Industrial uncertainty

Last month I spent almost a week in Zurich.  It is one of our favourite European cities [see ‘A reflection of existentialism‘ on December 20th, 2017]; however, on this occasion there was no time for sight-seeing because I was there for the mid-term meeting of the MOTIVATE project and to conduct some tests and demonstrations in the laboratories of our host, EMPA, the Swiss Federal Laboratories for Materials Science and Technology.  Two of our project partners, Dantec Dynamics GmbH based in Ulm, Germany, and the Athena Research Centre in Patras, Greece, have developed methods for quantifying the uncertainty present in measurements of deformation made in an industrial environment using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014].  Digital image correlation is a technique in which we usually apply a random speckle pattern to the object which allows us to track the movement of the object surface over time by searching for the new position of the speckles in the photographs of the object.  If we use a pair of cameras in a stereoscopic arrangement, then we can measure in-plane and out-of-plane displacements.  Digital image correlation is a well-established measurement technique that has become ubiquitous in mechanics laboratories. In previous EU projects, we have developed technology for quantifying uncertainty in in-plane [SPOTS project] and out-of-plane [ADVISE project] measurements in a laboratory environment.  However, when you take the digital image correlation equipment into an industrial environment, for instance an aircraft hangar to make measurements during a full-scale test, then additional sources of uncertainty and error appear. The new technology demonstrated last month allows these additional uncertainties to be quantified.  As part of the MOTIVATE project, we will be involved in a full-scale test on a large section of an Airbus aircraft next year and so, we will be able to utilise the new technology for the first time.

The photograph shows preparations for the demonstrations in EMPA’s laboratories.  In the foreground is a stereoscopic digital image correlation system with which we are about to make measurements of deformation of a section of aircraft skin, supplied by Airbus, which has a speckle pattern on its surface and is about to be loaded in compression by the large servo-hydraulic test machine.

References:

From SPOTS project:

Patterson EA, Hack E, Brailly P, Burguete RL, Saleem Q, Seibert T, Tomlinson RA & Whelan M, Calibration and evaluation of optical systems for full-field strain measurement, Optics and Lasers in Engineering, 45(5):550-564, 2007.

Whelan MP, Albrecht D, Hack E & Patterson EA, Calibration of a speckle interferometry full-field strain measurement system, Strain, 44(2):180-190, 2008.

From ADVISE project:

Hack E, Lin X, Patterson EA & Sebastian CM, A reference material for establishing uncertainties in full-field displacement measurements, Measurement Science and Technology, 26:075004, 2015.

Where have all the insects gone?

I remember when our children were younger, and we went on our summer holidays by car, that the car windscreen would be splattered with the remains of dead insects.  This summer my wife and I drove to Cornwall and back for our holidays almost without a single insect hitting our windscreen.  Where have all of the insects gone?  It would appear that we, the human species, have wiped them out as a consequence of the way we exploit the planet for our own comfort and convenience.  Insecticides and monocultures aided by genetically-modified crops make a direct contribution but our consumption of fossil fuels and intensive production of everything from beef [see ‘A startling result‘ on May 18th, 2016] to plastics is changing the environment [see ‘Productive cheating?‘ on November 27th, 2013]. The biologist, Edward O. Wilson observed that ‘If all humankind were to disappear, the world would regenerate back to the rich state of equilibrium that existed 10,000 years ago. If insects were to vanish, the environment would collapse into chaos.’  It looks like we are on the cusp of that collapse.

Cristiana Pașca Palmer, the executive secretary of the UN Convention on Biological Diversity has highlighted the impact of our actions as a species on the other species with which we share this planet.  We are making the planet uninhabitable for an increasing number of species to the extent that the rate of extinct is perhaps the fastest ever seen and we might be the first species to catalogue its own demise.  Our politicians have demonstrated their inability to act together over climate change even when it leads to national disasters in many countries; so, it seems unlikely that they will agree on significant actions to arrest the loss of bio-diversity.  We need to act as individuals, in whatever way we can, to reduce our ecological footprints – that impact that we have on the environment [see ‘New Year Resolution‘ on December 31st, 2014] .  As the Roman poet Horace wrote: ‘You are also affected when your neighbour’s house is on fire’; so, we should not think that none of this affect us.

See also:

Man, the Rubbish Maker

Are we all free-riders?

Epistemic triage

A couple of weeks ago I wrote about epistemic dependence and the idea that we need to trust experts because we are unable to verify everything ourselves as life is too short and there are too many things to think about.  However, this approach exposes us to the risk of being misled and Julian Baggini has suggested that this risk is increasing with the growth of psychology, which has allowed more people to master methods of manipulating us, that has led to ‘a kind of arms race of deception in which truth is the main casualty.’  He suggests that when we are presented with new information then we should perform an epstemic triage by asking:

  • Is this a domain in which anyone can speak the truth?
  • What kind of expert is a trustworthy source of truth in that domain?
  • Is a particular expert to be trusted?

The deluge of information, which streams in front of our eyes when we look at the screens of our phones, computers and televisions, seems to leave most of us grasping for a hold on reality.  Perhaps we should treat it all as fiction until have performed Baggini’s triage, at least on the sources of the information streams, if not also the individual items of information.

Source:

Julian Baggini, A short history of truth: consolations for a post-truth world, London: Quercus Editions Ltd, 2017.

Million to one

‘All models are wrong, but some are useful’ is a quote, usually attributed to George Box, that is often cited in the context of computer models and simulations.  Working out which models are useful can be difficult and it is essential to get it right when a model is to be used to design an aircraft, support the safety case for a nuclear power station or inform regulatory risk assessment on a new chemical.  One way to identify a useful model to assess its predictions against measurements made in the real-world [see ‘Model validation’ on September 18th, 2012].  Many people have worked on validation metrics that allow predicted and measured signals to be compared; and, some result in a statement of the probability that the predicted and measured signal belong to the same population.  This works well if the predictions and measurements are, for example, the temperature measured at a single weather station over a period of time; however, these validation metrics cannot handle fields of data, for instance the map of temperature, measured with an infrared camera, in a power station during start-up.  We have been working on resolving this issue and we have recently published a paper on ‘A probabilistic metric for the validation of computational models’.  We reduce the dimensionality of a field of data, represented by values in a matrix, to a vector using orthogonal decomposition [see ‘Recognizing strain’ on October 28th, 2015].  The data field could be a map of temperature, the strain field in an aircraft wing or the topology of a landscape – it does not matter.  The decomposition is performed separately and identically on the predicted and measured data fields to create to two vectors – one each for the predictions and measurements.  We look at the differences in these two vectors and compare them against the uncertainty in the measurements to arrive at a probability that the predictions belong to the same population as the measurements.  There are subtleties in the process that I have omitted but essentially, we can take two data fields composed of millions of values and arrive at a single number to describe the usefulness of the model’s predictions.

Our paper was published by the Royal Society with a press release but in the same week as the proposed Brexit agreement and so I would like to think that it was ignored due to the overwhelming interest in the political storm around Brexit rather than its esoteric nature.

Source:

Dvurecenska K, Graham S, Patelli E & Patterson EA, A probabilistic metric for the validation of computational models, Royal Society Open Science, 5:1180687, 2018.