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.
The harnessing of fusion energy has become something of a holy grail – sought after by many without much apparent progress. It is the energy process that ‘powers’ the stars and if we could reproduce it on earth in a controlled environment then it would offer almost unlimited energy with very low environmental costs. However, understanding the science is an enormous challenge and the engineering task to design, build and operate a fusion-fuelled power station is even greater. The engineering difficulties originate from the combination of two factors: the emergent behaviour present in the complex system and that it has never been done before. Engineering has achieved lots of firsts but usually through incremental development; however, with fusion energy it would appear that it will only work when all of the required conditions are present. In other words, incremental development is not viable and we need everything ready before flicking the switch. Not surprisingly, engineers are cautious about flicking switches when they are not sure what will happen. Yet, the potential benefits of getting it right are huge; so, we would really like to do it. Hence, the holy grail status: much sought after and offering infinite abundance.
Last week I joined the search, or at least offered guidance to those searching, by publishing an article in Royal Society Open Science on ‘An integrated digital framework for the design, build and operation of fusion power plants‘. Working with colleagues at the Culham Centre for Fusion Energy, Richard Taylor and I have taken our earlier work on an integrated nuclear digital environment for the nuclear energy using fission [see ‘Enabling or disruptive technology for nuclear engineering?‘ on january 28th, 2015] and combined it with the hierarchical pyramid of testing and simulation used in the aerospace industry [see ‘Hierarchical modelling in engineering and biology‘ on March 14th, 2018] to create a framework that can be used to guide the exploration of large design domains using computational models within a distributed and collaborative community of engineers and scientists. We hope it will shorten development times, reduce design and build costs, and improve credibility, operability, reliability and safety. It is a long list of potential benefits for a relatively simple idea in a relatively short paper (only 12 pages). Follow the link to find out more – it is an open access paper, so it’s free.
Patterson EA, Taylor RJ & Bankhead M, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016.
Patterson EA, Purdie S, Taylor RJ & Waldon C, An integrated digital framework for the design, build and operation of fusion power plants, Royal Society Open Science, 6(10):181847, 2019.
In the 1979 Glenn Harris proposed an analytical hierarchy of models for estimating tactical force effectiveness for the US Army which was represented as a pyramid with four layers with a theatre/campaign simulation at the apex supported by mission level simulations below which was engagement model and engineering models of assets/equipment at the base. The idea was adopted by the aerospace industry [see the graphic on the left] who place the complete aircraft on the apex supported by systems, sub-systems and components beneath in increasing numbers with the pyramid divided vertically in half to represent physical tests on one side and simulations on the other. This represents the need to validate predictions from computational models with measurements in the real-world [see post on ‘Model validation‘ on September 18th, 2012]. These diagrams are schematic representations used by engineers to plan and organise the extensive programmes of modelling and physical testing undertaken during the design of new aircraft [see post on ‘Models as fables‘ on March 16th, 2016]. The objective of the MOTIVATE research project is to reduce quantity and increase the quality of the physical tests so that pyramid becomes lop-sided, i.e. the triangle representing the experiments and tests is a much thinner slice than the one representing the modelling and simulations [see post on ‘Brave New World‘ on January 10th, 2018].
At the same time, I am working with colleagues in toxicology on approaches to establishing credibility in predictive models for chemical risk assessment. I have constructed an equivalent pyramid to represent the system hierarchy which is shown on the right in the graphic. The challenge is the lack of measurement data in the top left of the pyramid, for both moral and legal reasons, which means that there is very limited real-world data available to confirm the predictions from computational models represented on the right of the pyramid. In other words, my colleagues in toxicology, and computational biology in general, are where my collaborators in the aerospace industry would like to be while my collaborators in the aerospace want to be where the computational biologists find themselves already. The challenge is that in both cases a paradigm shift is required from objectivism toward relativism; since, in the absence of comprehensive real-world measurement data, validation or confirmation of predictions becomes a social process involving judgement about where the predictions lie on a continuum of usefulness.
Harris GL, Computer models, laboratory simulators, and test ranges: meeting the challenge of estimating tactical force effectiveness in the 1980’s, US Army Command and General Staff College, May 1979.
Trevisani DA & Sisti AF, Air Force hierarchy of models: a look inside the great pyramid, Proc. SPIE 4026, Enabling Technology for Simulation Science IV, 23 June 2000.
Patterson EA & Whelan MP, A framework to establish credibility of computational models in biology, Progress in Biophysics and Molecular Biology, 129:13-19, 2017.