# Fuck Yeah Fluid Dynamics

Celebrating the physics of all that flows. Ask a question, submit a post idea or send an email. You can also follow FYFD on Twitter and Google+. FYFD is written by Nicole Sharp, PhD.

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Aerial fireworks are essentially semi-controlled exploding rockets. Here Discovery Channel shares high-speed video of fireworks taking off. The turbulent billowing exhaust on the ground is reminiscent of other rocket launches. The tube-launched firework clip is a great example of an underexpanded nozzle. The pressure of the gases in the tube is higher than the ambient air, so when the gases escape, the exhaust fans out to equalize the pressure. And, finally, the explosion that propels the colorful chemicals outward forms jets that can affect the final form of the display. To my American readers: Happy 4th of July! And be safe! (VIdeo credit: Discovery Slow-Down)

Reader 3d-time asks:

Hi, there is a guy, at my college, who is doing a master’s degree thesis in turbulence. He says he uses fractals and computational methods. Can you explain how fractals can be used in fluid dynamics?

That’s a good question! Fractals are a relatively recent mathematical development, and they have several features that make them an attractive tool, especially in the field of turbulence. Firstly, fractals, especially the Mandelbrot set shown above, demonstrate that great complexity can be generated out of simple rules or equations. Secondly, fractals have a feature known as self-similarity, meaning that they appear essentially the same regardless of scale. If you zoom in on the Mandelbrot set, you keep finding copy after copy of the same pattern. Nature, of course, doesn’t have this perfect infinite self-similarity; at some point things break down into atoms if you keep zooming in. But it is possible to have self-similarity across a large range of scales. This is where turbulence comes in. Take a look at the turbulent plume of the volcanic eruption in the photo above. Physically, it contains scales ranging from hundreds of meters to millimeters, and these scales are connected to one another by their motion and the energy being passed from one scale to another. There have been theories suggested to describe the relationship between these scales, but no one has yet found a theory truly capable of explaining turbulence as we observe it. Both the self-similarity and the complex nature of fractals suggest they could be useful tools in finally unraveling turbulence. In fact, Mandelbrot himself wrote several papers connecting the two concepts. Perhaps your friend will help find the next hints!  (Image credit: U.S. Geological Survey, Wikimedia)

Observations show Jupiter’s iconic Great Red Spot is shrinking, most recently at a rate of more than 900 km a year. As it gets smaller, the storm is also changing shape and becoming more circular. Scientists don’t yet have an explanation for the shrinkage or its recent acceleration, but this is unsurprising given the rich complexity of the storm. For example, the source of the Red Spot’s longevity—it may be more than 300 years old—is still an open topic of research. Some of the most recent observations show smaller eddies feeding into the storm; the current hypothesis is that these eddies may be increasing the Red Spot’s dissipation and accelerating its breakup. (Photo credit: NASA/ESA; h/t to io9)

Flow patterns can change dramatically as fluid speed and Reynolds number increase. These visualizations show flow moving from left to right around a circular plunger. The lower Reynolds number flow is on the left, with a large, well-formed, singular vortex spinning off the plunger’s shoulder. The image on the right is from a higher Reynolds number and higher freestream speed. Now the instantaneous flow field is more complicated, with a string of small vortices extending from the plunger and a larger and messier area of recirculation behind the plunger. In general, increasing the Reynolds number of a flow makes it more turbulent, generating a larger range of length scales in the flow and increasing its complexity. (Image credit: S. O’Halloran)

Sneezing and coughing are major contributors to the spread of many pathogens. Both are multiphase flows, consisting of both liquid droplets and gaseous vapors that interact. The image on the left shows a sneeze cloud as a turbulent plume. The kink in the cloud shows that plume is buoyant, which helps it remain aloft. The right image shows trajectories for some of the larger droplets ejected in a sneeze. Like the sneeze cloud, these droplets persist for significant distances. The buoyancy of the cloud also helps keep aloft some of the smaller pathogen-bearing droplets. Researchers are building models for these multiphase flows and their interactions to better predict and counter the spread of such airborne pathogens. For similar examples of fluid dynamics in public health, see what coughing looks like, how hospital toilets may spread pathogens, and how adjusting viscoelastic properties may counter these effects. (Image credit: L. Bourouiba et al.)

Every year Chicago dyes its river green in honor of St. Patrick’s Day. This timelapse video shows this year’s dyeing, including several passes from a boat distributing the green dye. The color is remarkably slow to diffuse. The boat’s passage does little to affect the motion of the dye already in the river. This is because the boat mainly disturbs the surface and most of the color comes from dye spread throughout the water. It’s like if you tried to stir milk into your coffee just by tapping the surface with your spoon. Instead, the slower, large-scale turbulent motion of the river distributes the dye. For more St. Patrick’s Day physics, be sure to check out Guinness physics and why tapping a beer makes it foam. (Video credit: P. Tsai; submitted by Bobby E.)

Though they may appear random at first glance, turbulent flows do possess structure. The video above shows a numerical simulation of a mixing layer, a flow in which two adjacent regions of fluid move with different velocities. The upper third of the frame shows a top view, and the bottom frame shows a side view, in which the upper fluid layer moves faster than the lower one. The difference in velocities creates shear which quickly drives the mixing layer into turbulence. But watch the chaos carefully, and your eye will pick out vortices rolling clockwise in the largest scales of the mixing layer. These features are known as coherent structures, and they are key to current efforts to understand and model turbulent flows. (Video credit: A. McMullan)

When fast-moving liquids encounter regions of slow-moving liquids, they decelerate rapidly, trading their kinetic energy for potential energy and creating a hydraulic jump. Flow in the video above is from left to right. The depth difference between the incoming and outgoing water can be directly related to the velocity of the incoming fluid. Hydraulic jumps in rivers and spillways are often extremely turbulent, like the one in this video, but laminar examples exist as well. In fact, with the right height and flow rate, you can create stable hydraulic jumps right in your kitchen sink. The hydraulic jumps formed from a falling jet are typically circular, but with the right conditions, all sorts of wild shapes can be observed. (Video credit: H. Chanson)

It’s a big fluids round-up today, so let’s get right to it.

(Photo credit: Think Elephants International/R. Shoer)

When supersonic flow is achieved through a wind tunnel or rocket nozzle, the flow is said to have “started”. For this to happen, a shock wave must pass through, leaving supersonic flow in its wake. The series of images above show a shock wave passing through an ideal rocket nozzle contour. Flow is from the top to bottom. As the shock wave passes through the nozzle expansion, its interaction with the walls causes flow separation at the wall. This flow separation artificially narrows the rocket nozzle (see images on right), which hampers the acceleration of the air to its designed Mach number. It also causes turbulence and pressure fluctuations that can impact performance.  (Image credit: B. Olson et al.)