How AI helps to finally let the fusion reactor become a reality
In Marvel’s comic universe following the end of World War II Howard Stark tries to tap into the energy of the mystical “Tesseract” and develops the arc reactor — a technology he believes to hold the key to unlimited, sustainable energy and would make nuclear energy look like an AAA battery. However, the perfect reactor cannot be built without a certain theoretical element and he lacks the technology to synthesize it.
In the film “Iron Man”, his son Tony Stark builds a miniature version of the Arc Reactor when held hostage in an Afghan cave to power an electromagnet, which keeps deadly shrapnel from piercing his heart. Even this small reactor has a remarkable output of 3 GJ/s — as much as three times the average energy produced by a nuclear power plant. As the reactor’s waste products threaten to poison him, Tony searches for new elements for the reaction. With the help of his father’s blueprints and the artificial intelligence JARVIS, he succeeds in building the perfect arc reactor and thus initiates the energy revolution [1].
What at first sounds like a far-fetched sci-fi story has its origin in real technical developments — the fusion reactor. Even at the beginning of the atomic age, scientists toyed with the idea of generating electricity through controlled nuclear fusion, but to this day there is still no fusion reactor that generates more energy than it consumes. Now, with the help of supercomputers and artificial intelligence (AI), this goal seems to be within reach.
Todays nuclear power plants work with nuclear fission — basically an uncontrolled chain reaction that splits heavier elements, like uranium, into smaller ones. The nuclear reaction taking place in fusion reactors works the other way round and is an every-day encounter for us as it forms the basis of how the sun and all the shining stars radiate energy:
Two hydrogen isotopes — deuterium and tritium — fuse together under high pressure and enormous temperatures to form a helium nucleus by releasing a neutron. Since the masses of the original products are greater than those of the reaction products, a so-called mass defect is created. Based on Einstein’s famous formula e=mc2 this mass defect is responsible for the subsequent release of energy [2]. A single gram of the fusion fuel theoretically provides as much energy as eleven tons of coal, is almost inexhaustibly available on earth, and produces only short-lived, weakly radiating atomic waste [3] — the (almost) perfect energy source.
So where do things get complicated? In short: In a fusion reactor plasma must be heated to over one hundred million degrees Celsius. And this is where the problems begin, since the particles’ collisions with the reactor walls cool them down immediately and the reaction comes to a quick end. To prevent this the plasma is enclosed by strong magnetic fields, an extremely complex task.
Modern fusion reactors have a large number of control parameters that depend to a great extent on the current state of the reactor, e.g. the changing state of the reactor walls. Thus, to generate optimal plasma requires the optimization of hundreds of non-linear and strongly interrelated parameters, which makes manual optimization of individual parameters impossible. So how can suitable parameter configurations be found and the plasma be maintained searing hot for as long as possible in order to create a stable source of energy?
To answer this question, the American fusion start-up TAE Technologies has developed the “Optometrist” [4] algorithm in collaboration with tech-giant Google.
Similar to the way an optician provides a patient with two different lenses and asks which one gives him better vision, “Optometrist” shows a human expert two plasma configurations and their experimental results. The expert must then decide which of the two configurations has produced the better result.
