AI for real-time fusion plasma behavior prediction and manipulation

The designs are complicated, well, because in practice it's not as simple as "put these rocks next to each other and they get hot". When you put the rocks next to each other, they not only get hot, but also emit some nasty radiation that has to be shielded. And if the rocks get a liiiitle bit too close together, they might explode, which leads to huge headaches for everyone involved, so you'd better make sure that doesn't happen...

> Neutrons make hardware radioactive

True, but two caveats:

1. Neutron bombardment due to fusion makes hardware radioactive for less than 10 years, which isn't great but does not compare to fission waste;

2. Some fusion processes don't emit neutrons (aneutronic fusion). As I understand it, these processes aren't as efficient, but there is the possibility of a tradeoff between generation of ratioactive waste vs. efficiency.

When I used to work in grid computing almost 20 years ago, we were already running fusion experiments, realtime streaming the data to the grid, which would rapidly analyze it and compute some new parameters for the next run (I think they had a 20 minute downtime). I don't think it was considered machine learning at the time, though (and was certainly not deep learning as we practice it today).

> Nuclear power plants are largely considered as one of the most reliable sources of energy.

Reliable how?

I mean we first have the issue is we've never built one so how we can judge reliability?

I assume the author is alluding to the apparent abundance of fuel for nuclear fusion. This is and isn't true. Obviously hydrogen (particularly protium) is abundant. Deuterium is relatively abundant, even at ~150ppm. Tritium needs to be produced in a nuclear reactor.

Current hydrogen fusion models revolve around dueterium-tritium ("D-T") fusion. This is because you need to neutrons to sustain the reaction but that presents two huge problems:

1. Because everything is at such high temperature, you eject fast neutrons. This is an energy loss for the system and there's not really a lot you can do about it; and

2. Those free fast neutrons destroy your containment vessel and reactor (as do free Helium nuclei aka alpha particles).

And then after you do all that you boil water and turn a turbine just like you do in a coal or natural gas plant.

So "reliable" is an interesting and questionable claim.

There are other variants like so-called aneutronic fusion (eg Helium-3, which is far from abundant) and those aren't really "neutron free". They're really just "fewer neutrons".

So what about containment? Magnetic fields can contain charged particles and you have various designs (eg tokamak, stellarator) and that's what the AI is for here I guess.

But the core problem is to make this work you superheat the plasma so you're dealing with a turbulent fluid. That's inherently problematic. Any imperfection or failure in your containment field is going to be a problem.

Stars deal with this by being large and thus using sheer size (ie neutrons can't go that far without hitting another nucleus) and gravity.

It increasingly seems to me that commercial nuclear fusion power generator is a pipe dream, something we simply want to be true. I'm not convinced it'll ever be commercially viable.

I'd love to be proven wrong and certainly won't stop anyone from trying.

In a way AI is the new blockchain. Go back a few years and you had a gold rush of startups attaching every idea to "blockchain" to build hype. That's what AI is now. I don't think it fundamentally changes any of the inherent problems in nuclear fusion.

Aneutronic fusion is even harder than regular fusion so it's not a realistic solution in any near-term scenario.