The race to develop renewable energy technologies

In the early 20th century, just as electric grids were starting to transform daily life, an unlikely advocate for renewable energy voiced his concerns about burning fossil fuels. Thomas Edison expressed dismay over using combustion instead of renewable resources in a 1910 interview for Elbert Hubbard’s anthology, “Little Journeys to the Homes of the Great.”

“This scheme of combustion to get power makes me sick to think of — it is so wasteful,” Edison said. “You see, we should utilize natural forces and thus get all of our power. Sunshine is a form of energy, and the winds and the tides are manifestations of energy. Do we use them? Oh, no! We burn up wood and coal, as renters burn up the front fence for fuel.”

Over a century later, roughly 80 percent of global energy consumption still comes from burning fossil fuels. As the impact of climate change on the environment becomes increasingly drastic, there is a mounting sense of urgency for researchers and engineers to develop scalable renewable energy solutions.

“Even 100 years ago, Edison understood that we cannot replace combustion with a single alternative,” adds Reshma Rao PhD '19, a postdoc in MIT’s Electrochemical Energy Lab who included Edison’s quote in her doctoral thesis. “We must look to different solutions that might vary temporally and geographically depending on resource availability.”

Rao is one of many researchers across MIT’s Department of Mechanical Engineering who have entered the race to develop energy conversion and storage technologies from renewable sources such as wind, wave, solar, and thermal.

Harnessing energy from waves

When it comes to renewable energy, waves have other resources beat in two respects. First, unlike solar, waves offer a consistent energy source regardless of time of day. Second, waves provide much greater energy density than wind due to water’s heavier mass.

Despite these advantages, wave-energy harvesting is still in its infancy. Unlike wind and solar, there is no consensus in the field of wave hydrodynamics on how to efficiently capture and convert wave energy. Dick K.P. Yue, Philip J. Solondz Professor of Engineering, is hoping to change that.

“My group has been looking at new paradigms,” explains Yue. “Rather than tinkering with small improvements, we want to develop a new way of thinking about the wave-energy problem.”

One aspect of that paradigm is determining the optimal geometry of wave-energy converters (WECs). Graduate student Emma Edwards has been developing a systematic methodology to determine what kind of shape WECs should be.

“If we can optimize the shape of WECs for maximizing extractable power, wave energy could move significantly closer to becoming an economically viable source of renewable energy,” says Edwards. 

Another aspect of the wave-energy paradigm Yue’s team is working on is finding the optimal configuration for WECs in the water. Grgur Tokić PhD '16, an MIT alum and current postdoc working in Yue’s group, is building a case for optimal configurations of WECs in large arrays, rather than as stand-alone devices.

Before being placed in the water, WECs are tuned for their particular environment. This tuning involves considerations like predicted wave frequency and prevailing wind direction. According to Tokić and Yue, if WECs are configured in an array, this tuning could occur in real time, maximizing energy-harvesting potential.

In an array, “sentry” WECs could gather measurements about waves such as amplitude, frequency, and direction. Using wave reconstructing and forecasting, these WECs could then communicate information about conditions to other WECs in the array wirelessly, enabling them to tune minute-by-minute in response to current wave conditions.

“If an array of WECs can tune fast enough so they are optimally configured for their current environment, now we are talking serious business,” explains Yue. “Moving toward arrays opens up the possibilities of significant advances and gains many-times-over non-interacting, isolated devices.”

By examining the optimal size and configuration of WECs using theoretical and computational methods, Yue’s group hopes to develop potentially game-changing frameworks for harnessing the power of waves.

Accelerating the discovery of photovoltaics

The amount of solar energy that reaches the Earth’s surface offers a tantalizing prospect in the quest for renewable energy. Every hour, an estimated 430 quintillion joules of energy is delivered to Earth from the sun. That’s the equivalent of one year’s worth of global energy consumption by humans.

Tonio Buonassisi, professor of mechanical engineering, has dedicated his entire career to developing technologies that harness this energy and convert it into usable electricity. But time, he says, is of the essence. “When you consider what we are up against in terms of climate change, it becomes increasingly clear we are running out of time,” he says.

