MyResearch

Red to blue

Some research has a very long incubation time.  Last month, we published a short paper that describes the initial results of research that started just after I arrived in Liverpool in 2011.  There are various reasons for our slow progress, including our caution about the validity of the original idea and the challenges of working across discipline boundaries.  However, we were induced to rush to publication by the realization that others were catching up with us [see blog post and conference paper].  Our title does not give much away: ‘Characterisation of metal fatigue by optical second harmonic generation‘.

Second harmonic generation or frequency doubling occurs when photons interact with a non-linear material and are combined to produce new photons with twice the energy, and hence, twice the frequency and half the wavelength of the original photons.  Photons are discrete packets of energy that, in our case, are supplied in pulses of 2 picoseconds from a laser operating at a wavelength of 800 nanometres (nm).  The photons strike the surface, are reflected, and then collected in a spectrograph to allow us to evaluate the wavelength of the reflected photons.  We look for ones at 400 nm, i.e. a shift from red to blue.

The key finding of our research is that the second harmonic generation from material in the plastic zone ahead of a propagating fatigue crack is different to virgin material that has experienced no plastic deformation.  This is significant because the shape and size of the crack tip plastic zone determines the rate and direction of crack propagation; so, information about the plastic zone can be used to predict the life of a component.  At first sight, this capability appears similar to thermoelastic stress analysis that I have described in Instructive Update on October 4th, 2017; however, the significant potential advantage of second harmonic generation is that the component does not have to be subject to a cyclic load during the measurement, which implies we could study behaviour during a load cycle as well as conduct forensic investigations.  We have some work to do to realise this potential including developing an instrument for routine measurements in an engineering laboratory, rather than an optics lab.

Last week, I promised weekly links to posts on relevant Thermodynamics topics for students following my undergraduate module; so here are three: ‘Emergent properties‘, ‘Problem-solving in Thermodynamics‘, and ‘Running away from tigers‘.

 

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Instructive Update

Six months ago I wrote about our EU research project, called INSTRUCTIVE, and the likely consequences of Brexit for research [see my post: ‘Instructive report and Brexit‘ on March 29th, 2017].  We seem to be no closer to knowing the repercussions of Brexit on research in the UK and EU – a quarter of EU funding allocated to universities goes to UK universities so the potential impacts will hit both the UK and EU.  Some researchers take every opportunity to highlight these risks and the economic benefits of EU research; for instance the previous EU research programme, Framework Programme 7, is estimated to have created 900,000 jobs in Europe and increased GDP by about 1% in perpetuity.  However, most researchers are quietly getting on with their research and hoping that our political leaders will eventually arrive at a solution that safeguards our prosperity and security.  Our INSTRUCTIVE team is no exception to this approach.  We are about half-way through our project and delivered our first public presentation of our work at the International Conference on Advances in Experimental Mechanics last month.  We described how we are able to identify cracks in metallic structures before they are long enough to be visible to the naked eye, or any other inspection technique commonly used for aircraft structures.  We identify the cracks using an infra-red camera by detecting the energy released during the formation and accumulation of dislocations in the atomic structure that coalesce into voids and eventually into cracks [see my post entitled ‘Alan Arnold Griffith‘ on April 26th, 2017 for more on energy release during crack formation].  We can identify cracks at sub-millimetre lengths and then track them as they propagate through a structure.  At the moment, we are quantifying our ability to detect cracks forming underneath the heads of fasteners [see picture] and other features in real aerospace structures; so that we can move our technology out of the laboratory and into an industrial environment.  We have a big chunk of airplane sitting in the laboratory that we will use for future tests – more on that in later blog posts!

INSTRUCTIVE is an EU Horizon 2020 project funded under the Clean Sky 2 programme [project no. 686777] and involves Strain Solutions Ltd and the University of Liverpool working with Airbus.

Statistics on funding from http://russellgroup.ac.uk/news/horizon-2020-latest-statistics/and https://www.russellgroup.ac.uk/media/5068/24horizon-2020-the-contribution-of-russell-group-universities-june-201.pdf

For other posts on similar research topics, see ‘Counting photons to measure stress‘ on November 18th, 2015 and ‘Forensic engineering‘ on July 22nd, 2015.

Less uncertain predictions

Ultrasound time-of-flight C-scan of the delaminations formed by a 12J impact on a crossply laminate (top) and the corresponding surface strain field (bottom).

Here is a challenge for you: overall this blog has a readability index of 8.6 using the Flesch Kincaid Grades, which means it should be easily understood by 14-15 year olds.  However, my editor didn’t understand the first draft of the post below and so I have revised it; but it still scores 15 using Flesch Kincaid!  So, it might require the formation of some larger scale neuronal assemblies in your brain [see my post entitled ‘Digital Hive Mind‘ on November 30th, 2016].

