Slashdot Mirror
Phase Change in Fluids Simulated
brendotroy writes "After decades of work by the physics and computer science communities, scientists at the University of Rochester have finally created a mathematical model that will allow scientists to simulate and understand phase changes. This discovery 'could have an impact on everything from decaffeinating coffee to improving fuel cell efficiency in automobiles of the future.'"
could have an impact on everything from decaffeinating coffee
So it's going to be used for evil!!!!!
General Relativity: Space-time tells matter where to go; Matter tells space-time what shape to be.
Perhaps in the future, a swimming pool will hold 10,000 Litres of data by using phase changing properties to store binary computer data.
Saskboy's blog is good. 9 out of 10 dentists agree.
I doubt I'm the only one who remembers an article about some breakthrough opening the doors to making decaffinated coffee beans. So far, hasn't happened. Between this and today's other scientific breakthrough of bumblebee flight, are we any closer to a safer and smoother cup of decaf coffee?
I see this being apply to video games as the next Lens Flare fad!
Demented But Determined.
Bizarrely enough, it's 34 deg F here right now!
And it's supposed to be 56 degrees tomorrow.
Here in Rochester, we appreciate global warming!
The above gives an introduction to phase change as it is considered in terms of Complexity Theory. Approaching phase change through complexity theory, even for an outsider like myself, gives insight into how far reaching are the results of insight into phase change.
"Academicians are more likely to share each other's toothbrush than each other's nomenclature."
Cohen
phase change is hardly fun. After a recent visit to TacoBell I changed a solid into both a liquid and a gas is less time than it took me to get home in my car, after which both my girlfriend and my dignity evaporated.
MEDIA CONTACT: Jonathan Sherwood (585) 273-4726, jonathan.sherwood@rochester.edu
January 6, 2006
Phase Change in Fluids Finally Simulated After Decades of Effort
Eldred Chimowitz and Yonathan Shapir
Everyone knows what happens to water when it boils--everyone, that is, except computers. Modeling the transformation process of matter moving from one phase to another, such as from liquid to gas, has been all but impossible near the critical point. This is due to the increasingly complex way molecules behave as they approach the change from one phase to another. Researchers at the University of Rochester, however, have now created a mathematical model that will allow scientists to simulate and understand phase changes, which could have an impact on everything from decaffeinating coffee to improving fuel cell efficiency in automobiles of the future. The findings have been published in Physical Review Letters.
"This problem has baffled scientists for decades," says Yonathan Shapir, professor of physics and chemical engineering at the University of Rochester, and co-author of the paper. "This is the first time a computer program could simulate a phase transition because the computers would always bog down at what's known as the 'critical slowdown.' We figured out a way to perform a kind of end-run around that critical point slowdown and the results allow us to calculate certain critical point properties for the first time."
"Critical slowdown" is a phenomenon that happens as matter moves from one phase to another near the critical point. As molecules in a gas, for instance, are cooled, they lose some of their motion, but are still moving around and bumping into each other. As the temperature drops to where the gas will change into a liquid, the molecules' motion becomes correlated, or connected, across larger and larger distances. That correlation is a bit like deciding where to go to dinner--quick and easy with two people, but takes forever for a group of 20 to take action. The broadening correlation dramatically increases the time it takes for the gas to reach an overall equilibrium, and that directly leads to an increase in computing time required, approaching infinity and bogging down as the gas crosses the point of phase change.
To illustrate the effect, imagine a perfectly pure and still lake. If you drop a pebble into this lake, its ripples would spread outward, dissipating until the lake had returned to a calm equilibrium again. But, if you were to take this impossibly perfect lake just barely above the critical point and drop your pebble, the ripples would remain as ripples much longer--likely bouncing off the distant shores. This imaginary lake would take seemingly forever to return to its calm equilibrium again.
The research team of Shapir, Eldred Chimowitz, professor in the Department of Chemical Engineering, and physics graduate student Subhranil De created a novel approach to tackle the phase-change process. They devised a computational model consisting of two separate reservoirs of fluid at equilibrium and near the critical point threshold. One reservoir was slightly more pressurized than its neighbor. The reservoirs were opened to each other and the pressure difference caused the fluids to mix. The team let the simulation run until the entire system reached thermodynamic equilibrium. By watching the rate that equilibrium returned, the team was able to calculate the behavior at the critical point. Their simulation findings match predictions and experimental results, including very precise measurements performed in microgravity on the Space Shuttle.
