Google is helping the Tri-alpha energy nuclear fusion project with improved algorithms, sensor data analysis and simulations. Google developed a new computer algorithm that significantly speeds up progress. The team achieved a 50% reduction in energy losses from the system and a resulting increase in total plasma energy, which must reach a critical threshold for fusion to occur.
Tri-alpha runs a real plasma “shot” on the C-2U machine every 8 minutes. Each shot consists of creating two spinning blobs of plasma in the vacuum sealed innards of C-2U, smashing them together at over 600,000 miles per hour, creating a bigger, hotter, spinning football of plasma. Then they blast it continuously with particle beams (actually neutral hydrogen atoms) to keep it spinning. They hang on to the spinning football with magnetic fields for as long as 10 milliseconds. They’re trying to experimentally verify that these advanced beam driven field-reversed plasma configurations behave as expected by theory. If they do, this scheme could lead to net-energy-out fusion.
There are a lot of sensor outputs to look at, to try to figure out how the plasma was behaving. Before you know it, the power supplies are charged again, and they’re ready for another go.
The most impactful benefit of the Optometrist Algorithm was the discovery of the unexpected regime of sustained net plasma heating. It is remarkable that this achievement was realized despite an a priori lack of knowledge of the causality or physics of the regime.
The Optometrist Algorithm is a solution to the common problem of understanding and optimizing complex systems. Stochastic exploration combined with human-guided interpretation of results is a valuable tool that may solve a variety of difficult problems across modern science. We used the Optometrist Algorithm to advance understanding and performance of plasma fusion.
What was that about optimization? What are you optimizing?
That’s the thing, it’s not completely obvious what good plasma performance is. Of course, Tri Alpha has some of the world’s best plasma physicists, but even they disagree on what “good” is. We can boil down the machine controls to “only” 30 parameters or so, but when you have to wait 8 minutes per experiment, it’s a pretty hard problem even with a concrete objective. Also, it’s not entirely known, day-to-day, what the reliable operating envelope of the machine is. And it keeps changing since the quality of the vacuum keeps changing and electrodes wear out and…
So we boil the problem down to “let’s find plasma behaviors that an expert human plasma physicist thinks are interesting, and let’s not break the machine when we’re doing it.” We developed the Optometrist Algorithm, which is sort of a Markov Chain Monte Carlo (MCMC) where the likelihood function being explored is in the plasma physicist’s mind rather than being explicitly written down. Just like getting an eyeglass prescription, the algorithm presents the expert human with machine settings and the associated outcomes. They can just use their judgement on what is interesting, and what is unhealthy for the machine. These could be “That initial collision looked really strong!” or “The edge biasing is actually working well now!” or “Wow, that was awesome, but the electrode current was way too high, let’s not do that again!” The key improvement we provided was a technique to search the high-dimensional space of machine parameters efficiently.
Oh, I like MCMC, it’s like the best thing ever!
I knew you’d like that bit. Using this technique, we actually found something really interesting. As we describe in our paper, we found a regime where the neutral particle beams dumping energy into the plasma were able to completely balance the cooling losses, and the total energy in the plasma actually went up after formation. It was only for about 2 milliseconds, but still, it was a first! Since rising energy due to neutral beam heating was not necessarily expected for C-2U, it would have been difficult to plug into an objective function. We really needed a human expert to notice. This was a classic case of humans and computers doing a better job together than either could have separately. You know how it is — when you think you have an optimization problem, and you optimize the objective, you usually just look at the result and say, “No no no, that’s not what I meant,” and you add some other term and repeat until you get sick of it?
That hasn’t happened to me. This week. Yet.
Yeah, so we just cut out that iteration and let the expert human use their judgment. This learning from human preferences is becoming a thing. Google and Tri Alpha made a pretty good team for it, for a really important problem. So what now? So actually, Tri Alpha learned everything they could have from C-2U and then dismantled it. They built a new machine called Norman (after their late co-founder Norman Rostoker) in the same warehouse. It’s much more powerful both in plasma acceleration and in neutral particle beams. It also has a more sophisticated system to confine the plasma in the central region. The pressure vessel, accelerators, and banks of capacitors and power supplies cover the building’s concrete floor.
Many fields of basic and applied science require efficiently exploring complex systems with high dimensionality. An example of such a challenge is optimising the performance of plasma fusion experiments. The highly-nonlinear and temporally-varying interaction between the plasma, its environment and external controls presents a considerable complexity in these experiments. A further difficulty arises from the fact that there is no single objective metric that fully captures both plasma quality and equipment constraints. To efficiently optimise the system, we develop the Optometrist Algorithm, a stochastic perturbation method combined with human choice. Analogous to getting an eyeglass prescription, the Optometrist Algorithm confronts a human operator with two alternative experimental settings and associated outcomes. A human operator then chooses which experiment produces subjectively better results. This innovative technique led to the discovery of an unexpected record confinement regime with positive net heating power in a field-reversed configuration plasma, characterised by a over 50% reduction in the energy loss rate and concomitant increase in ion temperature and total plasma energy.