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AI for real-time fusion plasma behavior prediction and manipulation

control.princeton.edu

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Re: AI for real-time fusion plasma behavior prediction and manipulation

#91

Post by lupusreal »

avery17 wrote: post #47
lupusreal wrote: post #36

Earlier quoted context omitted.

The only hard part of dealing with nuclear waste is the social aspect. If not for that, you can simply and safely dump it into the ocean. Water is excellent shielding and the amount of uranium/etc already dissolved in sea water is absurd. Put it in a stainless steel vessel first if you want most of it to decay before coming into contact with the water, but that's not even necessary.

I really hope you aren't serious. Safe dry-cask storage on site is already a fantastic solution.

I'm completely serious. It was done extensively during the 20th century and never became an environmental issue. Nuclear waste is a social problem, not a technical problem.

Re: AI for real-time fusion plasma behavior prediction and manipulation

#92

Post by cosmic_quanta »

adrian_b wrote: post #83
cosmic_quanta wrote: post #33

Earlier quoted context omitted.

> 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. effic…

> Neutron bombardment due to fusion makes hardware radioactive for less than 10 years Very false. The current design target for fusion reactors is that the materials taken out of the reactor should become "low-level radioactive waste" after being stored for one hundred years . It is acknowledged however that it is likely that a small fraction of the materials will not satisfy the criteria for "low-level radioactive w…

Ah well I'm sorry, I must misremember

Re: AI for real-time fusion plasma behavior prediction and manipulation

#93

Post by aftbit »

perihelions wrote: post #32
aftbit wrote: post #9

>Nuclear power plants are largely considered as one of the most reliable sources of energy. Inside the plants, reactors use fission to heat water into steam, which is then used to spin turbines and produce carbon-free electricity. However, nuclear fission produces nuclear waste, which requires great amounts of regulation for safe storage and disposal. This is an odd angle to highlight. The risk of long-lived nuclear…

It is not true that coal is more radioactive than spent nuclear fuel. It's very much the opposite: SNF is 10^11 times more radioactive than coal per kilogram, or 10^6 times more radioactive per energy unit. Per the EPA, US coal has, at the high end, 10^3 Becquerel/kg of natural radioactivity [0]. Spent nuclear fuel has 3 million Curies/tonne (33 MWd/kg burnup fuel, at the age of 1 year) [1], which is equal to 10^14 B…

Sure, the spent fuel is considerably more radioactive per kilogram, but how many kilograms of coal does a typical plant burn in a decade, versus how many kilograms of nuclear fuel are spent?

Re: AI for real-time fusion plasma behavior prediction and manipulation

#94

Post by cyberax »

spenczar5 wrote: post #15
HPsquared wrote: post #7

Earlier quoted context omitted.

Neural networks have been used in industrial process control for many years. This is just another industrial control problem, perhaps a difficult one.

That’s really interesting! Do you have other interesting specific examples? I would have guessed that most industrial control problems were simpler sets of differential equations that could be directly estimated.

Furnace control was one of the first practical applications of multi-layer neural networks: https://www.researchgate.net/publication/282185187_Artificia...

Re: AI for real-time fusion plasma behavior prediction and manipulation

#95

Post by ben_w »

spenczar5 wrote: post #15
HPsquared wrote: post #7

Earlier quoted context omitted.

Neural networks have been used in industrial process control for many years. This is just another industrial control problem, perhaps a difficult one.

That’s really interesting! Do you have other interesting specific examples? I would have guessed that most industrial control problems were simpler sets of differential equations that could be directly estimated.

Back in ~2004, when I was looking for a job for the industrial placement year of my degree, one of the options was a nut packing factory using computer vision systems. I was intrigued, but went for the job processing satellite images instead.

Even well before that, ML is very closely related to statistics, so early practical applications would have been as simple as gathering data points on widget production and doing the kinds of analyses that are now backed into free spreadsheet software.

Re: AI for real-time fusion plasma behavior prediction and manipulation

#96

Post by perihelions »

adrian_b wrote: post #65
jylam wrote: post #62

Earlier quoted context omitted.

