Category Archives: FACTS

Hierarchical modelling in engineering and biology

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.

Slow moving nanoparticles

Random track of a nanoparticle superimposed on its image generated in the microscope using a pin-hole and narrowband filter.

A couple of weeks ago I bragged about research from my group being included in a press release from the Royal Society [see post entitled ‘Press Release!‘ on November 15th, 2017].  I hate to be boring but it’s happened again.  Some research that we have been performing with the European Union’s Joint Research Centre in Ispra [see my post entitled ‘Toxic nanoparticles‘ on November 13th, 2013] has been published this morning by the Royal Society Open Science.

Our experimental measurements of the free motion of small nanoparticles in a fluid have shown that they move slower than expected.  At low concentrations, unexpectedly large groups of molecules in the form of nanoparticles up to 150-300nm in diameter behave more like an individual molecule than a particle.  Our experiments support predictions from computer simulations by other researchers, which suggest that at low concentrations the motion of small nanoparticles in a fluid might be dominated by van der Waals forces rather the thermal motion of the surrounding molecules.  At the nanoscale there is still much that we do not understand and so these findings will have potential implications for predicting nanoparticle transport, for instance in drug delivery [e.g., via the nasal passage to the central nervous system], and for understanding enhanced heat transfer in nanofluids, which is important in designing systems such as cooling for electronics, solar collectors and nuclear reactors.

Our article’s title is ‘Transition from fractional to classical Stokes-Einstein behaviour in simple fluids‘ which does not reveal much unless you are familiar with the behaviour of particles and molecules.  So, here’s a quick explanation: Robert Brown gave his name to the motion of particles suspended in a fluid after reporting the random motion or diffusion of pollen particles in water in 1828.  In 1906, Einstein postulated that the motion of a suspended particle is generated by the thermal motion of the surrounding fluid molecules.  While Stokes law relates the drag force on the particle to its size and fluid viscosity.  Hence, the Brownian motion of a particle can be described by the combined Stokes-Einstein relationship.  However, at the molecular scale, the motion of individual molecules in a fluid is dominated by van der Waals forces, which results in the size of the molecule being unimportant and the diffusion of the molecule being inversely proportional to a fractional power of the fluid viscosity; hence the term fractional Stokes-Einstein behaviour.  Nanoparticles that approach the size of large molecules are not visible in an optical microscope and so we have tracked them using a special technique based on imaging their shadow [see my post ‘Seeing the invisible‘ on October 29th, 2014].


Coglitore D, Edwardson SP, Macko P, Patterson EA, Whelan MP, Transition from fractional to classical Stokes-Einstein behaviour in simple fluids, Royal Society Open Science, 4:170507, 2017. doi:

Instructive Update

Six months ago I wrote about our EU research project, called INSTRUCTIVE, and the likely consequences of Brexit for research [see my post: ‘Instructive report and Brexit‘ on March 29th, 2017].  We seem to be no closer to knowing the repercussions of Brexit on research in the UK and EU – a quarter of EU funding allocated to universities goes to UK universities so the potential impacts will hit both the UK and EU.  Some researchers take every opportunity to highlight these risks and the economic benefits of EU research; for instance the previous EU research programme, Framework Programme 7, is estimated to have created 900,000 jobs in Europe and increased GDP by about 1% in perpetuity.  However, most researchers are quietly getting on with their research and hoping that our political leaders will eventually arrive at a solution that safeguards our prosperity and security.  Our INSTRUCTIVE team is no exception to this approach.  We are about half-way through our project and delivered our first public presentation of our work at the International Conference on Advances in Experimental Mechanics last month.  We described how we are able to identify cracks in metallic structures before they are long enough to be visible to the naked eye, or any other inspection technique commonly used for aircraft structures.  We identify the cracks using an infra-red camera by detecting the energy released during the formation and accumulation of dislocations in the atomic structure that coalesce into voids and eventually into cracks [see my post entitled ‘Alan Arnold Griffith‘ on April 26th, 2017 for more on energy release during crack formation].  We can identify cracks at sub-millimetre lengths and then track them as they propagate through a structure.  At the moment, we are quantifying our ability to detect cracks forming underneath the heads of fasteners [see picture] and other features in real aerospace structures; so that we can move our technology out of the laboratory and into an industrial environment.  We have a big chunk of airplane sitting in the laboratory that we will use for future tests – more on that in later blog posts!

INSTRUCTIVE is an EU Horizon 2020 project funded under the Clean Sky 2 programme [project no. 686777] and involves Strain Solutions Ltd and the University of Liverpool working with Airbus.

Statistics on funding from

For other posts on similar research topics, see ‘Counting photons to measure stress‘ on November 18th, 2015 and ‘Forensic engineering‘ on July 22nd, 2015.

Getting smarter

A350 XWB passes Maximum Wing Bending test [from:

Garbage in, garbage out (GIGO) is a perennial problem in computational simulations of engineering structures.  If the description of the geometry of the structure, the material behaviour, the loading conditions or the boundary conditions are incorrect (garbage in), then the simulation generates predictions that are wrong (garbage out), or least an unreliable representation of reality.  It is not easy to describe precisely the geometry, material, loading and environment of a complex structure, such as an aircraft or a powerstation; because, the complete description is either unavailable or too complicated.  Hence, modellers make assumptions about the unknown information and, or to simplify the description.  This means the predictions from the simulation have to be tested against reality in order to establish confidence in them – a process known as model validation [see my post entitled ‘Model validation‘ on September 18th, 2012].

It is good practice to design experiments specifically to generate data for model validation but it is expensive, especially when your structure is a huge passenger aircraft.  So naturally, you would like to extract as much information from each experiment as possible and to perform as few experiments as possible, whilst both ensuring predictions are reliable and providing confidence in them.  In other words, you have to be very smart about designing and conducting the experiments as well as performing the validation process.

Together with researchers at Empa in Zurich, the Industrial Systems Institute of the Athena Research Centre in Athens and Dantec Dynamics in Ulm, I am embarking on a new EU Horizon 2020 project to try and make us smarter about experiments and validation.  The project, known as MOTIVATE [Matrix Optimization for Testing by Interaction of Virtual and Test Environments (Grant Nr. 754660)], is funded through the Clean Sky 2 Joint Undertaking with Airbus acting as our topic manager to guide us towards an outcome that will be applicable in industry.  We held our kick-off meeting in Liverpool last week, which is why it is uppermost in my mind at the moment.  We have 36-months to get smarter on an industrial scale and demonstrate it in a full-scale test on an aircraft structure.  So, some sleepness nights ahead…



ASME V&V 10-2006, Guide for verification & validation in computational solid mechanics, American Society of Mech. Engineers, New York, 2006.

European Committee for Standardisation (CEN), Validation of computational solid mechanics models, CEN Workshop Agreement, CWA 16799:2014 E.

Hack E & Lampeas G (Guest Editors) & Patterson EA (Editor), Special issue on advances in validation of computational mechanics models, J. Strain Analysis, 51 (1), 2016.