When it comes to building and improving tech – be it for explosive detection or space exploration – scientists need look no further than the animal kingdom for solutions inspired by evolution itself.
1. Fido's nose the bomb at scenting explosives
There are lots of different ways to detect explosives – from x-rays to honey bees.
While sniffer dogs are also commonly used in the field, problems can arise when these trusty canines get tired or bored. Researchers have looked closely at a 3D printed pooch nose for clues on how to build and improve current man-made explosive detection methods.
They saw that sniffing served to increase the aerodynamic reach of the nose. This is in comparison to most vapor detectors, which just continually draw in air.
When a dog’s nose expires, a jet of turbulent air leaves each nostril in a downward and outward direction. This jet draws air from ahead of the nose and pulls it towards the nostrils (‘fluid entrainment’)– ready for the next inhale.
The scientists modified a commercially available trace vapour detector with 3D printed doggy-inspired sniffing ‘nostrils’ – and saw a 16-fold improvement in the detection of vapours.
Researchers have looked to one of nature’s best chemical detectors – the dog – to help make today’s chemical detection devices better at sniffing out explosives and other contraband materials.
2. A whale of a tail – boosting wing efficiency
While humpback whales are famous for their size (weighing in at an average of 25-30 tons, or about 15 average sized cars), it’s actually the mechanisms behind their manoeuvrability that makes them pretty unique in the animal kingdom.
Tubercles (or bumps) on the leading edge of the humpbacks’ fins reduce flow over the fin and decrease its drag in the water.
Scientists were surprised when they saw this same effect when they designed wing structures (such as those used in windmills, fans and aircraft) with built-in tubercles. Previous theories had stated that the leading edge of these structures should be as straight and smooth as possible to be most efficient.
Biology Professor Richard E. Fish (not a pun), who initiated these studies, went on to found WhalePower, which now owns a number of patents on this not-so-fishy technology.
3. Marsupial inspired robust robots for rough terrain
Although it might sound fun, designing a jumping robot can be fairly tricky. How does the robot land safely? How does it move without wasting too much energy?
Scientists are particularly interested in answering these questions because a well-built jumping robot would be ideal for exploring rough terrain (on this planet and others). Bumpy surfaces can be problematic for wheel, track or leg-based robots.
Kangaroos are some of the world’s best long jumpers – the red kangaroo can cover 8 metres in a single hop, and reach speeds of up to 60km/h.
Multiple teams of mechanical engineers have mimicked the kangaroo’s anatomy in building a variety of roo-inspired robots, all with balancing ‘tails’, bilateral symmetry and elastic spring legs - which ‘charge’ upon landing.
4. Cracking complex optimisation problems with clues from krill
It’s not just hardware which stands to benefit from the lessons of evolution.
In an era where ‘big data’ is influencing just about every field, from marketing to genetic research, ‘optimisation problems’, which have been traditionally solved by computers, are getting harder and harder to crack.
A basic example of an optimisation problem, which you might remember from school, runs along the lines of a gardener wanting to partition a rectangle shaped zone with the largest possible area, but only having a limited length of fencing.
Problems such as these aren’t only faced by high school students, but also swarming or herding animals, like krill and ants. They too experience constraints (available energy, proximity of predators) whilst also having to achieve goals (eat, mate).
By examining the collective behaviour of these animals, who have evolved to solve these ongoing optimisation problems of their own, mathematicians and computer scientists are developing algorithms upon which problem solving software can be based.