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AI approach elevates plasma performance and stability across fusion devices
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Achieving a sustained fusion reaction is a delicate balancing act, requiring a sea of moving parts to come together to maintain a high-performing plasma: one that is dense enough, hot enough, and confined for long enough for fusion to take place.
Now, a team of fusion researchers led by engineers at Princeton and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have successfully deployed machine learning methods to suppress these harmful edge instabilities — without sacrificing plasma performance.
With their approach, which optimizes the system’s suppression response in real-time, the research team demonstrated the highest fusion performance without the presence of edge bursts at two different fusion facilities — each with its own set of operating parameters. The researchers reported their findings on May 11 in Nature Communications, underscoring the vast potential of machine learning and other artificial intelligence systems to quickly quash plasma instabilities.
“Not only did we show our approach was capable of maintaining a high-performing plasma without instabilities, but we also showed that it can work at two different facilities,” said research leader Egemen Kolemen, associate professor of mechanical and aerospace engineering and the Andlinger Center for Energy and the Environment. “We demonstrated that our approach is not just effective — it’s versatile as well.”
The costs of high-confinement
Researchers have long experimented with various ways to operate fusion reactors to achieve the necessary conditions for fusion. Among the most promising approaches involves operating a reactor in high-confinement mode, a regime characterized by the formation of a steep pressure gradient at the plasma’s edge that offers enhanced plasma confinement.
However, the high-confinement mode has historically come hand-in-hand with instabilities at the plasma’s edge, a challenge that has required fusion researchers to find creative workarounds.
One fix involves using the magnetic coils that surround a fusion reactor to apply magnetic fields to the edge of the plasma, breaking up the structures that might otherwise develop into a full-fledged edge instability. Yet this solution is imperfect: while successful at stabilizing the plasma, applying these magnetic perturbations typically leads to lower overall performance.
“We have a way to control these instabilities, but in turn, we’ve had to sacrifice performance, which is one of the main motivations for operating in the high-confinement mode in the first place,” said Kolemen, who is also a staff research physicist at PPPL.
The performance loss is partly due to the difficulty of optimizing the shape and amplitude of the applied magnetic perturbations, which in turn stems from the computational intensity of existing physics-based optimization approaches. These conventional methods involve a set of complex equations and can take tens of seconds to optimize a single point in time — far from ideal when plasma behavior can change in mere milliseconds. Consequently, fusion researchers have had to preset the shape and amplitude of the magnetic perturbations ahead of each fusion run, losing the ability to make real-time adjustments.
“In the past, everything has had to be pre-programmed,” said co-first author SangKyeun Kim, a staff research scientist at PPPL and former postdoctoral researcher in Kolemen’s group. “That limitation has made it difficult to truly optimize the system, because it means that the parameters can’t be changed in real time depending on how the conditions of the plasma unfold.”
Raising performance by lowering computation time
The Princeton-led team’s machine learning approach slashes the computation time from tens of seconds to the millisecond scale, opening the door for real-time optimization. The machine learning model, which is a more efficient surrogate for existing physics-based models, can monitor the plasma’s status from one millisecond to the next and alter the amplitude and shape of the magnetic perturbations as needed. This allows the controller to strike a balance between edge burst suppression and high fusion performance, without sacrificing one for the other.
“With our machine learning surrogate model, we reduced the calculation time of a code that we wanted to use by orders of magnitude,” said co-first author Ricardo Shousha, a postdoctoral researcher at PPPL and former graduate student in Kolemen’s group.
Because their approach is ultimately grounded in physics, the researchers said it would be straightforward to apply to different fusion devices around the world. In their paper, for instance, they demonstrated the success of their approach at both the KSTAR tokamak in South Korea and the DIII-D tokamak in San Diego. At both facilities, which each have a unique set of magnetic coils, the method achieved strong confinement and high fusion performance without harmful plasma edge bursts.
“Some machine learning approaches have been critiqued for being solely data-driven, meaning that they’re only as good as the amount of quality data they’re trained on,” Shousha said. “But since our model is a surrogate of a physics code, and the principles of physics apply equally everywhere, it’s easier to extrapolate our work to other contexts.”
The team is already working to refine their model to be compatible with other fusion devices, including planned future reactors such as ITER, which is currently under construction.
One active area of work in Kolemen’s group involves enhancing their model’s predictive capabilities. For instance, the current model still relies on encountering several edge bursts over the course of the optimization process before working effectively, posing unwanted risks to future reactors. If instead the researchers can improve the model’s ability to recognize the precursors to these harmful instabilities, it could be possible to optimize the system without encountering a single edge burst.
