Принстоны их сургууль болон АНУ-ын Эрчим хүчний яамны харьяа Плазмын физикийн лабораторийн судлаачид цөмийн нэгдлийн реактор дахь плазмын тогтворгүй байдлыг бодит цаг хугацаанд хянах PACMAN хэмээх шинэ программ хангамжийн бүтцийг хөгжүүлжээ.
Цөмийн нэгдлийн үйл явцын үед плазмын бөөмс Нарны цөмөөс ч илүү халуун болж, хэдхэн миллисекундын дотор тогтворгүйжих эрсдэлтэй байдаг нь хүний хурдаас хэтэрсэн хурдан хариу үйлдэл шаарддаг. PACMAN (Prediction And Control using MAchiNe learning) нь хэд хэдэн машин сургалтын загварыг нэгтгэн, ойролцоогоор 20 миллисекунд тутамд давтагдах хяналтын гогцоогоор дамжуулан плазмын төлөв байдлыг тасралтгүй зохицуулдаг. Энэхүү систем нь температур, нягтрал болон соронзон дохиог бодит цагт хэмжиж, аливаа хэлбэлзлийг илрүүлэн засах үүрэгтэй.
Судлаачид PACMAN-ыг Сан-Диегод байрлах DIII-D үндэсний цөмийн нэгдлийн байгууламжид таван удаагийн туршилтаар амжилттай шалгасан байна. Туршилтын явцад уг систем нь плазмын ирмэг дэх эрчим хүчний гэнэтийн огцом өсөлтийг урьдчилан таамаглаж, “tearing mode” хэмээх тогтворгүй байдлыг үүсэхээс нь өмнө 200 миллисекундийн өмнө илрүүлэн зогсоож чаджээ. Мөн зургаан гиротроныг (плазмыг халаах систем) нэгэн зэрэг удирдан, нарийн төвөгтэй зорилтуудыг биелүүлэхдээ оновчтой шийдлийг гаргасан байна.
Энэхүү шинэ бүтэц нь судалгааны багуудад шинэ машин сургалтын загваруудыг урьдынхаас илүү хурдан хугацаанд нэвтрүүлэх, туршилт хийх боломжийг олгож байна. Гэсэн хэдий ч судлаачид энэхүү систем нь хүний оролцоог бүрэн орлохгүй гэдгийг онцолж байна. Хэдийгээр хиймэл оюун ухаан нь хурдан шуурхай шийдвэр гаргадаг ч реакторын аюулгүй байдлын хязгаарыг мөрдөх болон системийн үндсэн зорилтыг тодорхойлох үүрэг нь эцсийн дүндээ хүний хяналтад үлддэг.
Уг судалгааны үр дүнг Nuclear Fusion сэтгүүлд нийтэлсэн бөгөөд PACMAN-ыг ирээдүйн өөр өөр хэмжээ, бүтэц бүхий цөмийн нэгдлийн төхөөрөмжүүдэд дасан зохицуулах боломжтой гэж эрдэмтэд үзэж байна. Энэхүү модульчлагдсан шийдэл нь хиймэл оюун ухааныг ашиглан плазмыг удирдах үйл явцыг нэг удаагийн туршилтаас илүүтэй, цөмийн нэгдлийн салбарт өргөнөөр ашиглах боломжтой дэд бүтэц болгох зорилготой юм.
Дэлгэрэнгүйг эх сурвалжаас харах
↓Эх сурвалжийг нээх ↓
In some fusion systems, particles hotter than the core of the sun can become unstable within just a few thousandths of a second. That is far too fast for a human operator to respond. Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University have developed a new software framework that uses artificial intelligence (AI) to make those rapid decisions while maintaining strict safety controls and leaving people responsible for setting the system’s objectives.
The framework is called PACMAN (a novel abbreviation for Prediction And Control using MAchiNe learning). Researchers successfully tested it on a real fusion system in five separate experiments. Its design and initial results are described in a new paper published in the journal Nuclear Fusion.
AI Takes on Fusion’s Millisecond Challenge
Fusion has the potential to provide a virtually unlimited supply of electricity. Researchers are exploring several approaches to making fusion practical on Earth, including machines known as tokamaks. These devices rely on powerful magnetic fields to confine a plasma: an electrically charged gas often called the fourth state of matter.
For fusion to continue successfully, the plasma must remain hot, dense, and stable. That requires frequent adjustments to systems such as the tokamak’s heating equipment, magnets and gas injectors. Even relatively small disturbances in the plasma, known as instabilities, can grow within milliseconds and disrupt the fusion reaction.
Predicting plasma behavior is another major challenge. Advanced computer simulations can take days or even months to complete. While those tools are valuable for planning future experiments, they are far too slow to guide an experiment in real time when the entire test may last only a few minutes.
“That’s great for preparing for the next experiment in a year, but for control we need models that make a decision in the moment,” said co-lead author Hiro Farre Kaga, a graduate student in the Princeton Program in Plasma Physics, which is a joint program of Princeton University and PPPL. “Machine learning models can describe the plasma behavior very well, and importantly, they are the only way we have to model the plasma in millisecond times. The speed of these models is what’s key for control.”
