Category Archives: structures

Seeing small changes is a big achievement

Figure 8 from Amjad et al 2022Some years ago I wrote with great excitement about publishing a paper in Royal Society Open Science [see ‘Press release!‘ on November 15th, 2017].  This has become a routine event; however, the excitement returned earlier this month when we had a paper published in the Proceedings of Royal Society of London on ‘A thermal emissions-based real-time monitoring system for in situ detection of cracks’.  The Proceedings were first published in February 1831 and this is only the second time in my career that my group has published a paper in them.  The last time was ten years ago and was also about cracks: ‘Quantitative measurement of plastic strain field at a fatigue crack tip’.  I have already described this earlier work in a post [see ‘Scattering electrons reveal dislocations in material structure’ on November 11th, 2020].  This was the first time that the size and shape of the plastic zone around a crack had been measured directly rather than inferred from other measurements.  It required an expensive scanning electron microscope and a well-equipped laboratory.  In contrast, the work in the paper published this month uses components that can be purchased for the price of a smart phone and assembled into a device not much larger than a smart phone.  The device detects the changes in the temperature distribution over the surface of the metal caused by the propagation of a crack due to repeated loading of the metal.  It is based on the principles of thermoelastic stress analysis [see ‘Counting photons to measure stress‘ on November 18th, 2015], which is a well-established measurement technique that usually requires expensive infra-red cameras.  Our key innovation is to not aim for absolute measurement values, which allows us to ignore calibration requirements, and instead to look for changes in the temperature distribution on the metal surface by extracting feature vectors from the images [see ‘Recognising strain‘ on October 28th 2015].  Our approach lowers the cost of the equipment required by several orders of magnitude, achieves comparable or better resolution of crack growth (around 1 mm) and will function at lower loading frequencies than techniques based on classical thermoelastic stress analysis.  Besides crack analysis, the common theme of the two papers is the innovative use of image processing to identify change, based on the fracture mechanics of crack propagation.

The research reported in this month’s paper was largely performed as part of the DIMES project about which I have written many posts.

The University of Liverpool was the coordinator of the DIMES project and the other partners were Empa, Dantec Dynamics GmbH and Strain Solutions Ltd.  Airbus was the topic manager on behalf of the Clean Sky 2 Joint Undertaking.

Logos of Clean Sky 2 and EUThe DIMES project received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820951.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

References:

Amjad, K., Lambert, C.A., Middleton, C.A., Greene, R.J., Patterson, E.A., 2022, A thermal emissions-based real-time monitoring system for in situ detection of cracks, Proc. R. Soc. A., doi: 10.1098/rspa.2021.0796.

Yang, Y., Crimp, M., Tomlinson, R.A., Patterson, E.A., 2012, Quantitative measurement of plastic strain field at a fatigue crack tip, Proc. R. Soc. A., 468(2144):2399-2415.

Image: Figure 8 from Amjad et al, 2022, Proc. R. Soc. A., doi: 10.1098/rspa.2021.0796.

Nudging discoveries along the innovation path

Decorative photograph of a Welsh hillThe path from a discovery to a successful innovation is often tortuous and many good ideas fall by the wayside.  I have periodically reported on progress along the path for our novel technique for extracting feature vectors from maps of strain data [see ‘Recognizing strain‘ on October 28th, 2015] and its application to validating models of structures by comparing predicted and measured data [see ‘Million to one‘ on November 21st, 2018], and to tracking damage in composite materials [see ‘Spatio-temporal damage maps‘ on May 6th, 2020] as well as in metallic aircraft structures [see ‘Out of the valley of death into a hype cycle‘ on February 24th 2021].  As industrial case studies, we have deployed the technology for validation of predictions of structural behaviour of a prototype aircraft cockpit [see ‘The blind leading the blind‘ on May 27th, 2020] as part of the MOTIVATE project and for damage detection during a wing test as part of the DIMES project.  As a result of the experience gained in these case studies, we recently published an enhanced version of our technique for extracting feature vectors that allows us to handle data from irregularly shaped objects or data sets with gaps in them [Christian et al, 2021].  Now, as part of the Smarter Testing project [see ‘Jigsaw puzzling without a picture‘ on October 27th, 2021] and in collaboration with Dassault Systemes, we have developed a web-based widget that implements the enhanced technique for extracting feature vectors and compares datasets from computational models and physical models.  The THEON web-based widget is available together with a video demonstration of its use and a user manual.  We supplied some exemplar datasets based on our work in structural mechanics as supplementary material associated with our publication; however, it is applicable across a wide range of fields including earth sciences, as we demonstrated in our recent work on El Niño events [see ‘From strain measurements to assessing El Niño events‘ on March 17th, 2021].  We feel that we have taken some significant steps along the innovation path which will lead to adoption of our technique by a wider community; but only time will tell whether this technology survives or falls by the wayside despite our efforts to keep it on track.

Bibliography

Christian WJR, Dvurecenska K, Amjad K, Pierce J, Przybyla C & Patterson EA, Real-time quantification of damage in structural materials during mechanical testing, Royal Society Open Science, 7:191407, 2020.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021

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

Middleton CA, Weihrauch M, Christian WJR, Greene RJ & Patterson EA, Detection and tracking of cracks based on thermoelastic stress analysis, R. Soc. Open Sci. 7:200823, 2020.

Wang W, Mottershead JE, Patki A, Patterson EA, Construction of shape features for the representation of full-field displacement/strain data, Applied Mechanics and Materials, 24-25:365-370, 2010.