For solar energy to have a meaningful impact, according to Buonassisi, researchers need to develop solar cell materials that are efficient, scalable, cost-effective, and reliable. These four variables pose a challenge for engineers — rather than develop a material that satisfies just one of these factors, they need to create one that ticks off all four boxes and can be moved to market as quickly as possible. “If it takes us 75 years to get a solar cell that does all of these things to market, it’s not going to help us solve this problem. We need to get it to market in the next five years,” Buonassisi adds.

To accelerate the discovery and testing of new materials, Buonassisi’s team has developed a process that uses a combination of machine learning and high-throughput experimentation — a type of experimentation that enables a large quantity of materials to be screened at the same time. The result is a 10-fold increase in the speed of discovery and analysis for new solar cell materials.

“Machine learning is our navigational tool,” explains Buonassisi. “It can de-bottleneck the cycle of learning so we can grind through material candidates and find one that satisfies all four variables.”

Shijing Sun, a research scientist in Buonassisi’s group, used a combination of machine learning and high-throughput experiments to quickly assess and test perovskite solar cells.

“We use machine learning to accelerate the materials discovery, and developed an algorithm that directs us to the next sampling point and guides our next experiment,” Sun says. Previously, it would take three to five hours to classify a set of solar cell materials. The machine learning algorithm can classify materials in just five minutes.

Using this method, Sun and Buonassisi made 96 tested compositions. Of those, two perovskite materials hold promise and will be tested further.

By using machine learning as a tool for inverse design, the research team hopes to assess thousands of compounds that could lead to the development of a material that enables the large-scale adoption of solar energy conversion. “If in the next five years we can develop that material using the set of productivity tools we’ve developed, it can help us secure the best possible future that we can,” adds Buonassisi.

New materials to trap heat

While Buonassisi’s team is focused on developing solutions that directly convert solar energy into electricity, researchers including Gang Chen, Carl Richard Soderberg Professor of Power Engineering, are working on technologies that convert sunlight into heat. Thermal energy from the heat is then used to provide electricity.

“For the past 20 years, I’ve been working on materials that convert heat into electricity,” says Chen. While much of this materials research is on the nanoscale, Chen and his team at the NanoEngineering Group are no strangers to large-scale experimental systems. They previously built a to-scale receiver system that used concentrating solar thermal power (CSP).

In CSP, sunlight is used to heat up a thermal fluid, such as oil or molten salt. That fluid is then either used to generate electricity by running an engine, such as a steam turbine, or stored for later use.

Over the course of a four-year project funded by the U.S. Department of Energy, Chen’s team built a CSP receiver at MIT’s Bates Research and Engineering Center in Middleton, Massachusetts. They developed the Solar Thermal Aerogel Receiver — nicknamed STAR.

The system relied on mirrors known as Fresnel reflectors to direct sunlight to pipes containing thermal fluid. Typically, for fluid to effectively trap the heat generated by this reflected sunlight, it would need to be encased in a high-cost vacuum tube. In STAR, however, Chen’s team utilized a transparent aerogel that can trap heat at incredibly high temperatures — removing the need for expensive vacuum enclosures. While letting in over 95 percent of the incoming sunlight, the aerogel retains its insulating properties, preventing heat from escaping the receiver.

In addition to being more efficient than traditional vacuum receivers, the aerogel receivers enabled new configurations for the CSP solar reflectors. The reflecting mirrors were flatter and more compact than conventionally used parabolic receivers, resulting in a savings of material. 

“Cost is everything with energy applications, so the fact STAR was cheaper than most thermal energy receivers, in addition to being more efficient, was important,” adds Svetlana Boriskina, a research scientist working on Chen’s team. 

After the conclusion of the project in 2018, Chen and Wang have continued their collaboration to explore solar thermal applications for the aerogel material used in STAR. They recently used the aerogel in a device that contained a heat-absorbing material. When placed on a roof on MIT’s campus, the heat-absorbing material, which was covered by a layer of the aerogel, reached an amazingly high temperature of 220 degrees Celsius. The outside air temperature, for comparison, was a chilly 0 C. Unlike STAR, this new system doesn’t require Fresnel reflectors to direct sunlight to the thermal material.

