realm remains undiscovered and unexplored. Humans know more about the surface of other planets and the moon than the bottom of our oceans. High-resolution seabed maps would not only be useful to find missing objects like ships or aircraft, but also to support a host of other scientific applications: understanding Earth’s geology, improving forecasting of ocean currents and corresponding weather and climate impacts, uncovering archaeological sites, monitoring marine ecosystems and habitats, and identifying locations containing natural resources such as mineral and oil deposits.Scientists and governments worldwide recognize the importance of creating a high-resolution global map of the seafloor; the problem is that no existing technology can achieve meter-scale resolution from the ocean surface. The average depth of our oceans is approximately 3,700 meters. However, today’s technologies capable of finding human-made objects on the seabed or identifying person-sized natural features — these technologies include sonar, lidar, cameras, and gravitational field mapping — have a maximum range of less than 1,000 meters through water.Ships with large sonar arrays mounted on their hull map the deep ocean by emitting low-frequency sound waves that bounce off the seafloor and return as echoes to the surface. Operation at low frequencies is necessary because water readily absorbs high-frequency sound waves, especially with increasing depth; however, such operation yields low-resolution images, with each image pixel representing a football field in size. Resolution is also restricted because sonar arrays installed on large mapping ships are already using all of the available hull space, thereby capping the sonar beam’s aperture size. By contrast, sonars on autonomous underwater vehicles (AUVs) that operate at higher frequencies within a few hundred meters of the seafloor generate maps with each pixel representing one square meter or less, resulting in 10,000 times more pixels in that same football field–sized area. However, this higher resolution comes with trade-offs: AUVs are time-consuming and expensive to deploy in the deep ocean, limiting the amount of seafloor that can be mapped; they have a maximum range of about 1,000 meters before their high-frequency sound gets absorbed; and they move at slow speeds to conserve power. The area-coverage rate of AUVs performing high-resolution mapping is about 8 square kilometers per hour; surface vessels map the deep ocean at more than 50 times that rate.A solution surfacesThe Autonomous Sparse-Aperture Multibeam Echo Sounder could offer a cost-effective approach to high-resolution, rapid mapping of the deep seafloor from the ocean’s surface. A collaborative fleet of about 20 ASVs, each hosting a small sonar array, effectively forms a single sonar array 100 times the size of a large sonar array installed on a ship. The large aperture achieved by the array (hundreds of meters) produces a narrow beam, which enables sound to be precisely steered to generate high-resolution maps at low frequency. Because very few sonars are installed relative to the array’s overall size (i.e., a sparse aperture), the cost is tractable.However, this collaborative and sparse setup introduces some operational challenges. First, for coherent 3D imaging, the relative position of each ASV’s sonar subarray must be accurately tracked through dynamic ocean-induced motions. Second, because sonar elements are not placed directly next to each other without any gaps, the array suffers from a lower signal-to-noise ratio and is less able to reject noise coming from unintended or undesired directions. To mitigate these challenges, the team has been developing a low-cost precision-relative navigation system and leveraging acoustic signal processing tools and new ocean-field estimation algorithms. The MIT campus collaborators are developing algorithms for data processing and image formation, especially to estimate depth-integrated water-column parameters. These enabling technologies will help account for complex ocean physics, spanning physical properties like temperature, dynamic processes like currents and waves, and acoustic propagation factors like sound speed.Processing for all required control and calculations could be completed either remotely or onboard the ASVs. For example, ASVs deployed from a ship or flying boat could be controlled and guided remotely from land via a satellite link or from a nearby support ship (with direct communications or a satellite link), and left to map the seabed for weeks or months at a time until maintenance is needed. Sonar-return health checks and coarse seabed mapping would be conducted on board, while full, high-resolution reconstruction of the seabed would require a supercomputing infrastructure on land or on a support ship.”Deploying vehicles in an area and letting them map for extended periods of time without the need for a ship to return home to replenish supplies and rotate crews would significantly simplify logistics and operating costs,” says co–principal investigator Paul Ryu, a researcher in the Advanced Undersea Systems and Technology Group.Since beginning their research in 2018, the team has turned their concept into a prototype. Initially, the scientists built a scale model of a sparse-aperture sonar array and tested it in a water tank at the laboratory’s Autonomous Systems Development Facility. Then, they prototyped an ASV-sized sonar subarray and demonstrated its functionality in Gloucester, Massachusetts. In follow-on sea tests in Boston Harbor, they deployed an 8-meter array containing multiple subarrays equivalent to 25 ASVs locked together; with this array, they generated 3D reconstructions of the seafloor and a shipwreck. Most recently, the team fabricated, in collaboration with Woods Hole Oceanographic Institution, a first-generation, 12-foot-long, all-electric ASV prototype carrying a sonar array underneath. With this prototype, they conducted preliminary relative navigation testing in Woods Hole, Massachusetts and Newport, Rhode Island. Their full deep-ocean concept calls for approximately 20 such ASVs of a similar size, likely powered by wave or solar energy.This work was funded through Lincoln Laboratory’s internally administered R&D portfolio on autonomous systems. The team is now seeking external sponsorship to continue development of their ocean floor–mapping technology, which was recognized with a 2024 R&D 100 Award.  More