Bringing Multiple AI Models Into One Fusion System
Machine learning has already shown considerable potential for controlling fusion plasmas. However, many previous efforts were developed individually, without a common framework that would make it easy for different models to work together. Fusion systems require multiple models because different parts of the machine and plasma must be monitored and controlled at the same time.
PACMAN was designed to provide that shared structure.
“We developed this framework so that models could communicate, outputs from those models could be shared and we could do exciting physics in one integrated system,” said Andy Rothstein, a graduate student at Princeton University’s Department of Mechanical and Aerospace Engineering and co-lead author of the paper.
The system combines several machine learning models in a repeating control loop that operates much faster than a person could.
“A really focused human operator can respond on the order of seconds,” Rothstein said. “The whole PACMAN framework typically runs in about 20 milliseconds, and it’s not running once. It’s running again and again and again. It can see small things happening in the plasma and adjust in a way that a human would never be able to do.”
How PACMAN Controls a Tokamak
PACMAN functions much like an assembly line with four stations. It begins by collecting live measurements from the tokamak, including temperature, density, and magnetic signals. The system then checks those readings for errors and combines them into a single package.
AI models then select the measurements they need and use them to estimate what the plasma is currently doing or what it is likely to do next. Controllers take those predictions and determine what actions are needed, such as increasing the power of a heating beam.
In the final stage, PACMAN resolves any conflicting instructions from the controllers, applies strict hardware safety limits, and sends the approved commands to the tokamak. Because the models and controllers operate independently, scientists can introduce new components without disrupting the rest of the framework.
AI Tested on a Real Fusion Machine
Researchers demonstrated PACMAN’s flexibility in five experiments using the DOE’s DIII-D National Fusion Facility tokamak in San Diego.
During those tests, PACMAN:
- Allowed an AI model trained through a trial-and-error approach known as reinforcement learning to take complete control of the heating systems.
- Predicted sudden bursts of energy from the plasma’s edge.
- Detected and controlled waves in the plasma driven by fast particles.
- Adjusted the plasma’s density and rotation to targets set by the researchers.
- Predicted an instability called a tearing mode and stopped it before it happened.
The tearing mode experiment showed one of the clearest potential advantages of the system. Conventional controllers cannot identify this instability until it has already begun.
“Then they try to suppress it, and that can come with a lot of performance degradation,” Farre Kaga said. “In one of the experiments we present, a machine learning model predicts the tearing mode about 200 milliseconds in advance, so the plasma can be changed to avoid it in the first place.”
PACMAN was also able to coordinate all six of DIII-D’s gyrotrons (systems that heat the plasma with powerful microwave beams) at the same time. To meet complex targets selected beforehand by researchers, the framework adjusted the gyrotrons’ power while also repositioning their mirrors in real time.
“There was no algorithm to find that optimal solution before,” Farre Kaga said. “When the shot ended and we looked at the data, it was doing exactly what we hoped, simultaneously moving all six in an optimal way to reach the goal.”
Faster Fusion Experiments With Humans Still in Control
Rothstein said one of the most surprising results was how much faster PACMAN made it possible to introduce additional AI models. Developing the framework and installing its first model required months of work.
“Then we went to put in the second model, and it took a couple of days. The testing was easier, and there were far fewer bugs,” he said. “DIII-D is first and foremost a research machine, and sometimes things don’t work out the way you expected. If you can put a model on in a week, you can retrain it and put a new one on the week after. It allows for iteration that wasn’t possible previously.”
The researchers emphasize that the framework is not intended to remove humans from fusion experiments. PACMAN applies hardware safety limits regardless of what an AI model recommends, and physicists examine the results after each experiment so they can refine the controllers before the next test.
“No matter how sophisticated your controllers, in the end it’s a human operator that sets the parameters for that control,” Farre Kaga said.
A Flexible AI Platform for Future Fusion Machines
PACMAN’s modular structure could also make it useful beyond DIII-D. Its developers believe the framework could be adapted for tokamaks with different shapes, sizes and instruments, including fusion machines that have not yet been designed.
“PACMAN uses a flexible setup where building-block AI algorithms can be put together. You can add a new one, swap one out or run several at once without touching the rest of the system,” said Egemen Kolemen, associate professor of mechanical and aerospace engineering at Princeton University, jointly appointed with the Andlinger Center for Energy and the Environment and PPPL. “That modularity is what turns AI plasma control from a series of one-off demonstrations into infrastructure the whole fusion community can build on.”
Other authors on the paper include Ricardo Shousha, Keith Erickson and SangKyeun Kim from PPPL, Jalal-ud-din Butt, Peter Steiner and Azarakhsh Jalalvand from Princeton University, and Takuma Wakatsuki from Japan’s National Institutes for Quantum Science and Technology.
The research was supported by the DOE Office of Science using the DIII-D National Fusion Facility under awards DE-FC02-04ER54698, DE-SC0015480 and DE-AC02-09CH11466, and by the National Science Foundation Graduate Research Fellowship under grant DGE-2039656.