“Our latest work using the aerogel enables sunlight concentration without focusing optics to harness thermal energy,” explains Chen. “If you aren’t using focusing optics, you can develop a system that is easier to use and cheaper than traditional receivers.”

The aerogel device could potentially be further developed into a system that powers heating and cooling systems in homes.

Solving the storage problem

While CSP receivers like STAR offer some energy storage capabilities, there is a push to develop more robust energy storage systems for renewable technologies. Storing energy for later use when resources aren’t supplying a consistent stream of energy — for example, when the sun is covered by clouds, or there is little-to-no wind — will be crucial for the adoption of renewable energy on the grid. To solve this problem, researchers are developing new storage technologies.  

Asegun Henry, Robert N. Noyce Career Development Professor, who like Chen has developed CSP technologies, has created a new storage system that has been dubbed “sun in a box.” Using two tanks, excess energy can be stored in white-hot molten silicon. When this excess energy is needed, mounted photovoltaic cells can be actuated into place to convert the white-hot light from the silicon back into electricity.

“It’s a true battery that can work with any type of energy conversion,” adds Henry.

Betar Gallant, ABS Career Development Professor, meanwhile, is exploring ways to improve the energy density of today’s electrochemical batteries by designing new storage materials that are more cost-effective and versatile for storing cleanly generated energy. Rather than develop these materials using metals that are extracted through energy-intensive mining, she aims to build batteries using more earth-abundant materials.

“Ideally, we want to create a battery that can match the irregular supply of solar or wind energy that peak at different times without degrading, as today’s batteries do” explains Gallant.

In addition to working on lithium-ion batteries, like Gallant, Yang Shao-Horn, W.M. Keck Professor of Energy, and postdoc Reshma Rao are developing technologies that can directly convert renewable energy to fuels.

“If we want to store energy at scale going beyond lithium ion batteries, we need to use resources that are abundant,” Rao explains. In their electrochemical technology, Rao and Shao-Horn utilize one of the most abundant resources — liquid water.

Using an active catalyst and electrodes, water is split into hydrogen and oxygen in a series of chemical reactions. The hydrogen becomes an energy carrier and can be stored for later use in a fuel cell. To convert the energy stored in the hydrogen back into electricity, the reactions are reversed. The only by-product of this reaction is water.  

“If we can get and store hydrogen sustainably, we can basically electrify our economy using renewables like wind, wave, or solar,” says Rao.

Rao has broken down every fundamental reaction that takes place within this process. In addition to focusing on the electrode-electrolyte interface involved, she is developing next-generation catalysts to drive these reactions.  

“This work is at the frontier of the fundamental understanding of active sites catalyzing water splitting for hydrogen-based fuels from solar and wind to decarbonize transport and industry,” adds Shao-Horn.

Securing a sustainable future

While shifting from a grid powered primarily by fossil fuels to a grid powered by renewable energy seems like a herculean task, there have been promising developments in the past decade. A report released prior to the UN Global Climate Action Summit in September showed that, thanks to $2.6 trillion of investment, renewable energy conversion has quadrupled since 2010.

In a statement after the release of the report, Inger Andersen, executive director of the UN Environment Program, stressed the correlation between investing in renewable energy and securing a sustainable future for humankind. “It is clear that we need to rapidly step up the pace of the global switch to renewables if we are to meet international climate and development goals,” Andersen said.

No single conversion or storage technology will be responsible for the shift from fossil fuels to renewable energy. It will require a tapestry of complementary solutions from researchers both here at MIT and across the globe.

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Intelligent Towing Tank propels human-robot-computer research

In its first year of operation, the Intelligent Towing Tank (ITT) conducted about 100,000 total experiments, essentially completing the equivalent of a PhD student’s five years’ worth of experiments in a matter of weeks.

The automated experimental facility, developed in the MIT Sea Grant Hydrodynamics Laboratory, automatically and adaptively performs, analyzes, and designs experiments exploring vortex-induced vibrations (VIVs). Important for engineering offshore ocean structures like marine drilling risers that connect underwater oil wells to the surface, VIVs remain somewhat of a phenomenon to researchers due to the high number of parameters involved.