Kolemen said the current work is yet another example of the potential for AI to overcome longstanding bottlenecks in developing fusion power as a clean energy resource. Previously, researchers led by Kolemen successfully deployed a separate AI controller to predict and avoid another type of plasma instability in real time at the DIII-D tokamak.
“For many of the challenges we have faced with fusion, we’ve gotten to the point where we know how to approach a solution but have been limited in our ability to implement those solutions by the computational complexity of our traditional tools,” said Kolemen. “These machine learning approaches have unlocked new ways of approaching these well-known fusion challenges.”
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‘Ice bucket challenge’ reveals that bacteria can anticipate the seasons
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Bacteria use their internal 24-hour clocks to anticipate the arrival of new seasons, according to research carried out with the assistance of an ‘ice bucket challenge.’
The team behind the findings gave populations of blue-green algae (cyanobacteria) different artificial day lengths at a constant warm temperature. Samples on plates received either short days, equinox days (equal light and dark), or long days, for eight days.
After this treatment, the blue-green algae were plunged into ice for two hours and survival rates monitored.
Samples that had been exposed to a succession of short days (eight hours light and 16 hours dark) in preparation for the icy challenge achieved survival rates of 75%, up to three times higher than colonies that had not been primed in this way.
One short day was not enough to increase the bacteria’s resistance to cold. Only after several short days, and optimally six to eight days, did the bacteria’s life chances significantly improve.
In cyanobacteria which had genes that make up their biological clock removed, survival rates were the same regardless of day lengths. This indicates that photoperiodism (the ability to measure the day-night cycle and change one’s physiology in anticipation of the upcoming season) is critical in preparing bacteria for longer-term environmental changes such as a new season or shifts in climate.
“The findings indicate that bacteria in nature use their internal clocks to measure day length and when the number of short days reaches a certain point, as they do in autumn/fall, they ‘switch’ to a different physiology in anticipation of the wintry challenges that lie ahead,” explained first author of the study, Dr Luísa Jabbur, who was a researcher at Vanderbilt University, Tennessee, in the laboratory of Prof. Carl Johnson when this study took place, and is now a BBSRC Discovery Fellow at the John Innes Centre.
The Johnson lab has a long history of studying the circadian clock of cyanobacteria, both from a mechanistic and an ecological perspective.
Previous studies have shown that bacteria have a version of a biological clock, which could allow them to measure differences in day-night length, offering an evolutionary advantage.
This study, which appears in Science, is the first time that anyone has shown that photoperiodism in bacteria has evolved to anticipate seasonal cues.
Based on these findings a whole new horizon of scientific exploration awaits. A key question is: how does an organism with a lifespan of between six and 24 hours evolve a mechanism that enables it not merely to react to, but to anticipate, future conditions?
“It’s like they are signalling to their daughter cells and their granddaughter cells, passing information that the days are getting short, you need to do something,” said Dr Jabbur.
Dr Jabbur and colleagues at the John Innes Centre will, as part of her BBSRC Discovery Fellowship, use cyanobacteria as a fast-reproducing model species to understand how photoperiodic responses might evolve in other species during climate change, with hopeful applications to major crops.
A key part of this work will be to understand more about the molecular memory systems by which information is passed from generation to generation in species. Research will investigate the possibility that an accumulation of compounds during the night on short days acts as a molecular switch that triggers change to a different physiology or phenotype.
For Dr Jabbur the findings amount to an early-career scientific breakthrough in the face of initial scepticism from her scientific mentor and the corresponding author of the paper, Professor Carl Johnson.
“As well as being a fascinating person and an inspiration, Carl sings in the Nashville Symphony Chorus, and he has an operatic laugh! It echoed round the department when I first outlined my idea for the icy challenge, to see if photoperiod was a cue for cyanobacteria in their natural element,” said Dr Jabbur.
“To be fair he told me to go away and try it, and as I went, he showed me a sign on his door with the Frank Westheimer quote: ‘Progress is made by young scientists who carry out experiments that old scientists say would not work.’
“It did work, first time. Then I repeated the experiments. There is something very precious about looking at a set of plates with bacteria on them and realizing that in that moment you know something that nobody else knows.”
Bacteria can anticipate the seasons: Photoperiodism in cyanobacteria appears in Science.
