It is about 35 years since I graduated with my PhD. It was not ground-breaking although, together with my supervisor, I did publish about half a dozen technical papers based on it and some of those papers are still being cited, including one this month which surprises me. I performed experiments and computer modelling on the load and stress distribution in threaded fasteners, or nuts and bolts. There were no digital cameras and no computer tomography; so, the experiments involved making and sectioning models of nuts and bolts in transparent plastic using three-dimensional photoelasticity [see ‘Art and Experimental Mechanics‘ on July 17th, 2012]. I took hundreds of photographs of the sections and scanned the negatives in a microdensitometer. The computer modelling was equally slow and laborious because there were no graphical user interfaces (GUI); instead, I had to type strings of numbers into a terminal, wait overnight while the calculations were performed, and then study reams of numbers printed out on long rolls of paper. The tedium of the experimental work inspired me to work on utilising digital technology to revolutionise the field of experimental mechanics over the following 15 to 20 years. In the past 15 to 20 years, I have moved back towards computer modelling and focused on transforming the way in which measurement data are used to improve the fidelity of computer models and to establish confidence in their predictions [see ‘Establishing fidelity and credibility in tests and simulations‘ on July 25th, 2018]. Since completing my PhD, I have supervised 32 students to successful completion of their PhDs. You might think that was a straightforward process of an initial three years for the first one to complete their research and write their thesis, followed by one graduating every year. But that is not how it worked out, instead I have had fallow years as well as productive years. At the moment, I am in a productive period, having graduated two PhD students per year since 2017 – that’s a lot of reading and I have spent much of the last two weekends reviewing a thesis which is why PhD theses are the topic of this post!
We held the kick-off meeting for a new research project this week. It’s a three-way collaboration involving three professors based in Portugal, the UK and USA [Chris Sutcliffe, John Lambros at UIUC and me]; so, our kick-off meeting should have involved at least two of us travelling to the laboratory of the third collaborator and spending some time brainstorming about the challenges that we have agreed to tackle over the next three years. Instead we had a call via Skype and a rather procedural meeting in which we covered all of the issues without really engendering any excitement or sparking any new ideas. It would appear that we need the stimulus of new environments to maximise our creativity and that we use body language as well as facial expressions to help us reach a friendly consensus on which crazy ideas are worth pursuing and which should be quietly forgotten.
Our new research project has a long title: ‘Thermoacoustic response of Additively Manufactured metals: A multi-scale study from grain to component scales‘. In simple terms, we are going to look at whether residual stresses could be designed to be beneficial to the performance of structural parts used in demanding environments such as those found in reusable spacecraft, hypersonic flight vehicles and breeder blankets in fusion reactors. Residual stresses are often induced during the manufacture of parts and are usually detrimental to the performance of the part. Our hypothesis is that in additive manufacturing, or 3D printing, we have sufficient control of the manufacture of the part that we can introduce ‘designer stresses’ which will improve the part’s performance in demanding environments. The 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 and is supported by The MTC and Renishaw plc; you can find out more at Grants on the Web. The research will be building on our recent research on ‘Potential dynamic buckling in hypersonic vehicle skin‘ [posted July 1st, 2020] and earlier work, see ‘Hot stuff‘ on September 13th, 2012. While the demanding environment is not new to us, we will be using 3D printed parts for the first time instead of components made by conventional (subtractive) machining and taking them to higher temperatures.