Guided by active learning, the ITT conducts series of experiments wherein the parameters of each next experiment are selected by a computer. Using an “explore-and-exploit” methodology, the system dramatically reduces the number of experiments required to explore and map the complex forces governing VIVs.

What began as then-PhD candidate Dixia Fan’s quest to cut back on conducting a thousand or so laborious experiments — by hand — led to the design of the innovative system and a paper recently published in the journal Science Robotics.

Fan, now a postdoc, and a team of researchers from the MIT Sea Grant College Program and MIT’s Department of Mechanical Engineering, École Normale Supérieure de Rennes, and Brown University, reveal a potential paradigm shift in experimental research, where humans, computers, and robots can collaborate more effectively to accelerate scientific discovery.

The 33-foot whale of a tank comes alive, working without interruption or supervision on the venture at hand — in this case, exploring a canonical problem in the field of fluid-structure interactions. But the researchers envision applications of the active learning and automation approach to experimental research across disciplines, potentially leading to new insights and models in multi-input/multi-output nonlinear systems.

VIVs are inherently-nonlinear motions induced on a structure in an oncoming irregular cross-stream, which prove vexing to study. The researchers report that the number of experiments completed by the ITT is already comparable to the total number of experiments done to date worldwide on the subject of VIVs.

The reason for this is the large number of independent parameters, from flow velocity to pressure, involved in studying the complex forces at play. According to Fan, a systematic brute-force approach — blindly conducting 10 measurements per parameter in an eight-dimensional parametric space — would require 100 million experiments.

With the ITT, Fan and his collaborators have taken the problem into a wider parametric space than previously practicable to explore. “If we performed traditional techniques on the problem we studied,” he explains, “it would take 950 years to finish the experiment.” Clearly infeasible, so Fan and the team integrated a Gaussian process regression learning algorithm into the ITT. In doing so, the researchers reduced the experimental burden by several orders of magnitude, requiring only a few thousand experiments.

The robotic system automatically conducts an initial sequence of experiments, periodically towing a submerged structure along the length of the tank at a constant velocity. Then, the ITT takes partial control over the parameters of each next experiment by minimizing suitable acquisition functions of quantified uncertainties and adapting to achieve a range of objectives, like reduced drag.

Earlier this year, Fan was awarded an MIT Mechanical Engineering de Florez Award for "Outstanding Ingenuity and Creative Judgment" in the development of the ITT. “Dixia’s design of the Intelligent Towing Tank is an outstanding example of using novel methods to reinvigorate mature fields,” says Michael Triantafyllou, Henry L. and Grace Doherty Professor in Ocean Science and Engineering, who acted as Fan’s doctoral advisor.

Triantafyllou, a co-author on this paper and the director of the MIT Sea Grant College Program, says, “MIT Sea Grant has committed resources and funded projects using deep-learning methods in ocean-related problems for several years that are already paying off.” Funded by the National Oceanic and Atmospheric Administration and administered by the National Sea Grant Program, MIT Sea Grant is a federal-Institute partnership that brings the research and engineering core of MIT to bear on ocean-related challenges.

Fan’s research points to a number of others utilizing automation and artificial intelligence in science: At Caltech, a robot scientist named “Adam” generates and tests hypotheses; at the Defense Advanced Research Projects Agency, the Big Mechanism program reads tens of thousands of research papers to generate new models.

Similarly, the ITT applies human-computer-robot collaboration to accelerate experimental efforts. The system demonstrates a potential paradigm shift in conducting research, where automation and uncertainty quantification can considerably accelerate scientific discovery. The researchers assert that the machine learning methodology described in this paper can be adapted and applied in and beyond fluid mechanics, to other experimental fields.

Other contributors to the paper include George Karniadakis from Brown University, who is also affiliated with MIT Sea Grant; Gurvan Jodin from ENS Rennes; MIT PhD candidate in mechanical engineering Yu Ma; and Thomas Consi, Luca Bonfiglio, and Lily Keyes from MIT Sea Grant.

