Tag Archives: nuclear energy

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!

Mapping atoms

Typical atom maps of P, Cu, Mn, Ni & Si (clockwise from bottom centre) in 65x65x142 nm sample of steel from Styman et al, 2015.

A couple of weeks ago I wrote about the opening plenary talk at the NNL Sci-Tec conference [‘The disrupting benefit of innovation’ on May 23rd, 2018].  One of the innovations discussed at the conference was the applications of atom probe tomography for understanding the mechanisms underpinning material behaviour.  Atom probe tomography produces three-dimensional maps of the location and type of individual atoms in a sample of material.  It is a destructive technique that uses a high energy pulse to induce field evaporation of ions from the tip of a needle-like sample.  A detector senses the position of the ions and their chemical identity is found using a mass spectrometer.  Only small samples can be examined, typically of the order of 100nm.

A group led by Jonathan Hyde at NNL have been exploring the use of atom probe tomography to understand the post-irradiation annealing of weld material in reactor pressure vessels and to examine the formation of bubbles of rare gases in fuel cladding which trap hydrogen causing material embrittlement.  A set of typical three-dimensional maps of atoms is shown in the thumb-nail from a recent paper by the group (follow the link for the original image).

It is amazing that we can map the location of atoms within a material and we are just beginning to appreciate the potential applications of this capability.  As another presenter at the conference said: ‘Big journeys begin with Iittle steps’.

BTW it was rewarding to see one of our alumni from our CPD course [see ‘Leadership is like shepherding’ on May 10th, 2017] presenting this work at the conference.


Styman PD, Hyde JM, Parfitt D, Wilford K, Burke MG, English CA & Efsing P, Post-irradiation annealing of Ni-Mn-Si-enriched clusters in a neutron-irradiated RPV steel weld using atom probe tomography, J. Nuclear Materials, 459:127-134, 2015.

The disrupting benefit of innovation

Most scientific and technical conferences include plenary speeches that are intended to set the agenda and to inspire conference delegates to think, innovate and collaborate.  Andrew Sherry, the Chief Scientist of the UK National Nuclear Laboratory (NNL) delivered a superb example last week at the NNL SciTec 2018 which was held at the Exhibition Centre Liverpool on the waterfront.  With his permission, I have stolen his title and one of his illustrations for this post.  He used a classic 2×2 matrix to illustrate different types of change: creative change in the newspaper industry that has constantly redeveloped its assets from manual type-setting and printing to on-line delivery via your phone or tablet; progressive change in the airline industry that has incrementally tested and adapted so that modern commercial aircraft look superficially the same as the first jet airliner but represent huge advances in economy and reliability; inventive change in Liverpool’s Albert Dock that was made redundant by container ships but has been reinvented as a residential, tourism and business district.  The fourth quadrant, he reserved for the civil nuclear industry in the UK which requires disruptive change because its core assets are threatened by the end-of-life closure of all existing plants and because its core activity, supplying electrical power, is threatened by cheaper alternatives.

At the end of last year, NNL brought together all the prime nuclear organisations in the UK with leaders from other sectors, including aerospace, construction, digital, medical, rail, robotics, satellite and ship building at the Royal Academy of Engineering to discuss the drivers of innovation.  They concluded that innovation is not just about technology, but that successful innovation is driven by five mutually dependent themes that are underpinned by enabling regulation:

  1. innovative technologies;
  2. culture & leadership;
  3. collaboration & supply chain;
  4. programme and risk management; and
  5. financing & commercial models.

SciTec’s focus was ‘Innovation through Collaboration’, i.e. tackling two of these themes, and Andrew tasked delegates to look outside their immediate circle for ideas, input and solutions [to the existential threats facing the nuclear industry] – my words in parentheses.

Innovative technology presents a potentially disruptive threat to all established activities and we ignore it at our peril.  Andrew’s speech was wake up call to an industry that has been innovating at an incremental scale and largely ignoring the disruptive potential of innovation.  Are you part of a similar industry?  Maybe it’s time to check out the threats to your industry’s assets and activities…


Sherry AH, The disruptive benefit of innovation, NNL SciTec 2018 (including the graphic & title).

McGahan AM, How industries change, HBR, October 2004.