University of Texas-led team solves a big problem for fusion energy
19 comments
·May 12, 2025perihelions
RhysU
> We report on a data-driven method for learning a nonperturbative guiding center model from full-orbit particle simulation data.
> Then we describe a data-driven method for learning from a dataset of full-orbit α-particle trajectories. We apply this method to the α-particle dynamics shown in Fig. 1 and find the learned non-perturbative guiding center model significantly outperforms the standard guiding center expansion. Our proposed method for learning applies on a per-magnetic field basis; changing requires re-training.
Is this interpolation at its heart? A variable transformation then a data fit?
Anyone know which functionals of these orbits are important? I don't know the space. I am wondering why the orbits with such nuance should be materially important when accessed via lower-order models.
jmyeet
I remain skeptical that fusion will ever be a commercially viable energy source. I'd love to be wrong.
The engineering challenges are so massive that even if they can be solved, which is far from certain, at what cost? With a dense high-energy plasma, you're dealing with a turbulent fluid where any imperfection in your magnetic confinement will likely dmaage the container.
People get caught up on cheap or free fuel and the fact that stars do this. The fuel cost is irrelevant if the capital cost of a plant is billions and billions of dollars. That has to be amortized over the life of the plant. Producing 1GW of power for $100 billion (made up numbers) is not commercially viable.
And stars solve the confinement problem with gravity and by being really, really large.
Neutron loss remains one of the biggest problems. Not only does this damage the container (ie "neutron embrittlement") but it's a significant energy loss for the system and so-called aneutronic fusion tends to rely on rare fuels like Helium-3.
And all of this to heat water to create steam and turn a turbine.
I see solar as the future. No moving parts. The only form of direct power generation. Cheap and getting cheaper and there are solutions to no power generation at night (eg batteries, long-distance power transmission).
red75prime
> high-energy electrons that can punch a hole in the surrounding walls.
What does it mean? Beta radiation can cause structural damage? Is it really a problem?
regularfry
The electrons are high enough energy that they can damage the wall, yes. But also they're simply a route for energy loss from the plasma that you don't want. E.g. https://www.nature.com/articles/s41598-023-48672-7
jmyeet
Yes. It's a significant problem for two reasons:
1. High energy particles destroy the container. Alpha particles, which are just Helium nuclei, are quite small and can in between metal atoms. Neutrons too. High energy electrons too; and
2. It's an energy loss for the system to lose particles this way.
Magnetic confinement works for alpha and beta particles because they're electrically charged. Neutrons are a far bigger problem, such that you have fun phrases like "neutron embrittlement".
scythe
It is a little jarring to hear "data-driven" and "nonperturbative" in the same sentence. It sounds a little bit like saying you designed a boat with a better lift-to-drag ratio. "Wait, is it a boat or a plane?". So, I opened the paper fully expecting to not understand anything, and I was pleasantly surprised.
> First we deduce formally-exact non-perturbative guiding center equations of motion assuming a hidden symmetry with associated conserved quantity J. We refer to J as the non-perturbative adiabatic invariant.
Simply: this is not just some kind of unsupervised ML black-box magic. There is a formal mathematical solution to something, but it has a certain gap, namely precisely what quantity is conserved and how to calculate it.
> Then we describe a data-driven method for learning J from a dataset of full-orbit α-particle trajectories. [...] Our proposed method for learning J applies on a per-magnetic field basis; changing B requires re-training. This makes it well-suited to stellarator design assessment tasks, such as α-loss fraction uncertainty quantification.
With the formal simplification of the dynamics in hand, the researchers believe that a trained model can then give a useful approximation of the invariant, which allows the formal model, with its unknown parameters now filled in, to be used to model the dynamics.
In a crude way, I think I have a napkin-level sketch of what they're doing here. Suppose we are modeling a projectile, and we know nothing of kinematics. They have determined that the projectile has a parabolic trajectory (the formal part) and then they are using data analysis to find the g coefficient that represents gravitational acceleration (the data-driven part). Obviously, you would never need machine learning in such a very simple case as I have described, but I think it approximates the main idea.
ChrisMarshallNY
One of the nice things about LLMs/ML, is that they can pound away at something for a billion cycles, and do exactly the same things that you or I would do.
for _ in 0...1000000000000 { do_something_complicated() }
xyst
Is there a collective repository on breakthroughs in energy generation by fusion? Sure, this team solves one "big" problem. But hints there are a plethora of other problems (or technology limitations) in this field.
DennisP
Part of the excitement these days is that the general march of technology has removed a lot of those technology limitations, due to advances in superconductors, lasers, supercomputers, fast high-power electronics, etc. (Superconductors and computers would be the ones relevant to stellarators, of course.)
lupusreal
Even with all of these advancements I don't see how you get around fusion reactors still being more complicated and expensive to build as fission reactors, and just as radioactive due to the huge amounts of neutron radiation the "easiest" kinds of fusion produce.
gnfargbl
The difference is that waste from neutron activation is "just" an engineering problem which might have an engineering solution (we hope).
Waste in the form of long-lived nuclear fission products is fundamentally an unsolvable issue. The only option is to confine it for geological timescales.
Both options are really much better, in my opinion, than pumping more carbon dioxide into our biosphere.
roflmaostc
And fusion reactors cannot end up like a Chernobyl disaster. That's a huge safety plus and one of the major concerns many countries are phasing out fission reactors.
tiahura
How is that different than the excitement 30 years ago?
tiahura
Is this a variation of the Fleischmann-Pons method?
gnfargbl
No, this has absolutely nothing to do with so-called "cold" fusion. Cold fusion was a hypothetical type of room-temperature nuclear fusion. It was reported in 1989 but not successfully replicated. It can't possibly work because of the Coulomb repulsion between nuclei is far too strong for them to come into contact at our everyday energy levels.
This work is related to actual genuine nuclear fusion, the kind that occurs at energy scales sufficient to overcome that Coulomb barrier. At those energy scales it becomes very hard to manage the plasma in which fusion occurs. This is a claimed advance in plasma management.
pfdietz
Ordinary fusion doesn't overcome the Coulomb barrier either. In a purely classical sense, fusion wouldn't happen, since the thermal energies are well below the height of the Coulomb barrier.
What happens is that thermal energies get high enough that the nuclei get close enough to have a significant rate of tunneling through the barrier. It's a quantum mechanical effect.
There is a nonzero rate of tunneling through the barrier even at room temperature -- just extremely low, far lower than putative cold fusion claims.
chiffre01
TLDR for the paper and article:
The paper introduces a new, data-driven method for simulating particle motion in fusion devices that is much more accurate than traditional models, especially for fast particles, and could significantly improve fusion reactor design.
https://arxiv.org/abs/2410.02175v2