Mechanical Engineering virtual taster session | Thurs 11 Nov 4:00pm - 5:00pm


Mechanical Engineering taster session


Sensing the world with sound




When: Thursday 11 November 2021, 4:00pm - 5:00pm

Age group: IB1, IB2, Year 12, Year 13, A Level

Places are limited, book your place to avoid disappointment.


In the pitch black, a bat speeds through the air and swoops around a tree to eat a tasty insect snack. Bats avoid trees and find snacks just by using sound. Scientists have taken this further and are using sound to detect flaws in the ancient UK sewage system! We are now designing a swarm of small robots that use sound to detect cracks and blocks in sewers. In this talk, you will learn a physicist's approach to using sound, like a bat, to find things in the dark. Will you be able to recognise the sound of a blocked sewer?


Maintaining the UK sewer system is a huge issue. Every time we suspect there is a major leak we need to dig up large parts of our roads, causing traffic jams, air pollution from dust, and the whole operation is very costly. To make matters worse, much of our sewers are ancient, falling apart, and we have no idea what pipes are where. Not to mention that it is difficult to convince people to go down there to inspect the sewers. How do we now maintain this ancient labyrinth? A team, led by the University of Sheffield, have the answer: we will develop a swarm of small robots to swim through our sewers and detect cracks and blocks.

After presenting the application, I will pose the question: how do these little robots detect things in the dark? I will then show how, with a physics approach, we can break this problem down into simple manageable smaller problems. These small problems involve simple trigonometry and the solution is illustrated with lots of videos from simulations. From there, we build back up to the complex problem.


The talk will involve lots of videos and have strong visual aids throughout. Videos will include bats flying around, sound sensors in pipes, and computer simulations. I will ask the audience to hear sounds and guess what they heard. Did the sound come from a blocked sewer or not? To finish we end with how machine learning is better at recognising sounds than we are.



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