Tag Archives: nuclear energy

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.


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.