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Modeling Supernovae With a Supercomputer
A team of scientists at the University of Chicago will be using 22 million processor-hours to simulate the physics of exploding stars. The team will make use of the Blue Gene/P supercomputer at Argonne National Laboratory to analyze four different scenarios for type Ia supernovae. Included in the link is a video simulation of a thermonuclear flame busting its way out of a white dwarf. The processing time was made possible by the Department of Energy's INCITE program.
"Burning in a white dwarf can occur as a deflagration or as a detonation. 'Imagine a pool of gasoline and throw a match on it. That kind of burning across the pool of gasoline is a deflagration,' Jordan said. 'A detonation is simply if you were to light a stick of dynamite and allow it to explode.' In the Flash Center scenario, deflagration starts off-center of the star's core. The burning creates a hot bubble of less dense ash that pops out the side due to buoyancy, like a piece of Styrofoam submerged in water."
blimey, i was expecting them to use prayer, because that has been proven to work unlike these so called 'computers', which according to Ben Stein the nazi's used
I'm a rabbit startled by the headlights of life
Probably someone who actually knows what simulation means.
Simulation is something which simulates a system or environment in order to predict actual behavior.
To speculate on the other hand is to make an inference based on inconclusive evidence; to surmise or conjecture.
So, he was indeed insightful when he stated that the lack of understanding would render the results speculative at best.
(all definitions courtesy of wikitionary)
If sharing a song makes you a pirate, what do I have to share to be a ninja?
You guys are missing the point of the modeling. You don't always model something to make predictions of its actual behavior. In this case modeling serves as an excellent way to test our models against empirical data collected from observations of supernovae. So, we do our best to construct a model, then compare this model to the real system in order to expose holes in our understanding of the phenomenon. This is good science.
SRSLY.
we understand little about it and the math formula used will be a half guess. supercomputer or not, results will be speculative at best.
I don't think you understand how experiments work... If the results of the computations are something other than what is observed in nature, then the methods and/or equations are proven wrong. That is most certainly a NON-speculative result.
Just because the model shows a burst of star stuff blowing out this way or that way in some particular configuration doesn't mean that scientists will leap up from their chairs and say "Stars do this, and we've proven it."
You can never know if your models are correct. All you can do is continually test them and try to prove them wrong. Maxwell's equations have not been proven to be correct -- they've just never been shown to be wrong. This simulation is just a step on the path of evidence.
There are several steps in constructing useful models and the last, and most controversial is that of "model validation".
In building a computer model/simulation, you generally follow these steps:
1) problem formulation - what do you want to figure out, gather data, get the "reference behavior pattern"
2) formulate a mental model of the system - what are the entities involved and how are they related
3) build and debug your model
4) verification - this is where you ensure the model behaves as expected against specific sets of inputs - as you change inputs, does it do what you expect (I turn up the volume knob - and the sound gets louder)
5) validation - this is where you compare the results of the model with the reference data from the real world. If it doesn't match, you then have to back up and figure out what's wrong... was the implementation of the model incorrect? were your initial hypotheses incorrect? And if it does match, have you gathered enough real world data to know your model is functioning well? How confident are you of this model's ability to model the system you're interested in.
So suppose you've built a model that you can validate against gathered data, you still have to demonstrate that your model is valuable to the scientific community.
You're probably going out on a limb to make strong assertions when a model demonstrates/predicts behavior that has not been observed. However, it can serve a great role in helping determine what other things to look for, what conditions may exist, or help see relationships that you may not have seen before.
The controversy is over how much you can use a model to help prove a hypothesis.
I had looked at ther work month ago when researching on writing my own N-body code. So, basically this is an implementation of Fast multipole Method (used in N-body computaion). Tradationaly (or rather in it's naive form) N-body codes are of the order N^2. Fast multipole algorithm (and Barnes-Hut and multitudes of their derivatives) does this at NLogN or better. You can have various kinds physical phenomenon occuring between two bodies/particles/points (graviational, electromagnetic etc) and this problem solves the physics for millions (or billions of such particles making up a supernovae) The entire 3d space is broken down into a oct-tree. You can traverse down to a group of particles (or one particle in Barnes-Hut), and traverse up calculating the force. The basic idea is to make a group of particles a large distance act like a single particle when calculating it's potential on another particle. Mind you the particle here is really a loose definition. It's really the most granular subdivion of space you could afford to calculate. Hence the need for bigger computers for better accuracy. The parallelism is MPI based. It's simpler to handle parallelism for nbody stuff compared to eulerian grid type problems.