"In principle, it's a difficult calculation," says Chimowitz. "Fluid systems require a different class of models than the common lattice models used by researchers who have studied dynamic critical behavior. These different classes give rise to different dynamic critical exponents and we found them, for the first time, in real fluid systems."
The best known examples of phase changes are perhaps water to ice and
7h3$3 4r3n'7 7h3 Ðr01Ð$ ¥0 4r3 £00|{1n9 f0r. M0v3 4£0n9. --OB1
Can it show why lakes don't freeze from the bottom up as water approaches 0 Celsius?
Freezing water is an example of a first order phase transition, involving a transfer of latent heat across a clearly defined phase boundary. Algorithms have been able to deal with those for some time (or so I assume). The big breakthrough here is that these guys figured out how to model a second order phase transition (i.e phase transitions in a supercritical fluid) without incurring infinite CPU time.
Most people are familiar with first order phase transitions (like melting ice or boiling water) but have never seen a second order phase transition. In general first order phase transitions involve a transfer of latent heat, and are noticeably discontinuous- the two phases are easily distinguishable from each other. Second order phase transitions do not involve a latent heat transfer and there is no abrupt discontinuity during the transition, as they occur above the critical temperature and critical pressure, beyond which the liquid and gas phases are indistinguishable.
The article doesn't explain this at all, but the giveaway here is that the reporter talks about the critical point.
I would like to point out that the article is not about plain phase changes, but rather about phase changes near the critical point , where liquid and gas phases become indistinguishable. Predicting ideal phase change behaviour has been done, but the critical point poses some unique challenges.
Languages aren't inherently fast -- implementations are efficient
Wrong, I highly suggest you take a Phase Transitions and Critical Phenomena class if you want to see the utility of methods such as this. As I noted in another post, though, this isn't the first method to allow computer simulations of points arbitrarily close to criticality, there have been other algorithms (eg Cluster algorithm) to allow this too. But every new algorithm to get past critical slowdown is very useful.
What we've learned in the past several decades in critical phenomena is how parameters change close to the critical point. For example, look up Critical Exponents and Scaling. What is very interesting is that critical exponents are unique to a universality class. So if you are able to take a new system and show that it boils down (no pun intended) to a previously-studied universality class, you can know instantly how various parameters will scale and change as a function of temperature, magnetic field, etc, close to the critical point.
And to give you an example of this, look at Superconductivity. It was originally discovered by Onnes in 1911, but it took 46 years until the BCS Theory was adequately able to explain how Cooper Pairs form and how resistanceless supercurrent can flow quantum-mechanically. Such a theory is referred to as 'microscopic', meaning it deals with the fundamental physics involved, specifically the electron-phonon-electron interaction and how the Fermi sea is unstable to Pair condensation.
However - alot of work was done prior to BCS dealing with 'macroscopic' theory, whereby certain laws were able to be formed (eg London equations for classical electrodynamics of a superconductor), we just didn't understand how or why they were valid.
One such important example is the Ginzburg-Landau theory (Landau won the Nobel Prize decades ago, Ginzburg just got it a couple of years ago), which extends Landau's Theory of 2nd-Order Phase Transitions to use a complex order parameter, which can vary in space. This yields the Ginzburg-Landau equations, which describe VERY WELL the behavior of a superconductor close to the transition point. It was using these equations that Josephson was able to come up with the concept of the Josephson Effect (earning him a Nobel Prize). And Abrikosov was able to come up wit the idea of Type II superconductors and vortices (he also won a Nobel Prize for this work). And after the BCS theory was understood, Gor'kov was able to show that the Ginzburg-Landau equations are a limiting case of the BCS theory close to the critical point.
However, the point of all this is that it was shown, before the microscopic BCS interactions were understood, scientists were able to do ALOT of things with the Ginzburg-Landau equations. What makes these so great is that they are able to approximate quantum mechanics decently, which the London equations were unable to do. And the best part is that scientists today (myself included) still use Ginzburg-Landau equations to model superconductors. It's just that much easier to use these equations for many interactions than the lower-level BCS theory. But amazingly, these equations were known BEFORE the BCS theory!
So back to your comment, such study of critical phenomena teaches us a great deal about systems in criticality, even if the methods involved are decoupled from the microscopic physics. Especially if one can determine the universality class of an unknown system. And for very complicated systems, critical exponents will be difficult to determine analytically and must be solved numerically. Hence the importance of simulations and algorithms such as this.
make world, not war