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New filtration material could remove long-lasting chemicals from water
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Water contamination by the chemicals used in today’s technology is a rapidly growing problem globally. A recent studyby the U.S. Centers for Disease Control found that 98 percent of people tested had detectable levels of PFAS, a family of particularly long-lasting compounds, also known as forever chemicals, in their bloodstream.
The findings are described in the journal ACS Nano, in a paper by MIT postdoc Yilin Zhang, professor of civil and environmental engineering Benedetto Marelli, and four others from MIT.
PFAS chemicals are present in a wide range of products, including cosmetics, food packaging, water-resistant clothing, firefighting foams, and antistick coating for cookware. A recent study identified 57,000 sites contaminated by these chemicals in the U.S. alone. The U.S. Environmental Protection Agency has estimated that PFAS remediation will cost $1.5 billion per year, in order to meet new regulations that call for limiting the compound to less than 7 parts per trillion in drinking water.
Contamination by PFAS and similar compounds “is actually a very big deal, and current solutions may only partially resolve this problem very efficiently or economically,” Zhang says. “That’s why we came up with this protein and cellulose-based, fully natural solution,” he says.
“We came to the project by chance,” Marelli notes. The initial technology that made the filtration material possible was developed by his group for a completely unrelated purpose — as a way to make a labelling system to counter the spread of counterfeit seeds, which are often of inferior quality. His team devised a way of processing silk proteins into uniform nanoscale crystals, or “nanofibrils,” through an environmentally benign, water-based drop-casting method at room temperature.
Zhang suggested that their new nanofibrillar material might be effective at filtering contaminants, but initial attempts with the silk nanofibrils alone didn’t work. The team decided to try adding another material: cellulose, which is abundantly available and can be obtained from agricultural wood pulp waste. The researchers used a self-assembly method in which the silk fibroin protein is suspended in water and then templated into nanofibrils by inserting “seeds” of cellulose nanocrystals. This causes the previously disordered silk molecules to line up together along the seeds, forming the basis of a hybrid material with distinct new properties.
By integrating cellulose into the silk-based fibrils that could be formed into a thin membrane, and then tuning the electrical charge of the cellulose, the researchers produced a material that was highly effective at removing contaminants in lab tests.
The electrical charge of the cellulose, they found, also gave it strong antimicrobial properties. This is a significant advantage, since one of the primary causes of failure in filtration membranes is fouling by bacteria and fungi. The antimicrobial properties of this material should greatly reduce that fouling issue, the researchers say.
“These materials can really compete with the current standard materials in water filtration when it comes to extracting metal ions and these emerging contaminants, and they can also outperform some of them currently,” Marelli says. In lab tests, the materials were able to extract orders of magnitude more of the contaminants from water than the currently used standard materials, activated carbon or granular activated carbon.
While the new work serves as a proof of principle, Marelli says, the team plans to continue working on improving the material, especially in terms of durability and availability of source materials. While the silk proteins used can be available as a byproduct of the silk textile industry, if this material were to be scaled up to address the global needs for water filtration, the supply might be insufficient. Also, alternative protein materials may turn out to perform the same function at lower cost.
Initially, the material would likely be used as a point-of-use filter, something that could be attached to a kitchen faucet, Zhang says. Eventually, it could be scaled up to provide filtration for municipal water supplies, but only after testing demonstrates that this would not pose any risk of introducing any contamination into the water supply. But one big advantage of the material, he says, is that both the silk and the cellulose constituents are considered food-grade substances, so any contamination is unlikely.
“Most of the normal materials available today are focusing on one class of contaminants or solving single problems,” Zhang says. “I think we are among the first to address all of these simultaneously.”
The research team included MIT postdocs Hui Sun and Meng Li, graduate student Maxwell Kalinowski, and recent graduate Yunteng Cao PhD ’22, now a postdoc at Yale. The work was supported by the Office of Naval Research, the National Science Foundation, and the Singapore-MIT Alliance for Research and Technology.
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‘Some pterosaurs would flap, others would soar’ — new study further confirms the flight capability of these giants of the skies
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Some species of pterosaurs flew by flapping their wings while others soared like vultures, demonstrates a new study published in the peer-reviewed Journal of Vertebrate Paleontology.
However, “remarkable” and “rare” three-dimensional fossils of two different large-bodied azhdarchoid pterosaur species — including one new-to-science — have enabled scientists to hypothesize that not only could the largest pterosaurs take to the air, but their flight styles could differ too.
