Category Archives: structures

Nuclear winter school

I spent the first full-week of January 2019 at a Winter School for a pair of Centres for Doctoral Training focussed on Nuclear Energy (see NGN CDT & ICO CDT).  Together the two centres involve eight UK universities and most of the key players in the UK industry.  So, the Winter School offers an opportunity for researchers in nuclear science and engineering, from academia and industry, to gather together for a week and share their knowledge and experience with more than 80 PhD students.  Each student gives a report on the progress of their research to the whole gathering as either a short oral presentation or a poster.  It’s an exhausting but stimulating week for everyone due to both the packed programmme and the range of subjects covered from fundamental science through to large-scale engineering and socio-economic issues.

Here are a few things that caught my eye:

First, the images in the thumbnail above which Paul Cosgrove from the University of Cambridge used to introduce his talk on modelling thermal and neutron fluxes.  They could be from an art gallery but actually they are from the VTT Technical Research Centre of Finland and show the geometry of an advanced test reactor [ATR] (top); the rate of collisions in the ATR (middle); and the neutron density distribution (bottom).

Second, a great app for your phone called electricityMap that shows you a live map of global carbon emissions and when you click on a country it reveals the sources of electricity by type, i.e. nuclear, gas, wind etc, as well as imports and exports of electricity.  Dame Sue Ion told us about it during her key-note lecture.  I think all politicians and journalists need it installed on their phones to check their facts before they start talking about energy policy.

Third, the scale of the concrete infrastructure required in current designs of nuclear power stations compared to the reactor vessel where the energy is generated.  The pictures show the construction site for the Vogtle nuclear power station in Georgia, USA (left) and the reactor pressure vessel being lowered into position (right).  The scale of nuclear power stations was one of the reasons highlighted by Steve Smith from Algometrics for why investors are not showing much interest in them (see ‘Small is beautiful and affordable in nuclear power-stations‘ on January 14th, 2015).  Amongst the other reasons are: too expensive (about £25 billion), too long to build (often decades), too back-end loaded (i.e. no revenue until complete), too complicated (legally, economically & socially), too uncertain politically, too toxic due to poor track record of returns to investors, too opaque in terms of management of industry.  That’s quite a few challenges for the next generation of nuclear scientists and engineers to tackle.  We are making a start by creating design tools that will enable mass-production of nuclear power stations (see ‘Enabling or disruptive technology for nuclear engineering?‘ on January 28th, 2015) following the processes used to produce other massive engineering structures, such as the Airbus A380 (see Integrated Digital Nuclear Design Programme); but the nuclear industry has to move fast to catch up with other sectors of the energy business, such as gas-fired powerstations or wind turbines.  If it were to succeed then the energy market would be massively transformed.

 

INSTRUCTIVE final reckoning

Our EU project, INSTRUCTIVE came to an end with the closing of 2018.  We have achieved all of our milestones and deliverables; and, now have 51 (=60-9) days to submit our final reports.  We have already presented the technical contents of those reports to representatives of our sponsors in a final review meeting just before the Christmas break.  I think that they were pleased with our progress; our findings certainly stimulated debate about how to move forward and implement the new technologies – lots of new questions that we did not know we should be asking when we started the project.

We are also disseminating the key results more publicly because this is an obligation inherent with receiving public funding for our research; but also, because I see no purpose in advancing knowledge without sharing it. During the course of the project we have given research updates at three conferences and the papers/abstracts for these are available via the University of Liverpool Repository [#1, #2 & #3].  And, we are in the process of producing three papers for publication in archived journals.

However, the real tangible benefit of the project is the move to next stage of development for the technology supported by a new project, called DIMES, that started on January 1st, 2019.  The aim of the DIMES project is to develop and demonstrate systems with the capability to detect a crack or delamination in a metallic or composite structure, and the potential to be deployed as part of an on-board structural health monitoring system for passenger aircraft.  In other words, the INSTRUCTIVE project has successfully demonstrated that a new philosophy for monitoring damage in aerospace structures, using disturbances to the strain field caused by the damage, is at least as effective as traditional non-destructive evaluation (NDE) techniques and in some circumstances provides much more sensitivity about the initiation and propagation of damage.  This has been sufficiently successful in the laboratory and on aircraft components in an industrial environment that is worth exploring its deployment for on-board monitoring and the first step is to use it in ground-based tests.

There will be more on DIMES as the project gets underway and updates on its progress will replace the twice-yearly ones on INSTRUCTIVE.