I'm not a specialist but here is what I think I know (I'm talking with the point of view of a Frenchman, who consumes most of his electricity from (fission) nuclear power plants): 1/ Uranium is not a renewable (quite the opposite), needs to be mined and treated (which is expensive and very polluting), and not present at the required concentrations in most of the world (this creates geopolitical issues). 2/ Fission nu…

Deuterium is also not renewable, even if it is more abundant than uranium. The H1-B11 reaction would be a much better energy source than anything else, but for now nobody knows any method to do it. There is no chance to do it by heating, but only by accelerating ions, and it is not known how a high enough reaction rate could be obtained.

Then wind power is not renewable either! The saturation wind power potential of this planet (250 terawatts?), integrated from now until this planet ceases to exist, is a finite number—and it is actually a smaller number than this planet's deuterium resource.

Re: AI for real-time fusion plasma behavior prediction and manipulation

#97

Post by ben_w »

atomic128 wrote: post #12

Neutrons make hardware radioactive. Many on Hacker News fantasize about fusion (not fission) reactors. These fusion reactors will be an intense source of fast neutrons. All the hardware in a fusion reactor will become radioactive. Not to mention the gamma rays. If you have to deal with radioactive materials, why not just use fission? After 70 years of working with fission reactors, we know how to build and operate th…

> Hacker News is hostile to fission and defeatist (unable to contemplate innovation in fission technology) but this attitude will gradually change.

Lots of us like fission and think the fears are overestimated.

Nevertheless, the observation is that new developments in fission tend to result in the cost increasing, not decreasing.

And I say that as someone with a similar mindset regarding fusion, though for different reasons: you can pick aneutronic fusion reactions… but look at what weapons can proliferate with transmutation from the neutrons you can also choose, and ask which governments will turn them down.

Re: AI for real-time fusion plasma behavior prediction and manipulation

#98

Post by ben_w »

adrian_b wrote: post #65
jylam wrote: post #62

Earlier quoted context omitted.

I'm not a specialist but here is what I think I know (I'm talking with the point of view of a Frenchman, who consumes most of his electricity from (fission) nuclear power plants): 1/ Uranium is not a renewable (quite the opposite), needs to be mined and treated (which is expensive and very polluting), and not present at the required concentrations in most of the world (this creates geopolitical issues). 2/ Fission nu…

Deuterium is also not renewable, even if it is more abundant than uranium. The H1-B11 reaction would be a much better energy source than anything else, but for now nobody knows any method to do it. There is no chance to do it by heating, but only by accelerating ions, and it is not known how a high enough reaction rate could be obtained.

> Deuterium is also not renewable, even if it is more abundant than uranium

Technology correct, in that after around a hundred trillion years even the red dwarf stars will have stopped burning hydrogen.

But last I checked as yet there is no known way to harness the only (and even then merely suspected) infinitely renewable energy source: the expansion of the universe.

Re: AI for real-time fusion plasma behavior prediction and manipulation

#99

Post by perihelions »

aftbit wrote: post #93
perihelions wrote: post #32

Earlier quoted context omitted.

It is not true that coal is more radioactive than spent nuclear fuel. It's very much the opposite: SNF is 10^11 times more radioactive than coal per kilogram, or 10^6 times more radioactive per energy unit. Per the EPA, US coal has, at the high end, 10^3 Becquerel/kg of natural radioactivity [0]. Spent nuclear fuel has 3 million Curies/tonne (33 MWd/kg burnup fuel, at the age of 1 year) [1], which is equal to 10^14 B…

Sure, the spent fuel is considerably more radioactive per kilogram, but how many kilograms of coal does a typical plant burn in a decade, versus how many kilograms of nuclear fuel are spent?

I answered that exact question! :)

Re: AI for real-time fusion plasma behavior prediction and manipulation

#100

Post by varjag »

ben_w wrote: post #95
spenczar5 wrote: post #15

Earlier quoted context omitted.

That’s really interesting! Do you have other interesting specific examples? I would have guessed that most industrial control problems were simpler sets of differential equations that could be directly estimated.

Back in ~2004, when I was looking for a job for the industrial placement year of my degree, one of the options was a nut packing factory using computer vision systems. I was intrigued, but went for the job processing satellite images instead. Even well before that, ML is very closely related to statistics, so early practical applications would have been as simple as gathering data points on widget production and doin…

From what I remember reading at the time, most of industrial vision applications 20 years ago had very little to do with neural networks. Or even with ML in general, relying on bespoke feature detectors.
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