This work was supported by DARPA, Fariba Fahroo, and Jan Vandenbrande through an EQUiPS (Enabling Quantification of Uncertainty in Physical Systems) grant, as well as Shell, Subsea 7, and the MIT Sea Grant College Program.

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Understanding the impact of deep-sea mining

Resting atop Thomas Peacock’s desk is an ordinary-looking brown rock. Roughly the size of a potato, it has been at the center of decades of debate. Known as a polymetallic nodule, it spent 10 million years sitting on the deep seabed, 15,000 feet below sea level. The nodule contains nickel, cobalt, copper, and manganese — four minerals that are essential in energy storage.

“As society moves toward driving more electric vehicles and utilizing renewable energy, there will be an increased demand for these minerals, to manufacture the batteries necessary to decarbonize the economy,” says Peacock, a professor of mechanical engineering and the director of MIT’s Environmental Dynamics Lab (END Lab). He is part of an international team of researchers that has been trying to gain a better understanding the environmental impact of collecting polymetallic nodules, a process known as deep-sea mining.

The minerals found in the nodules, particularly cobalt and nickel, are key components of lithium-ion batteries. Currently, lithium-ion batteries offer the best energy density of any commercially available battery. This high energy density makes them ideal for use in everything from cellphones to electric vehicles, which require large amounts of energy within a compact space.

“Those two elements are expected to see a tremendous growth in demand due to energy storage,” says Richard Roth, director of MIT’s Materials Systems Laboratory.

While researchers are exploring alternative battery technologies such as sodium-ion batteries and flow batteries that utilize electrochemical cells, these technologies are far from commercialization.

“Few people expect any of these lithium-ion alternatives to be available in the next decade,” explains Roth. “Waiting for unknown future battery chemistries and technologies could significantly delay widespread adoption of electric vehicles.”

Vast amounts of specialty nickel will be also needed to build larger-scale batteries that will be required as societies look to shift from an electric grid powered by fossil fuels to one powered by renewable resources like solar, wind, wave, and thermal.

“The collection of nodules from the seabed is being considered as a new means for getting these materials, but before doing so it is imperative to fully understand the environmental impact of mining resources from the deep ocean and compare it to the environmental impact of mining resources on land,” explains Peacock.

After receiving seed funding from MIT’s Environmental Solutions Initiative (ESI), Peacock was able to apply his expertise in fluid dynamics to study how deep-sea mining could affect surrounding ecosystems.

Meeting the demand for energy storage

Currently, nickel and cobalt are extracted through land-based mining operations. Much of this mining occurs in the Democratic Republic of the Congo, which produces 60 percent of the world’s cobalt. These land-based mines often impact surrounding environments through the destruction of habitats, erosion, and soil and water contamination. There are also concerns that land-based mining, especially in politically unstable countries, might not be able to supply enough of these materials as the demand for batteries rises.

The swath of ocean located between Hawaii and the West Coast of the United States — also  known as the Clarion Clipperton Fracture Zone — is estimated to possess six times more cobalt and three times more nickel than all known land-based stores, as well as vast deposits of manganese and a substantial amount of copper.

While the seabed is abundant with these materials, little is known about the short- and long-term environmental effects of mining 15,000 feet below sea level. Peacock and his collaborator Professor Matthew Alford from the Scripps Institution of Oceanography and the University of California at San Diego are leading the quest to understand how the sediment plumes generated by the collection of nodules from the seabed will be carried by water currents.

“The key question is, if we decide to make a plume at site A, how far does it spread before eventually raining down on the sea floor?” explains Alford. “That ability to map the geography of the impact of sea floor mining is a crucial unknown right now.”

The research Peacock and Alford are conducting will help inform stakeholders about the potential environmental effects of deep-sea mining. One pressing matter is that draft exploitation regulations for deep-sea mining in areas beyond national jurisdiction are currently being negotiated by the International Seabed Authority (ISA), an independent organization established by the United Nations that regulates all mining activities on the sea floor. Peacock and Alford’s research will help guide the development of environmental standards and guidelines to be issued under those regulations.