I wrote a couple of weeks ago about guessing the weight of a reader.  I used some national statistics and suggested how they could be updated using real data about readers’ weights with the help of Bayesian statistics [see my post entitled ‘Uncertainty about Bayesian statistics’ on July 5th, 2017].  It was an attempt to shed light on the topic of Bayesian statistics, which tends to be obscure or unknown.  I was stimulated by our own research using Bayesian statistics to predict the likelihood of failure in damaged components manufactured using composite material, such as carbon-fibre laminates used in the aerospace industry.  We are interested in the maximum load that can be carried by a carbon-fibre laminate after it has sustained some impact damage, such as might occur to an aircraft wing-skin that is hit by debris from the runway during take-off, which was the cause of the Concorde crash in Paris on July 25th, 2000.  The maximum safe load of the carbon-fibre laminate varies with the energy of the impact, as well as with the discrepancies introduced during its manufacture.  These multiple variables make our analysis more involved than I described for readers’ weights.  However, we have shown that the remaining strength of a damage laminate can be more reliably predicted from measurements of the change in the strain pattern around the damage than from direct measurements of the damage for instance, using ultrasound.

This might seem to be a counter-intuitive result.  However, it occurs because the failure of the laminate is driven by the energy available to create new surfaces as it fractures [see my blog on Griffith fracture on April 26th, 2017], and the strain pattern provides more information about the energy distribution than does the extent of the existing damage.  Why is this important – well, it offers a potentially more reliable approach to inspecting aircraft that could reduce operating costs and increase safety.

If you have stayed with me to the end, then well done!  If you want to read more, then see: Christian WJR, Patterson EA & DiazDelaO FA, Robust empirical predictions of residual performance of damaged composites with quantified uncertainties, J. Nondestruct. Eval. 36:36, 2017 (doi: 10.1007/s10921-017-0416-6).

Getting smarter

A350 XWB passes Maximum Wing Bending test [from: http://www.airbus.com/galleries/photo-gallery%5D

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…

Bibliography:

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.

http://www.engineeringvalidation.org/

Instructive report and Brexit

Even though this blog is read in more than 100 countries, surely nobody can be unaware of the furore about Brexit – the UK Government’s plan to leave the European Union.  The European Commission has been funding my research for more than twenty years and I am a frequent visitor to their Joint Research Centre in Ispra, Italy.  During the last decade, I have led consortia of industry, national labs and universities that rejoice in names such as SPOTS, VANESSA and, most recently MOTIVATE.  These are acronyms based loosely on the title of the research project.  Currently, there is no sign that these pan-European research programmes will exclude scientists and engineers from the UK, but then the process of leaving the EU has not yet started, so who knows…

At the moment, I am working with a small UK company, Strain Solutions Ltd, on a EU project called INSTRUCTIVE.  I said these were loose acronyms and this one is very loose: Infrared STRUctural monitoring of Cracks using Thermoelastic analysis in production enVironmEnts.  We are working with Airbus in France, Germany, Spain and the UK to transition a technology from the laboratory to the industrial test environment.  Airbus conducts full-scale fatigue tests on airframe structures to ensure that they have the appropriate life-cycle performance and the INSTRUCTIVE project will deliver a new tool for monitoring the development of damage, in the form of cracks, during these tests.  The technology is thermoelastic stress analysis, which is well-established as a laboratory-based technique [1] for structural analysis [2], fracture mechanics [3] and damage mechanics [4], that I described in a post on November 18th, 2015 [see ‘Counting photons to measure stress’].  It’s exciting to be evolving it into an industrial technique but also to be looking at the potential to apply it using cheap infrared cameras instead of the current laboratory instruments that cost tens of thousands of any currency.  It’s a three-year project and we’ve just completed our first year so we should finish before any Brexit consequences!  Anyway, the image gives you a taster and I plan to share more results with you shortly…

BTW – You might get the impression from my recent posts that teaching MOOCs [see ‘Slowing down time to think [about strain energy]’ on March 8th, 2017] and leadership [see ‘Inspirational leadership’ on March 22nd, 2018] were foremost amongst my activities.  I only write about my research occasionally.  This would not be an accurate impression because the majority of my working life is spent supervising and writing about research.  Perhaps, it’s because I spend so much time writing about research in my ‘day job’ that last year I only blogged about it three times on: digital twins [see ‘Can you trust your digital twin?’ on November 23rd, 2016], model credibility [see ‘Credibility is in the Eye of the Beholder’ on April 20th, 2016] and model validation [see Models as fables on March 16th, 2016].  This list gives another false impression – that my research is focussed on digital modelling and simulation.  It is just the trendiest part of my research activity.  So, I thought that I should correct this imbalance with some INSTRUCTIVE posts.

References:

[1] Greene, R.J., Patterson, E.A., Rowlands, R.E., 2008, ‘Thermoelastic stress analysis’, in Handbook of Experimental Mechanics edited by W.N. Sharpe Jr., Springer, New York.

[2] Rowlands, R.E., Patterson, E.A., 2008, ‘Determining principal stresses thermoelastically’, J. Strain Analysis, 43(6):519-527.

[3] Diaz, F.A., Patterson, E.A., Yates, J.R., 2009, ‘Assessment of effective stress intensity factors using thermoelastic stress analysis’, J. Strain Analysis, 44 (7), 621-632.

[4] Fruehmann RK, Dulieu-Barton JM, Quinn S, Thermoelastic stress and damage analysis using transient loading, Experimental Mechanics, 50:1075-1086, 2010.