On flatness and roughness

Photograph of aircraft carrier in heavy seas for decorative purposes onlyFlatness is a tricky term to define.  Technically, it is the deviation, or lack of deviation, from a plane. However, something that appears flat to human eye often turns out not to be at all flat when looked at closely and measured with a high resolution instrument.  It’s a bit like how the ocean might appear flat and smooth to a passenger sitting comfortably in a window seat of an aeroplane and looking down at the surface of the water below but feels like a roller-coaster to a sailor in a small yacht.  Of course, if the passenger looks at the horizon instead of down at the yacht below then they will realise the surface of the ocean is curved but this is unlikely to be apparent to the sailor who can only see the next line of waves advancing towards them.  Of course, the Earth is not flat and the waves are better described as surface roughness.  Some months ago I wrote about our struggles to build a thin flat metallic plate using additive manufacturing [see ‘If you don’t succeed, try and try again…’ on September 29th, 2021].  At the time, we were building our rectangular plates in landscape orientation and using buttresses to support them during the manufacturing process; however, when we removed the plates from the machine and detached the buttresses they deformed into a dome-shape.  I am pleased to say that our perseverance has paid off and recently we have been much more successful by building our plates orientated in portrait mode, i.e., with the short side of the rectangle horizontal, and using a more sophisticated design of buttresses.  Viewed from the right perspective our recent plates could be considered flat though in reality they deviate from a plane by less than 3% of their in-plane dimensions and also have a surface roughness of several tens of micrometres (that’s the average deviation from the surface).  The funding organisations for our research expect us to publish our results in a peer-reviewed journal that will only accept novel unpublished results so I am not going to say anything more about our flat plates.  Instead let me return to the ocean analogy and try to make you seasick by recalling an earlier career in which I was on duty on the bridge of an aircraft carrier ploughing through seas so rough, or not flat, that waves were breaking over the flight deck and the ship felt like it was still rolling and pitching when we sailed serenely into port some days later.

The current research is funded jointly by the National Science Foundation (NSF) in the USA and the Engineering and Physical Sciences Research Council (EPSRC) in the UK (see Grants on the Web).

Image from https://laststandonzombieisland.com/2015/07/22/warship-wednesday-july-22-2015-the-giant-messenger-god/1977-hms-hermes-r-12-with-her-bows-nearly-out-of-the-water/

Jigsaw puzzling without a picture

A350 XWB passes Maximum Wing Bending test

A350 XWB passes Maximum Wing Bending test

Research sometimes feels like putting together a jigsaw puzzle without the picture or being sure you have all of the pieces.  The pieces we are trying to fit together at the moment are (i) image decomposition of strain fields [see ‘Recognising strain’ on October 28th 2015] that allows fields containing millions of data values to be represented by a feature vector with only tens of elements which is useful for comparing maps or fields of predictions from a computational model with measurements made in the real-world; (ii) evaluation of the variation in measurement uncertainty over a field of view of measured displacements or strains in a large structure [see ‘Industrial uncertainty’ on December 12th 2018] which provides information about the quality of the measurements; and (iii) a probabilistic validation metric that provides a measure of how well predictions from a computational model represent measurements made in the real world [see ‘Million to one’ on November 21st 2018].  We have found some of the missing pieces of the jigsaw, for example we have established how to represent the distribution of measurement uncertainty in the feature vector domain [see ‘From strain measurements to assessing El Niño events’ on March 17th 2021] so that it can be used to assess the significance of differences between measurements and predictions represented by their feature vectors – this connects (i) and (ii) together.  Very recently we have demonstrated a generic technique for performing image decomposition of irregularly shaped fields of data or data fields with holes [see Christian et al, 2021] which extends the applicability of our method for comparing measurements and predictions to real-world objects rather than idealised shapes.  This allows (i) to be used in industrial applications but we still have to work out how to connect this to the probabilistic metric in (iii) while also incorporating spatially-varying uncertainty.  These techniques can be used in a wide range of applications, as demonstrated in our recent work on El Niño events [see Alexiadis et al, 2021], because, by treating all fields of data as images, the techniques are agnostic about the source and format of the data.  However, at the moment, our main focus is on their application to ground tests on aircraft structures as part of the Smarter Testing project in collaboration with Airbus, Centre for Modelling & Simulation, Dassault Systèmes, GOM UK Ltd, and the National Physical Laboratory with funding from the Aerospace Technology Institute.  Together we are working towards digital continuity across virtual and physical testing of aircraft structures to provide live data fusion and enable condition-led inspections, test control and validation of computational models.  We anticipate these advances will reduce time and costs for physical tests and accelerate the development of new designs of aircraft that will contribute to global sustainability targets (the aerospace industry has committed to reduce CO2 emissions to 50% of 2005 levels by 2050).  The Smarter Testing project has an ambitious goal which reveals that our pieces of the jigsaw puzzle belong to a small section of a much larger one.

For more on the Smarter Testing project see:

https://www.aerospacetestinginternational.com/news/structural-testing/smarter-testing-research-program-to-link-virtual-and-physical-aerospace-testing.html

https://www.aerospacetestinginternational.com/opinion/how-integrating-the-virtual-and-physical-will-make-aerospace-testing-and-certification-smarter.html

References

Alexiadis A, Ferson S, Patterson EA. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society open science. 8(3):201086, 2021.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021.

Image: http://www.airbus.com/galleries/photo-gallery