Category Archives: Engineering

Engineering is all about ingenuity

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Painting from Okemos High School Art Collection at MSU

Painting from Okemos High School Art Collection at MSU

Who was the first engineer?  It’s a tricky question to answer.  Some sources cite Ailnolth, who lived in the second half of the twelfth century and worked on the Tower of London, as one of the first to be called an ‘ingeniator’.  The word comes from the Latin and the Roman writer, Vitruvius, describes master builders as being ingenious or possessing ‘ingenium’.  Leonardo da Vinci (1452 – 1519) was perhaps the first person to be appointed as an engineer.  The Duke of Milan appointed him ‘Ingenarius Ducalis’ or Master of Ingenious Devices.

So it would appear that an engineer is ‘a skilful contriver or originator of something’,  which is the third definition in the on-line Oxford Dictionary after ‘a person who designs, builds, or maintains engines, machines or structures’ and ‘a person who controls an engine especially on an aircraft or ship’.  This type of engine, which uses heat to do work, is a relatively recent invention probably by Thomas Savery and Thomas Newcomen in the early eighteenth century.  Engineers have been contriving, designing and inventing ‘works of public utility’ [quote from my older hard copy Oxford English Dictionary] for many centuries before the heat engine hijacked the terminology.

Why does this matter?  Well, many people have a misconception that engineering is all about engines, the heat kind; and yes, some of us do design, build and maintain engines but very many more engineers contrive, design and invent works of public utility – in the broadest sense of the words, i.e. just about everything ‘invented’ in the world. In other words, engineering is using human ingenuity to produce something useful; preferably something that improves the quality of life – oh, but now we are moving into ethics and I will leave that for another day!

Sources:

Blockley D, Engineering: A Very Short Introduction, Oxford: Oxford University Press, 2012.

Auyang SY, Engineering – an endless frontier, Cambridge MA: Harvard University Press, 2004.

Little W, Fowler HW & Coulson J, The Shorter Oxford English Dictionary, C.T. Onions (editor), London: Guild Publishing, 1983.

 

Electron uncertainty

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daisyMost of us are uncomfortable with uncertainty.  Michael Faraday’s ability to ‘accept the given – certainties and uncertainties’ [see my post entitled ‘Steadiness and placidity’ on July 18th, 2016] was exceptional and perhaps is one reason he was able to make such outstanding contributions to science and engineering.  It has been said that his ‘Expts. on the production of Electricity from Magnetism, etc. etc.’ [Note 148 from Faraday’s notebooks] on August 29th 1831  began the age of electricity.  Electricity is associated with the flow of electric charge, which is often equated with the flow of electrons and electrons are subatomic particles with a negative elementary charge and a mass that is approximately 1/1836 atomic mass units.  A moving electron, and it is difficult to find a stationary one, has wave-particle duality – that is, it simultaneously has the characteristics of a particle and a wave.  So, there is uncertainty about the nature of an electron and most of us find this concept difficult to handle.

An electron is both matter and energy.  It is a particle in its materialisation as matter but a wave in its incarnation as energy.  However, this is probably too much of a reductionist description of a systemic phenomenon.  Nevertheless let’s stay with it for a moment, because it might help elucidate why the method of measurement employed in experiments with electrons influences whether our measurements reflect the behaviour of a particle or a wave.  Perhaps when we design our experiments from an energy perspective then electrons oblige by behaving as waves of energy and when we design from a matter perspective then electrons materialise as particles.

All of this leads to a pair of questions about what is matter and what is energy?  But, these are enormous questions, and even the Nobel Laureate Richard Feynman said ‘in physics today, we have no knowledge of what energy is’, so I’m going to leave them unanswered.  I’ve probably already riled enough physicists with my simplistic discussion.

Note: an atomic mass unit is also known as a Dalton and is equivalent to 1.66×10-27kg

Source:

Hamilton, J., A life of discovery: Michael Faraday, giant of the scientific revolution. New York: Random House, 2002.

Pielou EC, The Energy of Nature [the epilogue], Chicago: The University of Chicago Press, 2001.

Popping balloons

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Balloons ready for popping

Balloons ripe for popping!

Each year in my thermodynamics class I have some fun popping balloons and talking about irreversibilities that occur in order to satisfy the second law of thermodynamics.  The popping balloon represents the unconstrained expansion of a gas and is one form of irreversibility.  Other irreversibilities, including friction and heat transfer, are discussed in the video clip on Entropy in our MOOC on Energy: Thermodynamics in Everyday Life which will rerun from October 3rd, 2016.

Last week I was in Florida at the Annual Conference of the Society for Experimental Mechanics (SEM) and Clive Siviour, in his JSA Young Investigator Lecture, used balloon popping to illustrate something completely different.  He was talking about the way high-speed photography allows us to see events that are invisible to the naked eye.  This is similar to the way a microscope reveals the form and structure of objects that are also invisible to the naked eye.  In other words, a high-speed camera allows us to observe events in the temporal domain and a microscope enables us to observe structure in the spatial domain.  Of course you can combine the two technologies together to observe the very small moving very fast, for instance blood flow in capillaries.

Clive’s lecture was on ‘Techniques for High Rate Properties of Polymers’ and of course balloons are polymers and experience high rates of deformation when popped.  He went on to talk about measuring properties of polymers and their application in objects as diverse as cycle helmets and mobile phones.

Credibility is in the eye of the beholder

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Picture1Last month I described how computational models were used as more than fables in many areas of applied science, including engineering and precision medicine [‘Models as fables’ on March 16th, 2016].  When people need to make decisions with socioeconomic and, or personal costs, based on the predictions from these models, then the models need to be credible.  Credibility is like beauty, it is in the eye of the beholder.   It is a challenging problem to convince decision-makers, who are often not expert in the technology or modelling techniques, that the predictions are reliable and accurate.  After all, a model that is reliable and accurate but in which decision-makers have no confidence is almost useless.  In my research we are interested in the credibility of computational mechanics models that are used to optimise the design of load-bearing structures, whether it is the frame of a building, the wing of an aircraft or a hip prosthesis.  We have techniques that allow us to characterise maps of strain using feature vectors [see my post entitled ‘Recognising strain‘ on October 28th, 2015] and then to compare the ‘distances’ between the vectors representing the predictions and measurements.  If the predicted map of strain  is an perfect representation of the map measured in a physical prototype, then this ‘distance’ will be zero.  Of course, this never happens because there is noise in the measured data and our models are never perfect because they contain simplifying assumptions that make the modelling viable.  The difficult question is how much difference is acceptable between the predictions and measurements .  The public expect certainty with respect to the performance of an engineering structure whereas engineers know that there is always some uncertainty – we can reduce it but that costs money.  Money for more sophisticated models, for more computational resources to execute the models, and for more and better quality measurements.