Category Archives: design

Digital twins and seeking consensus

A couple of weeks ago I wrote about our work on a proof-of-concept for a digital twin of a fission nuclear reactor and its extension to fusion energy [‘Digitally-enabled regulatory environment for fusion power plants‘ on March 20th, 2019].  In parallel with this work and together with a colleague in the Dalton Nuclear Institute, I am supervising a PhD student who is studying the potential role of virtual reality and social network analysis in delivering nuclear infrastructure projects.  In a new PhD project, we are aiming to extend this research to consider the potential provided by an integrated nuclear digital environment [1] in planning the disposal of nuclear waste.  We plan to look at how provision of clear, evidence-based information and in the broader adoption of digital twins to enhance public confidence through better engagement and understanding.  This is timely because the UK’s Radioactive Waste Management (RWM) have launched their new consent-based process for siting a Geological Disposal Facility (GDF). The adoption of a digital environment to facilitate a consent-based process represents a new and unprecedented approach to the GDF or any other nuclear project in the UK. So this will be an challenging and exciting research project requiring an innovative and multi-disciplinary approach involving both engineering and social sciences.

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.

Reference:

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

Image: Artist’s impression of geological disposal facility from https://www.gov.uk/government/news/geological-disposal-understanding-our-work

 

Digitally-enabled regulatory environment for fusion powerplants

Digital twins are a combination of computational models and real-world data describing the form, function and condition of a system [see ‘Can you trust your digital twin?‘ on November 23rd 2016]. They are beginning to transform design processes for complex systems in a number of industries.  We have been working on a proof-of-concept study for a digital reactor in fission energy based on the Integrated Nuclear Digital Environment (INDE) [1].  The research has been conducted by the Virtual Engineering Centre (VEC) at the University of Liverpool together with partners from industry and national laboratories with funding from the UK Government for nuclear innovation.  In parallel, I having been working with a colleague at the University of Manchester and partners at the Culham Centre for Fusion Energy on the form of a digital environment for fusion energy taking account of the higher order of complexity, the scale of resources, the integration of novel technologies, and the likely diversity and distribution of organisations involved in designing, building and operating a fusion powerplant.  We have had positive interactions with the regulatory authorities during the digital fission reactor project and the culture of enabling-regulation [2] offers an opportunity for a new paradigm in the regulation of fusion powerplants.  Hence, we propose in a new PhD project to investigate the potential provided by the integration of digital twins with the regulatory environment to enable innovation in the design of fusion powerplants.

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.

References:

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

[2] http://www.onr.org.uk/documents/2018/guide-to-enabling-regulation-in-practice.pdf

Image: https://www.pexels.com/photo/diagram-drawing-electromagnetic-energy-326394/

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.

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.