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It's a theoretical breakthrough that explains an unexpectedly bad behavior of current designs that was not understood so far: a discrepancy between theory and e
by ogrisel 11y ago
It's a theoretical breakthrough that explains an unexpectedly bad behavior of current designs that was not understood so far: a discrepancy between theory and experimental observations.
Now that the model is fine enough to explain the turbulences observed in the experiments one might hope that this will help suggest design changes to better deal with the turbulences. However the MIT article does not suggest any practical design modification yet.
- semi-extrinsic 11y agoIt is a hard thing to write the code for and then run a simulation of coupled plasma turbulence taking 15 million CPU-hours to explain a phenomenon. But it is another thing entirely to take such a huge simulation and try to use many of them to do tokamak design optimization.
- marktangotango 11y agoIndeed, I found this quote from the original article relevant: >> Now, researchers at General Atomics are taking these new results and using them to develop a simplified, streamlined simulation that could be run on an ordinary laptop computer
- ogrisel 11y agoMaybe this could be done with machine learning to learn an approximate function to predict the outcome of the paired simulation from the outcome of the isolated simulations.
- S_A_P 11y agoI would be curious to know the process for even modeling this sort of thing. How do they know they got it right? How accurately does this model existing experiments? Im also wondering if they model to a result vs modeling to what should actually be happening. Would anyone learned on this subject please chime in? I've always wondered how modeling these types of complex systems works.
- tfgg 11y agoI think the answer is "with great difficulty". When you're developing something like this, you might have an issue with: - Experiment - Model - Approximation to the model - Numerics - Code (- Compiler & hardware) So, your answer might not match experiment. Maybe the experiment is wrong. Maybe it's an expected effect due to an approximation you used. Maybe your integration grid isn't sufficiently converged. You just have to systematically eliminate these, often by comparing to more expensive but verified simulations, or analytic solutions on simplified models.
- evanb 11y agoI don't do plasma physics but other sorts of HPC numerical physics. The answer is a myriad of ways: Check small numerical cases on an already-validated code. Check problems for which you have an analytic solution (in this case, maybe many laminar flow scenarios). Check problems where the 'weird part' (ie the turbulence in this case) is limited to one small part of the simulation. Compare only moderately complicated simulations to experiments that are already entirely understood.
- semi-extrinsic 11y agoIn addition to the points mentioned by others, for PDE based codes like this we have the very powerful "method of manufactured solutions". http://projecte-hermes.upc.edu/Optatives/B%20-%20Analysis%20of%20thermal%20and%20fluid%20dynamic%20issues%20in%20industrial%20and%20aeronautical%20systems%20and%20equipment/MMS.pdf http://projecte-hermes.upc.edu/Optatives/B%20-%20Analysis%20...
- lutusp 11y ago> I would be curious to know the process for even modeling this sort of thing. How do they know they got it right? It helps that the model creates the same outcomes as the actual observations (and the old model didn't). This isn't an ironclad guarantee that the model reflects reality, but it suggests that we're moving toward a more accurate reflection of nature. > I've always wondered how modeling these types of complex systems works. To explain I will use a simpler example -- weather forecasting. In weather forecasting, we need to model the atmosphere. It's not possible to model the entire atmosphere directly, instead we break the atmosphere into a bunch of cubes, each with an initial temperature and humidity and a few other things. Then we process the cubes in a supercomputer consisting of many processors running in parallel -- one processor per cube. The actual computation consists of seeing what effect a cube's neighbor's pressures, temperatures, etc. have on the tested cube, for a brief interval of time. Then we repeat the process for another slice of time. Ad infinitum. This is obviously a numerical process, with no overarching equation that explains it all, but with some basic first principles at work (a theme in modern physics). Such a process becomes more accurate if we can create more, smaller cubes, and for that we need more and faster processors. So most of modern supercomputer design involves thinking of ways to acquire more and faster parallel processors. The kind of model that led to the discussed result follows the same basic pattern. The irony is that the most often cited reason for having supercomoputers -- to be able to say whether it will rain 72 hours from now -- is also the least likely to succeed, because of the role played by quantum mechanics (i.e. by chance processes) in earth's atmosphere.
- cordite 11y agoLooked up how much that is as if a single CPU. 1711 years. 625000 days. This is beyond any HPC that I have ever witnessed before. Maybe CERN does more, but I don't quite know the details of it.
- drjesusphd 11y agoThat's why Fortran is still relevant.
- brgewut 11y agoSo the current designs don't work? Sounds more like a step back not a "breakthrough"
- Bjartr 11y agoDiscovering your believed correct model is wrong is a scientific breakthrough, as is discovering how to fix it.
- brgewut 11y agoOk, I thought "breaktrough" brings you closer to something. Eg. saying that "switch to Heliocentric worldview was a major breakthrough for Geocentric theory" sounds really weird to me.
- nevir 11y agoBetter understanding does bring you closer to making something tangible happen. The heliocentric theory was a breakthrough for a variety of technologies. We certainly would have found going into space much more difficult if we still followed the geocentric theory.
- Scarblac 11y agoThe step is that they figured out _why_ reality wasn't as good as the model. Another way to put it is that they improved the model. That _does_ bring us closer to improving actual reactors.
- daveguy 11y agoThe breakthrough would be more like saying, "the switch to heliocentric worldview was a major breakthrough for solar system modeling." 1) The reactors have been failing to generate sustained plasma due to specific failures. 2) The simulation models of the reactors did not correctly predict those failures. 3) The new model correctly predicts those failures. 4) Now you can iterate designs using the model with the expectation that these particular failures can be identified before building a full reactor. 5) Breakthrough.