Tag: chaos theory

  • The Real Butterfly Effect

    The Real Butterfly Effect

    The butterfly effect — that the flapping of a butterfly’s wings in Brazil can cause a tornado in Texas — expresses the sensitivity of a chaotic system to initial conditions. In essence, because we can’t possibly track every butterfly in Brazil, we’ll never perfectly predict tornadoes in Texas, even if the equations behind our weather forecast are deterministic.

    But this interpretation doesn’t fully capture the subtleties of the situation. With fluid dynamics, the small scales of a flow — like the turbulence in an individual cloud — are linked to the largest scales in the flow — for example, a hurricane. For short times, we’re actually quite good at predicting those large scales; our weather forecasts can distinguish sunny days and cloudy ones a week out. But at smaller scales, the forecast errors pile up quickly. No one can forecast that an individual cloud will form over your house three days from now. And because the small scales are linked to the larger scales, the uncertainties from the small scale cascade upward, limiting how far into the future we can reliably predict the weather.

    And, unfortunately, drilling down to capture smaller and smaller scales in our models can’t fix the problem, unless our initial uncertainties are identically zero. To get around this problem, weather forecasters instead use ensemble forecasting, where they run many simulations of the weather with slightly different initial conditions. Those differences in initial conditions let the forecasters play with those initial uncertainties — how accurate is the temperature reading from that station? How reliable is the instrument reporting that humidity? How old is the satellite data coming in? Once all the forecasts are run, they can see how many predicted sunny days versus rainy ones, which ones resulted in severe weather, and so on. Often the probabilities we see in our weather app — like 30% chance of rain — depend on factors including how many of the forecasts resulted in rain.

    Unfortunately, this butterfly effect permanently limits just how far into the future we can predict weather — at least until we fully understand the nature of the Navier-Stokes equations. For much more on this interesting aspect of chaos, check out this Physics Today article. (Image credit: NASA; see also T. Palmer at Physics Today)

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    On the Butterfly Effect

    Fluid dynamics is a veritable playground of chaotic systems, but that doesn’t always translate to easy explanations, as Henry Reich points out in this Minute Physics video. The common metaphor for chaos is the Butterfly Effect, an idea that a butterfly flapping its wings causes a typhoon on the other side of the world. I agree with Henry that this is a poor example of chaos, for many of the same reasons he lays out. In reality, we call a system chaotic when its outcome is so sensitive to the initial conditions that the result becomes effectively unpredictable. And there are some very simple systems that are chaotic, like a double pendulum or a three-body problem. The weather is, honestly, too complicated of a system for the metaphor to make sense, but fluid dynamics does have other, simpler examples, like mixing in porous media, bouncing droplets, or, my personal favorite, the fluid dynamical sewing machine. (Video credit: Minute Physics)

  • Resonating on a Bounce

    Resonating on a Bounce

    When we think of resonance, we often think of it in simple terms: hit the one right note, and the wine glass will shatter. But resonance isn’t always about a one-to-one ratio between a driving frequency and the resonating system. Especially in fluid dynamics, we often see responses that occur at other, related frequencies.

    One of the simplest places to see this is with a droplet bouncing on a bath of fluid. Above you see a liquid metal droplet bouncing on a bath of the same metal. At low amplitude, the pool surface moves at the driving frequency and a droplet bounces simply upon that surface, with one bounce per oscillation. Increase the amplitude, though, and the droplet’s bounce changes. It bounces twice – one large bounce and one small bounce – in the time it takes for the pool surface to go through one cycle. This is called period doubling because the bouncing occurs at twice the driving frequency.

    Turn the amplitude up further, and the system undergoes another change. Faraday waves form on the surface. They resonate at half the driving frequency, and a droplet’s bouncing will sync up with the waves. That means the droplet returns to a one-to-one bounce with the waves, but the waves themselves are no longer reacting at the driving frequency. It’s this kind of complexity that makes fluid systems fertile grounds for studying paths toward chaos. (Image and research credit: X. Zhao et al.)

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    Printing in Glass

    A group at MIT have created a new 3D printer that builds with molten glass. This allows them to manufacture items that would difficult, if not impossible, to create with traditional glassblowing or other modern techniques. One of the coolest aspects of this technique is that it can use viscous fluid instabilities like the fluid dynamical sewing machine to create different effects with the glass. You can see this around 1:56 in the video. Varying the height of the head and the speed at which it moves will cause the molten glass to fall and form into different but consistent coiling patterns. All in all, it’s a very cool application for using some nonlinear dynamics! (Video credit: MIT; via James H. and Gizmodo)

  • The Fluid Dynamical Sewing Machine

    The Fluid Dynamical Sewing Machine

    Anyone who has poured a viscous fluid like honey or syrup will have noticed its tendency to coil like rope. A similar effect is observed when a viscous fluid stream falls onto a moving belt. The photos above show some of the patterns seen in these “fluid-mechanical sewing machines” depending on the height of the thread and the speed of the moving belt. Notice how some of the patterns are doubles of another (i.e. two coils per side instead of one). This period doubling behavior is often seen in systems on their way to chaos.  (Photo credits: S. Chiu-Webster and J. Lister)

  • Chaos in Suspension

    Chaos in Suspension

    In science, the term chaotic is used to describe a system whose behavior is highly sensitive to initial conditions. This means that the end state can vary widely based on small changes at the start–also commonly known as the butterfly effect. Many fluid dynamical systems are chaotic, especially turbulent ones. Above are a series of photos showing the suspension of particles in a horizontally rotating cylinder. In parts A-D, the speed of rotation of the cylinder is increased, resulting in dispersion of the particles. As rotation rate is increased further, interesting concentration patterns form. #