Tag Archives: experimental mechanics

Engineering synaesthesia

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A street in Sante Fe

A street in Sante Fe

One of the most memorable places we visited when we lived in the United States was Sante Fe, New Mexico.  We rented a house on a hillside that was walking distance from downtown.  The landscape is stark, vast and vivid all at the same time.  Georgia O’Keeffe captured it beautifully in her paintings.  In our house in Liverpool, we have a number of prints from her paintings that we bought during a visit to the Georgia O’Keeffe Museum in Sante Fe about ten years ago. So it was a nostalgic experience to visit the O’Keeffe exhibition at the Tate Modern in London a few weeks ago and reacquaint ourselves with familiar originals as well as enjoy paintings we had not seen before. ‘Red and Yellow Cliffs‘ (1940) was one of my favourites in the exhibition which was reminiscent of many of the landscapes in New Mexico.  I also enjoyed the room entitled ‘Abstraction and the Senses’ that contained a series of paintings in which O’Keeffe took inspiration from sensory stimulation and expressed in her paintings the feelings induced by ‘signals’ from senses other than sight, such as hearing music.  This is known as synaesthesia: ‘the production of a sense impression relating to one sense or part of the body by stimulation of another sense or part of the body’, according the Oxford Online Dictionary.  Some people suffer from synaesthesia and hearing particular sounds might trigger a sensation of taste, or letters might be associated with colours, for instance ‘A’ with red. It can be very useful, for instance I ‘see’ numbers laid out in patterns and so can perform mental arithmetic pictorially.

Engineers make use of similar phenomena to visualize patterns of variables that are invisible.  For instance, moiré interferometry uses the interference between regular arrays of lines to magnify tiny differences in the arrays and generate visible fringe patterns – this is useful in comparing the dimensions of two objects to which the arrays are attached.  In photoelasticity, polarised light is used to generate colour fringe patterns that are contours of stress in transparent components or models of components [see my post entitled ‘Art and Experimental Mechanics‘ on July 12th, 2012].  Unfortunately this elegant, but analogue, technique has been almost completely usurped by digital analysis using computers. Many of these computers have a touch screen that convert your thoughts, conveyed by the tap or swipe of your fingers, into text or commands for devices attached physically or wirelessly to the computer. And, virtual reality goggles, head sets and haptic devices allow the computer to reverse the process by transmitting signals to our senses, which often confuse us as they become intermingled in a new form of synaesthesia.  Georgia O’Keeffe died in 1986 at the age of 98 and so missed out on this aspect of the digital revolution but it might have generated a whole series of beautiful paintings.

Sources:

http://www.nhs.uk/conditions/synaesthesia/Pages/Introduction.aspx

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.

Models as fables

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moel arthurIn his book, ‘Economic Rules – Why economics works, when it fails and how to tell the difference‘, Dani Rodrik describes models as fables – short stories that revolve around a few principal characters who live in an unnamed generic place and whose behaviour and interaction produce an outcome that serves as a lesson of sorts.  This seems to me to be a healthy perspective compared to the almost slavish belief in computational models that is common today in many quarters.  However, in engineering and increasingly in precision medicine, we use computational models as reliable and detailed predictors of the performance of specific systems.  Quantifying this reliability in a way that is useful to non-expert decision-makers is a current area of my research.  This work originated in aerospace engineering where it is possible, though expensive, to acquire comprehensive and information-rich data from experiments and then to validate models by comparing their predictions to measurements.  We have progressed to nuclear power engineering in which the extreme conditions and time-scales lead to sparse or incomplete data that make it more challenging to assess the reliability of computational models.  Now, we are just starting to consider models in computational biology where the inherent variability of biological data and our inability to control the real world present even bigger challenges to establishing model reliability.

Sources:

Dani Rodrik, Economic Rules: Why economics works, when it fails and how to tell the difference, Oxford University Press, 2015

Patterson, E.A., Taylor, R.J. & Bankhead, M., A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016

Hack, E., Lampeas, G. & Patterson, E.A., An evaluation of a protocol for the validation of computational solid mechanics models, J. Strain Analysis, 51(1):5-13, 2016.

Patterson, E.A., Challenges in experimental strain analysis: interfaces and temperature extremes, J. Strain Analysis, 50(5): 282-3, 2015

Patterson, E.A., On the credibility of engineering models and meta-models, J. Strain Analysis, 50(4):218-220, 2015