Tacit knowledge is traditionally defined as knowledge that is not explicit or that is difficult to express or transfer from someone else. This description of what it is not makes the definition itself tacit knowledge which is not very helpful. Management guides resolve this by giving examples, such as aesthetic sense, or innovation and leadership skills which are elusive skills that are hard to explain [see ‘Innovation out of chaos‘ on June 29th 2016 and ‘Clueless on leadership style‘ on June 14th, 2017]. In engineering, there are a series of skills that are hard to explain or teach, including creative problem-solving [see ‘Learning problem-solving skills‘ on October 24th, 2018], artful design [see ‘Skilled in ingenuity‘ on August 19th, 2015] and elegant modelling [see ‘Credibility is in the eye of the beholder‘ on April 20th, 2016]. In a university course we attempt to lay the foundations for this tacit engineering knowledge; however, much of it is gained in work through experience and becomes regarded by organisations as part of their intellectual assets – the core of their competitiveness and source of their sustainable technology advantage. In our work on integrated nuclear digital environments, from which digital twins can be spawned, we would like to capture both explicit and tacit knowledge about complex systems throughout their life cycle which will extend beyond the working lives of their designers, builders and operators. One of the potential advantages of digital twins is as a knowledge management system by duplicating the life of the physical system and thus allowing its safer and cheaper operation in the long-term as well as its eventual decommissioning. However, besides the very nature of tacit knowledge that makes its capture difficult, we are finding that its perceived value as an intellectual asset renders stakeholders reluctant to discuss it with us; never mind consider how it might be preserved as part of a digital twin. Research has shown that tacit knowledge sharing is influenced by environmental factors including national culture, leadership characteristics and social networks [Cai et al, 2020]. I suspect that all of these factors were present in the heyday of the UK civil nuclear power industry when it worked together to construct advanced and complex systems; however, it has not built a power station since 1995 and, at the moment, new power stations are cancelled more often than built, which has almost certainly depressed all of these factors. So, perhaps we should not be surprised by the difficulties encountered in establishing an integrated nuclear digital environment despite its importance for the future of the industry.
Want to know how to assess the quality of predictions of structural deformation from a computational model and how to diagnose the causes of differences between measurements and predictions? The MOTIVATE project has the answers; that might seem like an over-assertive claim but read on and make your own judgment. Eighteen months ago, I reported on a new method for quantifying the uncertainty present in measurements of deformation made in an industrial environment [see ‘Industrial uncertainty’ on December 12th, 2018] that we were trialling on a 1 m square panel of an aircraft fuselage. Recently, we have used the measurement uncertainty we found to make judgments about the quality of predictions from computer models of the panel under compressive loading. The top graphic shows the outside surface of the panel (left) with a speckle pattern to allow measurements of its deformation using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014 for a brief explanation of DIC]; and the inside surface (right) with stringers and ribs. The bottom graphic shows our results for two load cases: a 50 kN compression (top row) and a 50 kN compression and 1 degree of torsion (bottom row). The left column shows the out-of-plane deformation measured using a stereoscopic DIC system and the middle row shows the corresponding predictions from a computational model using finite element analysis [see ‘Did cubism inspire engineering analysis?’ on January 25th, 2017]. We have described these deformation fields in a reduced form using feature vectors by applying image decomposition [see ‘Recognizing strain’ on October 28th, 2015 for a brief explanation of image decomposition]. The elements of the feature vectors are known as shape descriptors and corresponding pairs of them, from the measurements and predictions, are plotted in the graphs on the right in the bottom graphic for each load case. If the predictions were in perfect agreement with measurements then all of the points on these graphs would lie on the line equality [y=x] which is the solid line on each graph. However, perfect agreement is unobtainable because there will always be uncertainty present; so, the question arises, how much deviation from the solid line is acceptable? One answer is that the deviation should be less than the uncertainty present in the measurements that we evaluated with our new method and is shown by the dashed lines. Hence, when all of the points fall inside the dashed lines then the predictions are at least as good as the measurements. If some points lie outside of the dashed lines, then we can look at the form of the corresponding shape descriptors to start diagnosing why we have significant differences between our model and experiment. The forms of these outlying shape descriptors are shown as insets on the plots. However, busy, or non-technical decision-makers are often not interested in this level of detailed analysis and instead just want to know how good the predictions are. To answer this question, we have implemented a validation metric (VM) that we developed [see ‘Million to one’ on November 21st, 2018] which allows us to state the probability that the predictions and measurements are from the same population given the known uncertainty in the measurements – these probabilities are shown in the black boxes superimposed on the graphs.
These novel methods create a toolbox for alleviating uncertainty about predictions of structural behaviour in industrial contexts. Please get in touch if you want more information in order to test these tools yourself.
The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.
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.