The new findings are led by experts from the University of Michigan, in the US, the Natural Resources Authority and Yarmouk University, in Jordan, and the Saudi Geological Survey, in Saudi Arabia.
Their paper details how these fossils — which date back to the latest Cretaceous period (approximately 72 to 66 million years ago) — were remarkably three-dimensionally preserved within the two different sites that preserve a nearshore environment on the margin of Afro-Arabia, an ancient landmass that included both Africa and the Arabian Peninsula. The research team used high-resolution computed tomography (CT) scans to then analyze the internal structure of the wing bones.
“The dig team was extremely surprised to find three-dimensionally preserved pterosaur bones, this is a very rare occurrence,” explains lead author Dr Kierstin Rosenbach, from the Department of Earth and Environmental Sciences of the University of Michigan.
“Since pterosaur bones are hollow, they are very fragile and are more likely to be found flattened like a pancake, if they are preserved at all.
“With 3D preservation being so rare, we do not have a lot of information about what pterosaur bones look like on the inside, so I wanted to CT scan them.
“It was entirely possible that nothing was preserved inside, or that CT scanners were not sensitive enough to differentiate fossil bone tissue from the surrounding matrix.”
Luckily, though, what the team uncovered was “remarkable,” via “exciting internal structures not only preserved, but visible in the CT scanner.”
CT scans reveal one soars; one flaps!
Newly collected specimens of the already-known giant pterosaur, Arambourgiania philadelphiae, confirm its 10-meter wingspan and provide the first details of its bone structure. CT images revealed that the interior of its humerus, which is hollow, contains a series of ridges that spiral up and down the bone.
This resembles structures in the interior of wing bones of vultures. The spiral ridges are hypothesized to resist the torsional loadings associated with soaring (sustained powered flight that requires launch and maintenance flapping).
The other specimen analyzed was the new-to-science Inabtanin alarabia, which had a five-meter wingspan. The team named it after the place where it was excavated — near a large grape-colored hill, called Tal Inab. The generic name combines the Arabic words “inab,” for grape, and “tanin” for dragon. ‘Alarabia’ refers to the Arabian Peninsula.
Inabtanin is one of the most complete pterosaurs ever recovered from Afro-Arabia, and the CT scans revealed the structure of its flight bones was completely different from that of Arambourgiania.
The interior of the flight bones were crisscrossed by arrangement with struts that match those found in the wing bones of modern flapping birds.
This indicates it was adapted to resist bending loads associated with flapping flight, and so it is likely that Inabtanin flew this way — although this does not preclude occasional use of other flight styles too.
“The struts found in Inabtanin were cool to see, though not unusual,” says Dr Rosenbach.
“The ridges in Arambourgiania were completely unexpected, we weren’t sure what we were seeing at first!
“Being able to see the full 3D model of Arambourgiania’s humerus lined with helical ridges was just so exciting.”
What explains this difference?
The discovery of diverse flight styles in differently-sized pterosaurs is “exciting,” the experts state, because it opens a window into how these animals lived. It also poses interesting questions, like to what extent flight style is correlated with body size and which flight style is more common among pterosaurs.
“There is such limited information on the internal bone structure of pterosaurs across time, it is difficult to say with certainty which flight style came first,” Dr Rosenbach adds.
“If we look to other flying vertebrate groups, birds and bats, we can see that flapping is by far the most common flight behavior.
“Even birds that soar or glide require some flapping to get in the air and maintain flight.
“This leads me to believe that flapping flight is the default condition, and that the behavior of soaring would perhaps evolve later if it were advantageous for the pterosaur population in a specific environment; in this case the open ocean.”
Co-author Professor Jeff Wilson Mantilla, Curator at Michigan’s Museum of Paleontology, and Dr Iyad Zalmout, from the Saudi Geological Survey, found these specimens in 2007 at sites in the north and south of Jordan.
Professor Jeff Wilson Mantilla says the “variations likely reflect responses to mechanical forces applied on the pterosaurs’ wings during flight.”
Enabling further study of vertebrate flight
Concluding, Dr Rosenbach states: “Pterosaurs were the earliest and largest vertebrates to evolve powered flight, but they are the only major volant group that has gone extinct.
“Attempts to-date to understand their flight mechanics have relied on aerodynamic principles and analogy with extant birds and bats.
“This study provides a framework for further investigation of the correlation between internal bone structure and flight capacity and behavior, and will hopefully lead to broader sampling of flight bone structure in pterosaur specimens.”
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