Tag Archives: CDT

Webs of expertise and knowledge

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I am writing this post while I am in the middle of leading a breakout activity for more than a hundred PhD students from our Centres for Doctoral Training in nuclear science and engineering, GREEN and SATURN.  We have asked them to construct a knowledge network for a start-up company commissioned to build either a fusion energy power station or a power station based on small modular reactors (SMRs).  A knowledge network is a web of expertise and information whose value comes from the connections and interactions within and outside an organization.  Our aim is to encourage students to think beyond science and engineering and consider the interactions required to deliver safe, economic nuclear power.

We have brought the students together in York from six universities located in the North of England (Lancaster, Leeds, Liverpool, Manchester & Sheffield) and Scotland (Strathclyde).  This is an annual event usually held in the first working week of the New Year (see ‘Nuclear Winter School’ on January 23rd 2019).

The breakout activity has three one-hour time-slots on three consecutive days.  In today’s time-slot, we have divided the students into twenty groups of seven and given them paper, pencils, and a circle stencil plus an eraser with which to draw knowledge networks.  We are hoping for creativity, lively discussions, and some fun.  In yesterday’s one-hour slot, they had briefings from the Chief Manufacturing Engineer for a company building SMRs and the Deputy Chief Engineer of a company developing a fusion power station, as well as from a Digital Knowledge Management Consultant whose PhD led to a paper on knowledge networks, which we shared with the students last month (see ‘Evolutionary model of knowledge management’ on March 6th, 2024).  Tomorrow, one person from each group will have two minutes to present their knowledge network, via a portable visualiser, to an audience of several hundred.  What can go wrong?  Twenty two-minute presentations in one hour with one minute for questions and changeover.

GREEN (Growing skills for Reliable Economic Energy from Nuclear) is co-funded by a consortia of industrial organisations and the UK EPSRC (grant no. EP/S022295/1).

SATURN (Skills And Training Underpinning a Renaissance in Nuclear) succeeded GREEN and is also co-funded by a consortia of industrial organisations and the UK EPSRC (grant no. EP/Y034856/1).

Image shows thumbnail of figure from shared paper with knowledge networks for an engineering consultancy company and an electricity generator, follow this link for full size image.

Reliable predictions of non-Newtonian flows of sludge

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Regular readers of this blog will be aware that I have been working for many years on validation processes for computational models of structures employed in a wide range of sectors, including aerospace engineering [see ‘The blind leading the blind’ on May 27th, 2020] and nuclear energy [see ‘Million to one’ on November 21st, 2018].  Validation is determining the extent to which predictions from a model are representative of behaviour in the real-world [see ‘Model validation’ on September 18th, 2012].  More recently, I have been working on model credibility, which is the willingness of people, besides the modeller, to use the predictions from models in decision-making [see, for example, ‘Credible predictions for regulatory decision-making’ on December 9th, 2020].  I have started to consider the complex world of predictive modelling of fluid flow and I am hoping to start a collaboration with a new colleague on the flow of sludges.  Sludges are more common than you might think but we are interested in modelling the flow of waste, both wastewater (sewage) and nuclear wastes.  We have a PhD studentship available sponsored jointly by the GREEN CDT and the National Nuclear Laboratory.  The project is interdisciplinary in two dimensions because it will combine experiments and simulations as well as uniting ideas from solid mechanics and fluid mechanics.  The integration of concepts and technologies across these boundaries brings a level of adventure to the project which will be countered by building on well-established research in solid mechanics on quantitative comparisons of measurements and predictions and by employing current numerical and experimental work on wastewater sludges.  If you are interested or know someone who might want to join our research then you can find out more here.

Image: Sewage sludge disposal in Germany: Andrea Roskosch / UBA

Same problems in a different language

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I spent a lot of time on trains last week.  I left Liverpool on Tuesday evening for Bristol and spent Wednesday at Airbus in Filton discussing the implementation of the technologies being developed in the EU Clean Sky 2 projects MOTIVATE and DIMES.  On Wednesday evening I travelled to Bracknell and on Thursday gave a seminar at Syngenta on model credibility in predictive toxicology before heading home to Liverpool.  But, on Friday I was on the train again, to Manchester this time, to listen to a group of my PhD students presenting their projects to their peers in our new Centre for Doctoral Training called Growing skills for Reliable Economic Energy from Nuclear, or GREEN.  The common thread, besides the train journeys, is the Fidelity And Credibility of Testing and Simulation (FACTS).  My research group is working on how we demonstrate the fidelity of predictions from models, how we establish trust in both predictions from computational models and measurements from experiments that are often also ‘models’ of the real world.  The issues are similar whether we are considering the structural performance of aircraft [as on Wednesday], the impact of agro-chemicals [as on Thursday], or the performance of fusion energy and the impact of a geological disposal site [as on Friday] (see ‘Hierarchical modelling in engineering and biology‘ on March 14th, 2018) .  The scientific and technical communities associated with each application talk a different language, in the sense that they use different technical jargon and acronyms; and they are surprised and interested to discover that similar problems are being tackled by communities that they rarely think about or encounter.

Digital twins and seeking consensus

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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