Tag Archives: mechanics

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.

Change in focus

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Decorative image of a painting by Sarah Evans The new academic year is well and truly underway.  It was 2019 when we last welcomed students to campus in person for the start of the academic year.  In my role as Dean, I have been touring lecture theatres trying to speak to and welcome students in all of our taught programmes in the School of Engineering.  It is exciting to see packed lecture theatres full of students eager to listen and learn.  For the first time in a decade, I am not teaching this year so that I can focus on other activities.  I have mixed feelings about giving up teaching.  I taught my first class thirty-six years ago in Mechanics of Solids.  For the last eleven years I have been teaching Thermodynamics to first year students [see, for example ‘From nozzles and diffusers to stars and stripes‘ on March 30th, 2022].  So, teaching has been a substantial part of my working life and its absence will leave a large hole.  I will miss the excitement of standing in front of a class of hundreds of students as well as the rewards of interacting with undergraduate students who are encountering and engaging with a new subject.  One consequence of my change in focus is likely to be a decline in the frequency of blog posts featuring thermodynamics [you can read them all under ‘Thermodynamics’ in Categories], but perhaps that will be a relief to many readers.

Image: Painting by Sarah Evans owned by the author.

Aorta: structure to rupture

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Decorative image from a video showing predicted flow through aortic valve and resultant stress in leaflets of valveRegular readers have probably already realised that I have very broad interests in engineering from aircraft and power stations [see ‘Conversations about engineering over dinner and haircut‘ on February 16th, 2022] to nanoparticles interacting with cells [see ‘Fancy a pint of science‘ on April 27th, 2022].  So, it will come as no surprise to hear that I gave a welcome address to a workshop on ‘Aorta: Structure to Rupture‘ last week.  The workshop was organised in Liverpool by one of my colleagues, with sponsorship from the British Heart Foundation, and I was invited to welcome delegates in my capacity as Dean of the School of Engineering.  It was exciting on two levels: speaking, for the first time in more than two years, to an audience who had travelled from around the world to discuss research. And because the topic was closely associated with cardiac dynamics, which is a field that I worked in for nearly twenty years until around 2006.  I was part of an interdisciplinary team modelling the fluid-structure interaction in the aortic valve as it opens when blood is pumped through it by the heart and then closes to prevent back flow into the heart.  The team dispersed after I moved to the USA in 2004.  So speaking to the workshop last week was something of a trip down memory lane for me and led me to look up our last publication in the field.  I was surprised to find it was cited seven times last year.

The image in the thumbnail is a snapshot from a video showing the predicted time-varying distribution of blood flow through the aortic valve and the resultant distribution of stress in the leaflets of the valve during a heart beat.  The simultation is described in our last publication in cardiac dynamics: Carmody, C. J., Burriesci, G., Howard, I. C., & Patterson, E. A.,  An approach to the simulation of fluid–structure interaction in the aortic valve. J. Biomechanics, 39(1), 158-169, 2006.

Diving into three-dimensional fluids

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My research group has been working for some years on methods that allow straightforward comparison of large datasets [see ‘Recognizing strain’ on October 28th 2015].  Our original motivation was to compare maps of predicted strain over the surface of engineering structures with maps of measurements.  We have used these comparison methods to validate predictions produced by computational models [see ‘Million to one’ on November 21st 2018] and to identify and track changes in the condition of engineering structures [see ‘Out of the valley of death into a hype cycle’ on February 24th 2021].  Recently, we have extended this second application to tracking changes in the environment including the occurance of El Niño events [see ‘From strain measurements to assessing El Niño events’ on March 17th, 2021].  Now, we are hoping to extend this research into fluid mechanics by using our techniques to compare flow patterns.  We have had some success in exploring the use of methods to optimise the design of the mesh of elements used in computational fluid dynamics to model some simple flow regimes.  We are looking for a PhD student to work on extending our model validation techniques into fluid mechanics using volumes of data from measurement and predictions rather than fields, i.e., moving from two-dimensional to three-dimensional datasets.  If you are interested or know someone who might be interested then please get in touch.

There is more information on the PhD project here.