Researchers at Rensselaer Polytechnic Institute have finally discovered how dolphins can swim at more than 20 miles per hour.
In 1936, British zoologist Sir James Gray discovered that dolphins could swim at more than 20 miles per hour.
However, he also concluded that the muscles of the dolphins simply weren’t strong enough to support those kinds of speeds. The conundrum came to be known as Gray’s Paradox.
For decades, the puzzle prompted much speculation and conjecture in the scientific community. But now, armed with flow measurement technology, researchers at Rensselaer Polytechnic Institute have tackled the problem and conclusively solved Gray’s Paradox.
‘Sir Gray was certainly on to something, and it took nearly 75 years for technology to bring us to the point where we could get at the heart of his paradox,’ said Timothy Wei, professor and acting dean of Rensselaer’s School of Engineering.
‘For the first time, I think we can safely say the puzzle is solved. The short answer is that dolphins are simply much stronger than Gray or many other people ever imagined.’
After studying dolphins, Gray said that they are not capable of producing enough thrust, or power-induced acceleration, to overcome the drag created as the mammal sped forward through the water. This drag should prevent dolphins from attaining significant speed, but simple observation proved otherwise – a paradox.
In the absence of a sound explanation, Gray theorised that dolphin skin must have special drag-reducing properties.
More than 70 years later, Wei has developed a tool that conclusively measures the force a dolphin generates with its tail.
Wei created the water flow diagnostic technology by modifying and combining force-measurement tools developed for aerospace research with a video-based flow-measurement technique known as Digital Particle Image Velocimetry, which can capture up to 1,000 video frames per second.
Wei videotaped two bottlenose dolphins as they swam through a section of water populated with hundreds of thousands of tiny air bubbles. He then used computer software to track the movement of the bubbles. The colour-coded results show the speed and direction of the water flow around and behind the dolphin, which allowed the researchers to calculate precisely how much force the dolphin was producing.
Wei also used the technique to film dolphins as they were doing tail-stands, a trick where the dolphins ‘walk’ on water by holding most of their bodies vertical above the water while supporting themselves with short, powerful thrusts of their tails.
The results show that dolphins produce on average about 200 pounds of force when flapping their tail – about 10-times more force than Gray originally hypothesised.
At peak performance, the dolphins produced between 300 and 400 pounds of force. Human Olympic swimmers, by comparison, peak at about 60 to 70 pounds of force, Wei said. He knows this because he has been working with USA Swimming over the past few years to use these same bubble-tracking DPIV and force-measuring techniques to better understand how elite swimmers interact with the water, and improve lap times.
‘It was a natural extension to go from swimmers to dolphins,’ said Wei, whose research ranges from aeronautical and hydrodynamic flow of vehicles to more biological topics dealing with the flow of cells and fluid in the human body.
The dolphins Wei filmed, Primo and Puka, are retired US Navy dolphins that now live at the Long Marine Laboratory at UC Santa Cruz.
Wei said the research team is likely to continue to investigate the flow dynamics and force generation of other marine animals.
For more information on Wei’s work with Olympic swimmers visit: http://news.rpi.edu/update.do?artcenterkey=2477
A single frame from a research video tracking the flow of water around Primo, a retired US Navy bottlenose dolphin
The same frame, but with visualised information illustrating the water flow. The arrows indicate in which direction the water is moving, and the colours indicate the speed. The red and dark-blue arrows signify the fastest-moving water