Category Archives: Learning & Teaching

Coverts inspire adaptive wing design

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Earlier this summer, when we were walking the South West Coastal Path [see ‘The Salt Path‘ on August 14th, 2019], we frequently saw kestrels hovering above the path ahead of us.  It is an enthralling sight watching them use the air currents around the cliffs to soar, hang and dive for prey.  Their mastery of the air looks effortless.  What you cannot see from the ground is the complex motion of their wing feathers changing the shape and texture of their wing to optimise lift and drag.  The base of their flight feathers are covered by small flexible feathers called ‘coverts’ or ‘tectrix’, which in flight reduce drag by providing a smooth surface for airflow.  However, at low speed, such as when hovering or landing, the coverts lift up and the change the shape and texture of the wing to prevent aerodynamic stalling.  In other words, the coverts help the airflow to follow the contour of the wing, or to remain attached to the wing, and thus to generate lift.  Aircraft use wing flaps on their trailing edges to achieve the same effect, i.e. to generate sufficient lift at slow speeds, but birds use a more elegant and lighter solution: coverts.  Coverts are deployed passively to mitigate stalls in lower speed flight, as in the picture.  When I was in the US last month [see ‘When upgrading is downgrading‘ on August 21st, 2019], one of the research reports was by Professor Aimy Wissa of the Department of Mechanical Science & Engineering at the University of Illinois Urbana-Champaign, who is working on ‘Spatially distributed passively deployable structures for stall mitigation‘ in her Bio-inspired Adaptive Morphology laboratory.  She is exploring how flaps could be placed over the surface of aircraft wings to deploy in a similar way to a bird’s covert feathers and provide enhanced lift at low speeds.  This would be useful for drones and other unmanned air vehicles (UAVs) that need to manoeuvre in confined spaces, for instance in cityscapes.

I must admit that I had occasionally noticed the waves of fluttering small feathers across the back of a bird’s wing but, until I listened to Aimy’s presentation, I had not realised their purpose; perhaps that lack of insight is why I specialised in structural mechanics rather than fluid mechanics with the result that I was worrying about the fatigue life of the wing flaps during her talk.

 

The picture is from a video available at Kestrel Hovering and Hunting in Cornwall by Paul Dinning.

 

Pareto principle in train travel

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The moral of this story is don’t travel with me.  Last week, I wrote about my train being delayed by someone pulling the emergency handle before we got to the end of the platform in Liverpool [see ‘Stopped in Lime Street’ on June 26th, 2019].  Four days later, I was once again on a late afternoon train to London waiting for it to leave Lime Street station.  This time we didn’t even get started before the train manager announced that a road vehicle had hit a bridge between Crewe and Liverpool; and, so we were being held in Liverpool for an unknown period of time.  I sent a message to my family telling them about the delay and one, an engineer, replied that I was ‘hitting the low frequency failure modes on the service quality pareto’.  The Pareto principle is also known as the 80/20 principle.  I first encountered it when I was working at the University of Sheffield and the Vice-Chancellor,  Professor Gareth Roberts, used it to describe the distribution of research output in academic departments, i.e., 80% of research was produced by 20% of the professors.  In service maintenance, it is assumed that 80% of service interruptions are caused by 20% of the possible failure modes.  Hence, if you can address the correct 20% of failure modes then you will prevent 80% of the service interruptions, which is an efficient use of your resources.  The remaining, unaddressed failure modes are likely to occur infrequently and, hence, can be described as low frequency modes; including passengers pulling emergency handles or people driving vehicles into bridges.

How do you drive into a bridge and block the main railway lines between London and the north-west of England?  Perhaps the driver was using their smart phone which was not smart enough to warn them of the impending collision with the bridge.  So, there’s a new product for someone to develop: a smartphone app that connects to dashboard camera in your vehicle and warns you of impending collisions, or better still just drives the vehicle for you.  Yes, I know some vehicles come with all of this installed but not everyone is driving the latest model; so, a retro-fit system should sell well and protect train passengers from unexpected delays caused by road vehicles damaging rail infrastructure.

By the way, the 14:47 to London magically became the 15:47 to London and left on time!

Meta-knowledge: knowledge about knowledge

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As engineers, we like to draw simple diagrams of the systems that we are attempting to analyse because most of us are pictorial problem-solvers and recording the key elements of a problem in a sketch helps us to identify the important issues and select an appropriate solution procedure [see ‘Meta-representational competence’ on May 13th, 2015].  Of course, these simple representations can be misleading if we omit parameters or features that dominate the behaviour of the system; so, there is considerable skill in idealising a system so that the analysis is tractable, i.e. can be solved.  Students find it especially difficult to acquire these skills [see ‘Learning problem-solving skills‘ on October 24th, 2018] and many appear to avoid drawing a meaningful sketch even when examinations marks are allocated to it [see ‘Depressed by exams‘ on January 31st, 2018].  Of course, in thermodynamics it is complicated by the entropy of the system being reduced when we omit parameters in order to idealise the system; because with fewer parameters to describe the system there are fewer microstates in which the system can exist and, hence according to Boltzmann, the entropy will be lower [see ‘Entropy on the brain‘ on November 29th, 2017].  Perhaps this is the inverse of realising that we understand less as we know more.  In other words, as our knowledge grows it reveals to us that there is more to know and understand than we can ever hope to comprehend [see ‘Expanding universe‘ on February 7th, 2018]. Is that the second law of thermodynamics at work again, creating more disorder to counter the small amount of order achieved in your brain?

Image: Sketch made during an example class

Knowledge explosions

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Photo credit: Tom

When the next cohort of undergraduate students were born, Wikipedia had only just been founded [January 2001] and Google had been in existence for just over a decade [since 1998].  In their lifetime, the number of articles on Wikipedia has grown to nearly 6 million in the English language, which is equivalent to 2,500 print volumes of the Encyclopedia Britannica, and counting all language editions there are 48 million articles.  When Leonardo Da Vinci was born in 1452, Johan Gutenberg had just published his first Bible using moveable type.  By the time Leonardo Da Vinci was 20 years old, about 15 million books had been printed which was more than all of the scribes in Europe had produced in the previous 1500 years.  Are these comparable explosions in the availability of knowledge?  The proportion of the global population that is literate has changed dramatically from about 2%, when Leonardo was alive, to over 80% today which probably makes the arrival of the internet, Wikipedia and other online knowledge bases much more significant than the invention of the printing press.

Today what matters is not what you know but what you can do with the knowledge because access to the internet via your smart phone has made memorisation redundant.