Category Archives: FACTS

Joining the dots

Six months ago, I wrote about ‘Finding DIMES’ as we kicked off a new EU-funded project to develop an integrated measurement system for identifying and tracking damage in aircraft structures.  We are already a quarter of the way through the project and we have a concept design for a modular measurement system based on commercial off-the-shelf components.  We started from the position of wanting our system to provide answers to four of the five questions that Farrar & Worden [1] posed for structural health monitoring systems in 2007; and, in addition to provide information to answer the fifth question.  The five questions are: Is there damage? Where is the damage? What kind of damage is present? How severe is the damage?  And, how much useful life remains?

During the last six months our problem definition has evolved through discussions with our EU Topic Manager, Airbus, to four objectives, namely: to quantify applied loads; to provide condition-led/predictive maintenance; to find indications of damage in composites of 6mm diameter or greater and in metal to detect cracks longer than 1mm; and to provide a digital solution.  At first glance there may not appear to be much connection between the initial problem definition and the current version; but actually, they are not very far apart although the current version is more specific.  This evolution from the idealised vision to the practical goal is normal in engineering projects.

We plan to use point sensors, such as resistance strain gauges or fibre Bragg gratings, to quantify applied loads and track usage history; while imaging sensors will allow us to measure strain fields that will provide information about the changing condition of the structure using the image decomposition techniques developed in previous EU-funded projects: ADVISE, VANESSA (see ‘Setting standards‘ on January 29th, 2014) and INSTRUCTIVE.  We will use these techniques to identify and track cracks in metals [2]; while for composites, we will apply a technique developed through an EPSRC iCASE award from 2012-16 on ‘Full-field strain-based methods for NDT & structural integrity measurement’ [3].

I gave a short briefing on DIMES to a group of Airbus engineers last month and it was good see some excitement in the room about the direction of the project.  And, it felt good to be highlighting how we are building on earlier investments in research by joining the dots to create a deployable measurement system and delivering the complete picture in terms of information about the condition of the structure.

Image: Infra red photograph of DIMES meeting in Ulm.


  1. Farrar & Worden, An introduction to structural health monitoring, Phil. Trans. R Soc A, 365:303-315, 2007
  2. Middleton, C.A., Gaio, A., Greene, R.J. & Patterson, E.A., Towards automated tracking of initiation and propagation of cracks in aluminium alloy coupons using thermoelastic stress analysis, Nondestructive Evaluation, 38:18, 2019.
  3. Christian, W.J.R., DiazDelaO, F.A. & Patterson, E.A., Strain-based damage assessment of accurate residual strength prediction of impacted composite laminates, Composites Structures, 184:1215-1223, 2018.

The INSTRUCTIVE and DIMES projects have received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 685777 and No. 820951 respectively.

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.

Crack tip plasticity in reactor steels

Amplitude of temperature in steel due to a cyclic load with a crack growing from left to right along the horizontal centre line with the stress concentration at its tip exhibiting the peak values. The wedge shapes in the left corners are part of the system.

At this time of year the flow into my inbox is augmented daily by prospective PhD students sending me long emails describing how their skills, qualifications and interests perfectly match the needs of my research group, or sometimes someone else’s group if they have not been careful in setting up their mass mailing.  At the moment, I have four PhD projects for which I am looking for outstanding students; so, because it will help prospective students and might interest my other readers but also because I am short of ideas for the blog, I plan to describe one project per week for the next month.

The first project is about the effect of hydrogen on crack tip plasticity in reactor steels.  Fatigue cracks grow in steels by coalescing imperfections in the microstructure of the material until small voids are formed in areas of high stress.  When these voids connect together a crack is formed.  Repeated loading and unloading of the material provides the energy to move the imperfections, known as dislocations, and geometric features in structures are stress concentrators which focus this energy causing cracks to be formed in their vicinity.  The movement of dislocations causes permanent, or plastic deformation of the material.  The sharp geometry of a crack tip becomes a stress concentrator creating a plastic zone in which dislocations pile up and voids form allowing the crack to extend [see post on ‘Alan Arnold Griffith‘ on April 26th, 2017].  It is possible to detect the thermal energy released during plastic deformation using a technique known as thermoelastic stress analysis [see ‘Counting photons to measure stress‘ on November 18th 2015] as well as to measure the stress field associated with the propagating crack [1].  One of my current PhD students has been using this technique to investigate the effect of irradiation damage on the growth of cracks in stainless steel used in nuclear reactors.  We use an ion accelerator at the Dalton Cumbrian Facility to introduce radiation damage into specimens the size of a postage stamp and afterwards apply cyclic loads and watch the fatigue crack grow using our sensitive infra-red cameras.  We have found that the irradiation reduced the rate of crack growth and we will be publishing a paper on it shortly [and a PhD thesis].  In the new project, our industrial sponsors want us to explore the effect of hydrogen on crack growth in irradiated steel, because the presence of hydrogen is known to accelerate fatigue crack growth [2] which is believe to happen as a result of hydrogen atoms disrupting the formation of dislocations at the microscale and localising plasticity at crack tip on the mesoscale.  However, these ideas have not been demonstrated in experiments, so we plan to do this using thermoelastic stress analysis and to investigate the combined influence of hydrogen and irradiation by developing a process for pre-charging the steel specimens with hydrogen using an electrolytic cell and irradiating them using the ion accelerator.  Both hydrogen and radiation are present in a nuclear reactor and hence the results will be relevant to predicting the safe working life of nuclear reactors.

The PhD project is fully-funded for UK and EU citizens as part of a Centre for Doctoral Training and will involve a year of specialist training followed by three years of research.  For more information following this link.


  1. 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.
  2. Matsunaga, H., Takakuwa, O., Yamabe, J., & Matsuoka, S., 2017, Hydrogen-enhanced fatigue crack growth in steels and its frequency dependence. Phil. Trans. R. Soc. A, 375(2098), 20160412

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.


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

Establishing fidelity and credibility in tests & simulations (FACTS)

A month or so ago I gave a lecture entitled ‘Establishing FACTS (Fidelity And Credibility in Tests & Simulations)’ to the local branch of the Institution of Engineering Technology (IET). Of course my title was a play on words because the Oxford English Dictionary defines a ‘fact’ as ‘a thing that is known or proved to be true’ or ‘information used as evidence or as part of report’.   One of my current research interests is how we establish predictions from simulations as evidence that can be used reliably in decision-making.  This is important because simulations based on computational models have become ubiquitous in engineering for, amongst other things, design optimisation and evaluation of structural integrity.   These models need to possess the appropriate level of fidelity and to be credible in the eyes of decision-makers, not just their creators.  Model credibility is usually provided through validation processes using a small number of physical tests that must yield a large quantity of reliable and relevant data [see ‘Getting smarter‘ on June 21st, 2017].  Reliable and relevant data means making measurements with low levels of uncertainty under real-world conditions which is usually challenging.

These topics recur through much of my research and have found applications in aerospace engineering, nuclear engineering and biology. My lecture to the IET gave an overview of these ideas using applications from each of these fields, some of which I have described in past posts.  So, I have now created a new page on this blog with a catalogue of these past posts on the theme of ‘FACTS‘.  Feel free to have a browse!