The series of posts on the INSTRUCTIVE project can be found at https://realizeengineering.blog/category/myresearch/instructive-project/

instructive acknowledgement

Industrial uncertainty

Last month I spent almost a week in Zurich.  It is one of our favourite European cities [see ‘A reflection of existentialism‘ on December 20th, 2017]; however, on this occasion there was no time for sight-seeing because I was there for the mid-term meeting of the MOTIVATE project and to conduct some tests and demonstrations in the laboratories of our host, EMPA, the Swiss Federal Laboratories for Materials Science and Technology.  Two of our project partners, Dantec Dynamics GmbH based in Ulm, Germany, and the Athena Research Centre in Patras, Greece, have developed methods for quantifying the uncertainty present in measurements of deformation made in an industrial environment using digital image correlation (DIC) [see ‘256 shades of grey‘ on January 22, 2014].  Digital image correlation is a technique in which we usually apply a random speckle pattern to the object which allows us to track the movement of the object surface over time by searching for the new position of the speckles in the photographs of the object.  If we use a pair of cameras in a stereoscopic arrangement, then we can measure in-plane and out-of-plane displacements.  Digital image correlation is a well-established measurement technique that has become ubiquitous in mechanics laboratories. In previous EU projects, we have developed technology for quantifying uncertainty in in-plane [SPOTS project] and out-of-plane [ADVISE project] measurements in a laboratory environment.  However, when you take the digital image correlation equipment into an industrial environment, for instance an aircraft hangar to make measurements during a full-scale test, then additional sources of uncertainty and error appear. The new technology demonstrated last month allows these additional uncertainties to be quantified.  As part of the MOTIVATE project, we will be involved in a full-scale test on a large section of an Airbus aircraft next year and so, we will be able to utilise the new technology for the first time.

The photograph shows preparations for the demonstrations in EMPA’s laboratories.  In the foreground is a stereoscopic digital image correlation system with which we are about to make measurements of deformation of a section of aircraft skin, supplied by Airbus, which has a speckle pattern on its surface and is about to be loaded in compression by the large servo-hydraulic test machine.

References:

From SPOTS project:

Patterson EA, Hack E, Brailly P, Burguete RL, Saleem Q, Seibert T, Tomlinson RA & Whelan M, Calibration and evaluation of optical systems for full-field strain measurement, Optics and Lasers in Engineering, 45(5):550-564, 2007.

Whelan MP, Albrecht D, Hack E & Patterson EA, Calibration of a speckle interferometry full-field strain measurement system, Strain, 44(2):180-190, 2008.

From ADVISE project:

Hack E, Lin X, Patterson EA & Sebastian CM, A reference material for establishing uncertainties in full-field displacement measurements, Measurement Science and Technology, 26:075004, 2015.

Million to one

‘All models are wrong, but some are useful’ is a quote, usually attributed to George Box, that is often cited in the context of computer models and simulations.  Working out which models are useful can be difficult and it is essential to get it right when a model is to be used to design an aircraft, support the safety case for a nuclear power station or inform regulatory risk assessment on a new chemical.  One way to identify a useful model to assess its predictions against measurements made in the real-world [see ‘Model validation’ on September 18th, 2012].  Many people have worked on validation metrics that allow predicted and measured signals to be compared; and, some result in a statement of the probability that the predicted and measured signal belong to the same population.  This works well if the predictions and measurements are, for example, the temperature measured at a single weather station over a period of time; however, these validation metrics cannot handle fields of data, for instance the map of temperature, measured with an infrared camera, in a power station during start-up.  We have been working on resolving this issue and we have recently published a paper on ‘A probabilistic metric for the validation of computational models’.  We reduce the dimensionality of a field of data, represented by values in a matrix, to a vector using orthogonal decomposition [see ‘Recognizing strain’ on October 28th, 2015].  The data field could be a map of temperature, the strain field in an aircraft wing or the topology of a landscape – it does not matter.  The decomposition is performed separately and identically on the predicted and measured data fields to create to two vectors – one each for the predictions and measurements.  We look at the differences in these two vectors and compare them against the uncertainty in the measurements to arrive at a probability that the predictions belong to the same population as the measurements.  There are subtleties in the process that I have omitted but essentially, we can take two data fields composed of millions of values and arrive at a single number to describe the usefulness of the model’s predictions.

Our paper was published by the Royal Society with a press release but in the same week as the proposed Brexit agreement and so I would like to think that it was ignored due to the overwhelming interest in the political storm around Brexit rather than its esoteric nature.

Source:

Dvurecenska K, Graham S, Patelli E & Patterson EA, A probabilistic metric for the validation of computational models, Royal Society Open Science, 5:1180687, 2018.