Category Archives: design

Alleviating industrial uncertainty

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.

Reinforcement ensures long-term structural integrity

Last month when I was in Taiwan [see ‘Ancient Standards‘ on January 29th, 2020] , I visited Kuosheng Nuclear Power Plant which has a pair of boiling water reactors that each generate 986 MWe, or between them about 7% of Taiwan’s electricity.  The power station is approaching the end of its licensed life in around 2023 after being constructed in 1978 and delivering electricity commercially for about 40 years, since the early 1980’s.  There is an excellent exhibition centre at the power station that includes the life-size mock-up of the reinforcement rods in the concrete of the reactors shown in the photograph.  I am used to seeing reinforcing bar, or rebar as it is known, between 6 to 12mm in diameter on building site, but I had never seen any of this diameter (about 40 to 50mm diameter) or in such a dense grid.  On the other hand, we are not building any nuclear power stations in the UK at the moment so there aren’t many opportunities to see closeup the scale of structure required.

When seeing nothing is a success

In November I went to Zurich twice: once for the workshop that I wrote about last week [see ‘Fake facts and untrustworthy predictions’ on December 4th, 2019]; and, a second time for a progress meeting of the DIMES project [see ‘Finding DIMES’ on February 6th, 2019].  The progress meeting went well.  The project is on schedule and within budget. So, everyone is happy and you are wondering why I am writing about it.  It was what our team was doing around the progress meeting that was exciting.  A few months ago, Airbus delivered a section of an A320 wing to the labs of EMPA who are our project partner in Switzerland, and the team at EMPA has been rigging the wing section for a simple bending test so that we can use it to test the integrated measurement system which we are developing in the DIMES project [see ‘Joining the dots’ on July 10th, 2019].  Before and after the meeting, partners from EMPA, Dantec Dynamics GmbH, Strain Solutions Ltd and my group at the University of Liverpool were installing our prototype systems to monitor the condition of the wing when we apply bending loads to it.  There is some pre-existing damage in the wing that we hope will propagate during the test allowing us to track it with our prototype systems using visible and infra-red spectrum cameras as well as electrical and optical sensors.  The data that we collect during the test will allow us to develop our data processing algorithms and, if necessary, refine the system design.  The final stage of the DIMES project will involve installing a series of our systems in a complete wing undergoing a structural test in the new Airbus Wing Integration Centre (AWIC) in Filton, near Bristol in the UK.  The schedule is ambitious because we will need to install the sensors for our systems in the wing in the first quarter of next year, probably before we have finished all of the tests in EMPA.  However, the test in Bristol probably will not start until the middle of 2020, by which time we will have refined our algorithm for data processing and be ready for the deluge of data that we are likely to receive from the test at Airbus.  The difference between the two wing tests besides the level of maturity of our measurement system, is that no damage should be detected in the wing at Airbus whereas there will be detectable damage in the wing section in EMPA.  So, a positive result will be a success at EMPA but a negative result, i.e. no damage detected, will be a success at Airbus.

The DIMES 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. 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.

Thought leadership in fusion engineering

The harnessing of fusion energy has become something of a holy grail – sought after by many without much apparent progress.  It is the energy process that ‘powers’ the stars and if we could reproduce it on earth in a controlled environment then it would offer almost unlimited energy with very low environmental costs.  However, understanding the science is an enormous challenge and the engineering task to design, build and operate a fusion-fuelled power station is even greater.  The engineering difficulties originate from the combination of two factors: the emergent behaviour present in the complex system and that it has never been done before.  Engineering has achieved lots of firsts but usually through incremental development; however, with fusion energy it would appear that it will only work when all of the required conditions are present.  In other words, incremental development is not viable and we need everything ready before flicking the switch.  Not surprisingly, engineers are cautious about flicking switches when they are not sure what will happen.  Yet, the potential benefits of getting it right are huge; so, we would really like to do it.  Hence, the holy grail status: much sought after and offering infinite abundance.

Last week I joined the search, or at least offered guidance to those searching, by publishing an article in Royal Society Open Science on ‘An integrated digital framework for the design, build and operation of fusion power plants‘.  Working with colleagues at the Culham Centre for Fusion Energy, Richard Taylor and I have taken our earlier work on an integrated nuclear digital environment for the nuclear energy using fission [see ‘Enabling or disruptive technology for nuclear engineering?‘ on january 28th, 2015] and combined it with the hierarchical pyramid of testing and simulation used in the aerospace industry [see ‘Hierarchical modelling in engineering and biology‘ on March 14th, 2018] to create a framework that can be used to guide the exploration of large design domains using computational models within a distributed and collaborative community of engineers and scientists.  We hope it will shorten development times, reduce design and build costs, and improve credibility, operability, reliability and safety.  It is a long list of potential benefits for a relatively simple idea in a relatively short paper (only 12 pages).  Follow the link to find out more – it is an open access paper, so it’s free.

References

Patterson EA, Taylor RJ & Bankhead M, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016.

Patterson EA, Purdie S, Taylor RJ & Waldon C, An integrated digital framework for the design, build and operation of fusion power plants, Royal Society Open Science, 6(10):181847, 2019.