Tag Archives: simulation

Establishing fidelity and credibility in tests & simulations (FACTS)

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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!

Fourth industrial revolution

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Have you noticed that we are in the throes of a fourth industrial revolution?

The first industrial revolution occurred towards the end of the 18th century with the introduction of steam power and mechanisation.  The second industrial revolution took place at the end of the 19th and beginning of the 20th century and was driven by the invention of electrical devices and mass production.  The third industrial revolution was brought about by computers and automation at the end of the 20th century.  The fourth industrial revolution is happening as result of combining physical and cyber systems.  It is also called Industry 4.0 and is seen as the integration of additive manufacturing, augmented reality, Big Data, cloud computing, cyber security, Internet of Things (IoT), simulation and systems engineering.  Most organisations are struggling with the integration process and, as a consequence, are only exploiting a fraction of the capabilities of the new technology.  Revolutions are, by their nature, disruptive and those organisations that embrace and exploit the innovations will benefit while the existence of the remainder is under threat [see [‘The disrupting benefit of innovation’ on May 23rd, 2018].

Our work on the Integrated Nuclear Digital Environment, on Digital Twins, in the MOTIVATE project and on hierarchical modelling in engineering and biology is all part of the revolution.

Links to these research posts:

‘Enabling or disruptive technology for nuclear engineering?’ on January 28th, 2015

‘Can you trust your digital twin?’ on November 23rd, 2016

‘Getting Smarter’ on June 21st, 2017

‘Hierarchical modelling in engineering and biology’ [March 14th, 2018]

 

Image: Christoph Roser at AllAboutLean.com from https://commons.wikimedia.org/wiki/File:Industry_4.0.png [CC BY-SA 4.0].

Hierarchical modelling in engineering and biology

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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.

Sources:

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.

Brave New World

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OLYMPUS DIGITAL CAMERATerm has started, and our students are preparing for end-of-semester examinations; so, I suspect that they would welcome the opportunity to deploy the sleeping-learning that Aldous Huxley envisaged in his ‘Brave New World’ of 2540.  In the brave new world of digital engineering, some engineers are attempting to conceive of a world in which experiments have become obsolete because we can rely on computational modelling to simulate engineering systems.  This ambitious goal is a driver for the MOTIVATE project [see my post entitled ‘Getting smarter‘ on June 21st, 2017]; an EU-project that kicked-off about six months ago and was the subject of a brainstorming session in the Red Deer in Sheffield last September [see my post entitled ‘Anything other than lager, stout or porter!‘ on September 6th, 2017.  The project has its own website now at www.engineeringvalidation.org

A world without experiments is almost unimaginable for engineers whose education and training is deeply rooted in empiricism, which is the philosophical approach that requires assumptions, models and theories to be tested against observations from the real-world before they can be accepted.  In the MOTIVATE project, we are thinking about ways in which fewer experiments can provide more and better measured data for the validation of computational models of engineering systems.   In December, under the auspices of the project, experts from academia, industry and national labs from across Europe met near Bristol and debated how to reshape the traditional flow-chart used in the validation of engineering models, which places equal weight on experiments and computational models [see ASME V&V 10-2006 Figure 2].  In a smaller follow-up meeting in Zurich, just before Christmas [see my post ‘A reflection of existentialism‘ on December 20th, 2017], we blended the ideas from the Bristol session into a new flow-chart that could lead to the validation of some engineering systems without conducting experiments in parallel.  This is not perhaps as radical as it sounds because this happens already for some evolutionary designs, especially if they are not safety-critical.  Nevertheless, if we are to achieve the paradigm shift towards the new digital world, then we will have to convince the wider engineering community about our novel approach through demonstrations of its successful application, which sounds like empiricism again!  More on that in future updates.

Image by Erwin Hack: Coffee and pastries awaiting technical experts debating behind the closed door.