Category Archives: FACTS

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].

Source:

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 http://russellgroup.ac.uk/news/horizon-2020-latest-statistics/and https://www.russellgroup.ac.uk/media/5068/24horizon-2020-the-contribution-of-russell-group-universities-june-201.pdf

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: http://www.airbus.com/galleries/photo-gallery%5D

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…

Bibliography:

 

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.

http://www.engineeringvalidation.org/

Instructive report and Brexit

Even though this blog is read in more than 100 countries, surely nobody can be unaware of the furore about Brexit – the UK Government’s plan to leave the European Union.  The European Commission has been funding my research for more than twenty years and I am a frequent visitor to their Joint Research Centre in Ispra, Italy.  During the last decade, I have led consortia of industry, national labs and universities that rejoice in names such as SPOTS, VANESSA and, most recently MOTIVATE.  These are acronyms based loosely on the title of the research project.  Currently, there is no sign that these pan-European research programmes will exclude scientists and engineers from the UK, but then the process of leaving the EU has not yet started, so who knows…

At the moment, I am working with a small UK company, Strain Solutions Ltd, on a EU project called INSTRUCTIVE.  I said these were loose acronyms and this one is very loose: Infrared STRUctural monitoring of Cracks using Thermoelastic analysis in production enVironmEnts.  We are working with Airbus in France, Germany, Spain and the UK to transition a technology from the laboratory to the industrial test environment.  Airbus conducts full-scale fatigue tests on airframe structures to ensure that they have the appropriate life-cycle performance and the INSTRUCTIVE project will deliver a new tool for monitoring the development of damage, in the form of cracks, during these tests.  The technology is thermoelastic stress analysis, which is well-established as a laboratory-based technique [1] for structural analysis [2], fracture mechanics [3] and damage mechanics [4], that I described in a post on November 18th, 2015 [see ‘Counting photons to measure stress’].  It’s exciting to be evolving it into an industrial technique but also to be looking at the potential to apply it using cheap infrared cameras instead of the current laboratory instruments that cost tens of thousands of any currency.  It’s a three-year project and we’ve just completed our first year so we should finish before any Brexit consequences!  Anyway, the image gives you a taster and I plan to share more results with you shortly…

BTW – You might get the impression from my recent posts that teaching MOOCs [see ‘Slowing down time to think [about strain energy]’ on March 8th, 2017] and leadership [see ‘Inspirational leadership’ on March 22nd, 2018] were foremost amongst my activities.  I only write about my research occasionally.  This would not be an accurate impression because the majority of my working life is spent supervising and writing about research.  Perhaps, it’s because I spend so much time writing about research in my ‘day job’ that last year I only blogged about it three times on: digital twins [see ‘Can you trust your digital twin?’ on November 23rd, 2016], model credibility [see ‘Credibility is in the Eye of the Beholder’ on April 20th, 2016] and model validation [see Models as fables on March 16th, 2016].  This list gives another false impression – that my research is focussed on digital modelling and simulation.  It is just the trendiest part of my research activity.  So, I thought that I should correct this imbalance with some INSTRUCTIVE posts.

References:

[1] Greene, R.J., Patterson, E.A., Rowlands, R.E., 2008, ‘Thermoelastic stress analysis’, in Handbook of Experimental Mechanics edited by W.N. Sharpe Jr., Springer, New York.

[2] Rowlands, R.E., Patterson, E.A., 2008, ‘Determining principal stresses thermoelastically’, J. Strain Analysis, 43(6):519-527.

[3] Diaz, F.A., Patterson, E.A., Yates, J.R., 2009, ‘Assessment of effective stress intensity factors using thermoelastic stress analysis’, J. Strain Analysis, 44 (7), 621-632.

[4] Fruehmann RK, Dulieu-Barton JM, Quinn S, Thermoelastic stress and damage analysis using transient loading, Experimental Mechanics, 50:1075-1086, 2010.