“We have a unique opportunity to help regulators and other concerned parties to assess draft regulations using our data and modeling, before operations start and we regret the impact of our activity,” says Carlos Munoz Royo, a PhD student in MIT’s END Lab.

Tracking plumes in the water

In deep-sea mining, a collector vehicle would be deployed from a ship. The collector vehicle then travels 15,000 feet down to the seabed, where it vacuums up the top four inches of the seabed. This process creates a plume known as a collector plume.

“As the collector moves across the seabed floor, it stirs up sediment and creates a sediment cloud, or plume, that’s carried away and distributed by ocean currents,” explains Peacock.

The collector vehicle picks up the nodules, which are pumped through a pipe back to the ship. On the ship, usable nodules are separated from unwanted sediment. That sediment is piped back into the ocean, creating a second plume, known as a discharge plume.

Peacock collaborated with Pierre Lermusiaux, professor of mechanical engineering and of ocean science and engineering, and Glenn Flierl, professor of Earth, atmospheric, and planetary sciences, to create mathematical models that predict how these two plumes travel through the water.

To test these models, Peacock set out to track actual plumes created by mining the floor of the Pacific Ocean. With funding from MIT ESI, he embarked on the first-ever field study of such plumes. He was joined by Alford and Eric Adams, senior research engineer at MIT, as well as other researchers and engineers from MIT, Scripps, and the United States Geological Survey.

With funding from the UC Ship Funds Program, the team conducted experiments in consultation with the ISA during a weeklong expedition in the Pacific Ocean aboard the U.S. Navy R/V Sally Ride in March 2018. The researchers mixed sediment with a tracer dye that they were able to track using sensors on the ship developed by Alford’s Multiscale Ocean Dynamics group. In doing so, they created a map of the plumes’ journeys.

The field experiments demonstrated that the models Peacock and Lermusiaux developed can be used to predict how plumes will travel through the water — and could help give a clearer picture of how surrounding biology might be affected.

Impact on deep-sea organisms

Life on the ocean floor moves at a glacial pace. Sediment accumulates at a rate of 1 millimeter every millennium. With such a slow rate of growth, areas disturbed by deep-sea mining would be unlikely to recover on a reasonable timescale.

“The concern is that if there is a biological community specific to the area, it might be irretrievably impacted by mining,” explains Peacock. 

According to Cindy Van Dover, professor of biological oceanography at Duke University, in addition to organisms that live in or around the nodules, other organisms elsewhere in the water column could be affected as the plumes travel.

“There could be clogging of filter feeding structures of, for example, gelatinous organisms in the water column, and burial of organisms on the sediment,” she explains. “There could also be some metals that get into the water column, so there are concerns about toxicology.”

Peacock’s research on plumes could help biologists like Van Dover assess collateral damage from deep-sea mining operations in surrounding ecosystems.

Drafting regulations for mining the sea

Through connections with MIT’s Policy Lab, the Institute is one of only two research universities with observer status at the ISA.

“The plume research is very important, and MIT is helping with the experimentation and developing plume models, which is vital to inform the current work of the International Seabed Authority and its stakeholder base,” explains Chris Brown, a consultant at the ISA. Brown was one of dozens of experts who convened on MIT’s campus last fall at a workshop discussing the risks of deep-sea mining.

To date, the field research Peacock and Alford conducted is the only ocean dataset on midwater plumes that exists to help guide decision-making. The next step in understanding how plumes move through the water will be to track plumes generated by a prototype collector vehicle. Peacock and his team in the END Lab are preparing to participate in a major field study using a prototype vehicle in 2020.

Thanks to recent funding provided by the 11th Hour Project, Peacock and Lermusiaux hope to develop models that give increasingly accurate predictions about how deep-sea mining plumes will travel through the ocean. They will continue to interact with academic colleagues, international agencies, NGOs, and contractors to develop a clearer picture of deep-sea mining’s environmental impact.

“It’s important to have input from all stakeholders early in the conversation to help make informed decisions, so we can fully understand the environmental impact of mining resources from the ocean and compare it to the environmental impact of mining resources on land,” says Peacock.

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