Getting smarter

A350 XWB passes Maximum Wing Bending test [from:

Garbage in, garbage out (GIGO) is a perennial problem in computational simulations of engineering structures.  If the description of the geometry of the structure, the material behaviour, the loading conditions or the boundary conditions are incorrect (garbage in), then the simulation generates predictions that are wrong (garbage out), or least an unreliable representation of reality.  It is not easy to describe precisely the geometry, material, loading and environment of a complex structure, such as an aircraft or a powerstation; because, the complete description is either unavailable or too complicated.  Hence, modellers make assumptions about the unknown information and, or to simplify the description.  This means the predictions from the simulation have to be tested against reality in order to establish confidence in them – a process known as model validation [see my post entitled ‘Model validation‘ on September 18th, 2012].

It is good practice to design experiments specifically to generate data for model validation but it is expensive, especially when your structure is a huge passenger aircraft.  So naturally, you would like to extract as much information from each experiment as possible and to perform as few experiments as possible, whilst both ensuring predictions are reliable and providing confidence in them.  In other words, you have to be very smart about designing and conducting the experiments as well as performing the validation process.

Together with researchers at Empa in Zurich, the Industrial Systems Institute of the Athena Research Centre in Athens and Dantec Dynamics in Ulm, I am embarking on a new EU Horizon 2020 project to try and make us smarter about experiments and validation.  The project, known as MOTIVATE [Matrix Optimization for Testing by Interaction of Virtual and Test Environments (Grant Nr. 754660)], is funded through the Clean Sky 2 Joint Undertaking with Airbus acting as our topic manager to guide us towards an outcome that will be applicable in industry.  We held our kick-off meeting in Liverpool last week, which is why it is uppermost in my mind at the moment.  We have 36-months to get smarter on an industrial scale and demonstrate it in a full-scale test on an aircraft structure.  So, some sleepness nights ahead…


ASME V&V 10-2006, Guide for verification & validation in computational solid mechanics, American Society of Mech. Engineers, New York, 2006.

European Committee for Standardisation (CEN), Validation of computational solid mechanics models, CEN Workshop Agreement, CWA 16799:2014 E.

Hack E & Lampeas G (Guest Editors) & Patterson EA (Editor), Special issue on advances in validation of computational mechanics models, J. Strain Analysis, 51 (1), 2016.

Happenstance, not engineering?


A few weeks ago I wrote that ‘engineering is all about ingenuity‘ [post on September 14th, 2016] and pointed out that while some engineers are involved in designing, manufacturing and maintaining engines, most of us are not.  So, besides being ingenious, what do the rest of us do?  Well, most of us contribute in some way to the conception, building and sustaining of networks.  Communication networks, food supply networks, power networks, transport networks, networks of coastal defences, networks of oil rigs, refineries and service stations, or networks of mines, smelting works and factories that make everything from bicycles to xylophones.  The list is endless in our highly networked society.  A network is a group of interconnected things or people.  And, engineers are responsible for all of the nodes in our networks of things and for just about all the connections in our networks of both things and people.

Engineers have been constructing networks by building nodes and connecting them for thousands of years, for instance the ancient Mesopotamians were building aqueducts to connect their towns with distance water supplies more than four millenia ago.

Engineered networks are so ubiquitous that no one notices them until something goes wrong, which means engineers tend to get blamed more than praised.  But apparently that is the fault of the ultimate network: the human brain.  Recent research has shown that blame and praise are assigned by different mechanisms in the brain and that blame can be assigned by every location in the brain responsible for emotion whereas praise comes only from a single location responsible for logical thought.  So, we blame more frequently than we praise and we tend to assume that bad things are deliberate while good things are happenstance.  So reliable networks are happenstance rather than good engineering in the eyes of most people!


Ngo L, Kelly M, Coutlee CG, Carter RM , Sinnott-Armstrong W & Huettel SA, Two distinct moral mechanisms for ascribing and denying intentionality, Scientific Reports, 5:17390, 2015.

Bruek H, Human brains are wired to blame rather than to praise, Fortune, December 4th 2015.


Art and engineering

Windows of the Soul II [3D video art installation:]

Windows of the Soul II [3D video art installation:

A couple of weeks ago I wrote about the meaning of the words ‘engineer’ and ‘engineering’ [see my post entitled ‘Engineering is all about ingenuity‘ on September 14th, 2016] .  And it was clear that most engineers are involved in some sort of creative activity.  One of the common skills that unites the many different types of engineering is creative problem-solving.  But in that case how are engineers different from artists who are also involved in creative acts?  David Blockley summarises it succinctly as engineers produce something useful and artists produce something extraordinary.  Of course, very occasionally we manage to do both and an artist-engineer produces something extraordinary that is also useful.  I say ‘very occasionally’ because extraordinary implies it is exceptional, which eliminates mass-produced artifacts. It is difficult to identify modern creations that fit this description – the Large Hadron Collider is an extraordinary piece of engineering but is it art?  It is a product of the application of human skill and imagination, which is another definition of art.  Or the Solar Impulse – the solar powered plane that flew around the world?

On the other hand, when we visit art galleries we can buy prints and postcards that are copies of the artworks displayed in the gallery. Is the mass-produced, but iconic, engineering artifact equivalent to an art print? Perhaps the original has to be rather less transitory than the latest model of phone or car.  The advent of computer-aided engineering and rapid prototyping means that the original often only exists in virtual space, which is more equivalent to the video installations that are becoming more commonplace in galleries, such as Sonia Falcone’s ‘Best Video Installation Art at the Biennale in Santa Cruz Bolivia‘.

Engineering is all about ingenuity

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!


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.


Credibility is in the eye of the beholder

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.