This is the second in a series of posts reflecting on my route to becoming an engineer. In the first one I described how I chose a degree in mechanical engineering so that I would have appreciation of the technical difficulties that engineers might cite when requesting operational changes for a ship that I hoped one day to command [see ‘Reasons I became an engineer: #1’ on April 19th, 2023]. I think I selected mechanical engineering because it provided a broader engineering education than other engineering degrees and I did not know enough to choose any other branch of engineering. I went to the University of Sheffield and during vacations returned to the Royal Navy serving onboard HMS Active and flew out to join her wherever she was in the world, except when I went to the Royal Navy Engineering College at Manadon outside Plymouth to undertake engineering applications training. I cast a brass nameplate, which I still have in my office, and made a toolbox that I also still have at home. After graduation, I returned full-time to the Royal Navy as a sub-lieutenant and started my career as a naval officer in the executive or seaman branch. However, I did not settle and missed engineering so I asked for and was refused a transfer to the Royal Corps of Naval Constructors who work on the design and development of warships. As a result, I resigned my commission in the Royal Navy and got a job as a research assistant in the Department of Mechanical Engineering at the University of Sheffield where I registered for a PhD in engineering. I had taken a positive step towards becoming an engineer but perhaps on the premise of what I did not want to do rather than what I did want to do.
Tag Archives: experimental mechanics
Structural damage assessment using infrared detectors in fusion environments
About six months ago, I described the success of my research group in detecting the early stages of the development of damage in structural components using small, cheap devices based on infrared measurements [see ‘Seeing small changes is a big achievement‘ on October 26th, 2022] after it had been reported in the Proceedings of the Royal Society. The research was motivated by the needs of the aerospace industry and largely supported via the European Union’s Horizon 2020 research and innovation programme. We are planning to extend the research to allow our technology to be used for diagnostics in future fusion power plants. Plasma facing components in these powerplants will experience significant structural and functional degradation in service due to the extreme condition in the reactor. Our aim is to develop systems based on our infrared monitoring technology that can identify and track material degradation without the need for plant shutdown thereby enabling unplanned maintenance to be undertaken at the earliest sign of component failure. We are collaborating with the UKAEA and are looking to recruit a PhD student to work on the project supported by the GREEN CDT and Eurofusion. If you are interested or know someone who might be interested then please follow this link for more information.
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., 478: 20210796.
Seeing small changes is a big achievement
Some 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.
The 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.
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
The 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.
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.