Tag Archives: research

Reliable predictions of non-Newtonian flows of sludge

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Regular readers of this blog will be aware that I have been working for many years on validation processes for computational models of structures employed in a wide range of sectors, including aerospace engineering [see ‘The blind leading the blind’ on May 27th, 2020] and nuclear energy [see ‘Million to one’ on November 21st, 2018].  Validation is determining the extent to which predictions from a model are representative of behaviour in the real-world [see ‘Model validation’ on September 18th, 2012].  More recently, I have been working on model credibility, which is the willingness of people, besides the modeller, to use the predictions from models in decision-making [see, for example, ‘Credible predictions for regulatory decision-making’ on December 9th, 2020].  I have started to consider the complex world of predictive modelling of fluid flow and I am hoping to start a collaboration with a new colleague on the flow of sludges.  Sludges are more common than you might think but we are interested in modelling the flow of waste, both wastewater (sewage) and nuclear wastes.  We have a PhD studentship available sponsored jointly by the GREEN CDT and the National Nuclear Laboratory.  The project is interdisciplinary in two dimensions because it will combine experiments and simulations as well as uniting ideas from solid mechanics and fluid mechanics.  The integration of concepts and technologies across these boundaries brings a level of adventure to the project which will be countered by building on well-established research in solid mechanics on quantitative comparisons of measurements and predictions and by employing current numerical and experimental work on wastewater sludges.  If you are interested or know someone who might want to join our research then you can find out more here.

Image: Sewage sludge disposal in Germany: Andrea Roskosch / UBA

Reflecting on self

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In a recent interview, the artist William Kentridge described becoming another person when standing back from a work in progress and becoming a critical director of the other person’s work.  He talked about ‘constructing myself from yesterday’s dream and tomorrow’s expectation’.  I have had similar experiences when I am speaking to an audience, lecturing to students or making a presentation at a conference.  I mentally stand back from the speaking self and the other self reviews what is happening and sometimes starts mind-wandering triggered by something said by the speaking self or a reaction from the audience.  I talk about ‘self’ when I am lecturing on leadership as part of our Continuous Professional Development programme [see ‘On being a leader’ on October 13th, 2021].  I am often asked what is meant by ‘self’ and ‘identity’, particularly in the context of Kegan’s scheme of cognitive development [see ‘Illusion of self’ on February 2nd, 2017].  I sense that students are often dissatisfied with my answers.  So, let me attempt a written answer here.  A dictionary definition of ‘self’ is ‘the entire being of an individual that constitutes the individuality and identity of a person’.  In psychology, it might be defined as ‘the totality of the individual, consisting of all characteristic attributes, conscious and unconscious, mental and physical.’  A dictionary definition of ‘identity’ is ‘the distinguishing character or personality of an individual’ and in sociology it is ‘the qualities, beliefs, personality traits, appearance and, or experiences that characterise a person’.  Hence, combining these definitions, identity is the attributes that characterise your ‘self’ and distinguishes you from others.  Kegan’s schema implies that our sense of self develops through childhood, adolescence and early adulthood to the extent that some people (about 35%) can separate their relationships and identity from their self and hence are capable of more nuanced decision-making – this is known as the Institutional stage.  About one percent of the population develop to a further stage, known as the Interindividual stage, where they are capable holding many identities and handling the resultant paradoxes that arise, which can help them to exercise both emotion and rationality as leaders.  I think that self is closely related to our consciousness and consequently is constructed from yesterday’s experiences and tomorrow’s dreams to misquote Kentridge.  So, perhaps it is reasonable to think that we construct, or at least evolve, a self each day as we engage in different roles, for example in my case as a teacher, researcher, university leader or family member.  I suspect that it is my researcher self that sits on the shoulder of my teacher self and mind-wanders while my teacher self talks about something else.  My experiences and dreams in each role are different, divergent even, and means that I have at least two selves that exist towards opposite ends of the ‘Change Style Indicator and have different qualities as well as experiences.

Sources

Peter Aspden, ‘The self is a construction we make every day: Lunch with the FT – William Kentridge’, 22 October / 23 October 2022.

Kegan, R., The evolving self: problem and process in human development, Cambridge, MA: Harvard University Press, 1982.

Longman Dictionary of the English Language, Harlow, UK: Longman Group Limited, 1984.

Seeing small changes is a big achievement

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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

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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.