Category Archives: mechanics

Spontaneously MOTIVATEd

Some posts arise spontaneously, stimulated by something that I have read or done, while others are part of commitment to communicate on a topic related to my research or teaching, such as the CALE series.  The motivation for a post seems unrelated to its popularity.  This post is part of that commitment to communicate.

After 12 months, our EU-supported research project, MOTIVATE [see ‘Getting Smarter‘ on June 21st, 2017] is one-third complete in terms of time; and, as in all research it appears to have made a slow start with much effort expended on conceptualizing, planning, reviewing prior research and discussions.  However, we are on-schedule and have delivered on one of our four research tasks with the result that we have a new validation metric and a new flowchart for the validation process.  The validation metric was revealed at the Photomechanics 2018 conference in Toulouse earlier this year [see ‘Massive Engineering‘ on April 4th, 2018].  The new flowchart [see the graphic] is the result of a brainstorming [see ‘Brave New World‘ on January 10th, 2018] and much subsequent discussion; and will be presented at a conference in Brussels next month [ICEM 2018] at which we will invite feedback [proceedings paper].  The big change from the classical flowchart [see for example ASME V&V guide] is the inclusion of historical data with the possibility of not requiring experiments to provide data for validation purposes. This is probably a paradigm shift for the engineering community, or at least the V&V [Validation & Verification] community.  So, we are expecting some robust feedback – feel free to comment on this blog!

References:

Hack E, Burguete RL, Dvurecenska K, Lampeas G, Patterson EA, Siebert T & Szigeti E, Steps toward industrial validation experiments, In Proceedings Int. Conf. Experimental Mechanics, Brussels, July 2018 [pdf here].

Dvurcenska K, Patelli E & Patterson EA, What’s the probability that a simulation agrees with your experiment? In Proceedings Photomechanics 2018, Toulouse, March 2018.

 

 

Mapping atoms

Typical atom maps of P, Cu, Mn, Ni & Si (clockwise from bottom centre) in 65x65x142 nm sample of steel from Styman et al, 2015.

A couple of weeks ago I wrote about the opening plenary talk at the NNL Sci-Tec conference [‘The disrupting benefit of innovation’ on May 23rd, 2018].  One of the innovations discussed at the conference was the applications of atom probe tomography for understanding the mechanisms underpinning material behaviour.  Atom probe tomography produces three-dimensional maps of the location and type of individual atoms in a sample of material.  It is a destructive technique that uses a high energy pulse to induce field evaporation of ions from the tip of a needle-like sample.  A detector senses the position of the ions and their chemical identity is found using a mass spectrometer.  Only small samples can be examined, typically of the order of 100nm.

A group led by Jonathan Hyde at NNL have been exploring the use of atom probe tomography to understand the post-irradiation annealing of weld material in reactor pressure vessels and to examine the formation of bubbles of rare gases in fuel cladding which trap hydrogen causing material embrittlement.  A set of typical three-dimensional maps of atoms is shown in the thumb-nail from a recent paper by the group (follow the link for the original image).

It is amazing that we can map the location of atoms within a material and we are just beginning to appreciate the potential applications of this capability.  As another presenter at the conference said: ‘Big journeys begin with Iittle steps’.

BTW it was rewarding to see one of our alumni from our CPD course [see ‘Leadership is like shepherding’ on May 10th, 2017] presenting this work at the conference.

Source:

Styman PD, Hyde JM, Parfitt D, Wilford K, Burke MG, English CA & Efsing P, Post-irradiation annealing of Ni-Mn-Si-enriched clusters in a neutron-irradiated RPV steel weld using atom probe tomography, J. Nuclear Materials, 459:127-134, 2015.

Third time lucky

At the end of last year my research group had articles published by the Royal Society’s journal  Open Science in two successive months [see ‘Press Release!‘ on November 15th, 2017 and ‘Slow moving nanoparticles‘ on December 13th, 2017].  I was excited about both publications because I had only had one article published before by the Royal Society and because the Royal Society issues a press release whenever it publishes a new piece of science.  However, neither press release generated any interest from anyone; probably because science does not sell newspapers (or attract viewers) unless it is bad news or potentially life-changing.  And our work on residual stress around manufactured holes in aircraft or on the motion of nanoparticles does not match either of these criteria.

Last month, we did it again with an article on ‘An experimental study on the manufacture and characterization of in-plane fibre-waviness defects in composites‘.  Third time lucky, because this time our University press office were interested enough to write a piece for the news page of the University website, entitled ‘Engineers develop new method to recreate fibre waviness defects in lab‘.  Fibre waviness is an issue in the manufacture of structural components of aircraft using carbon fibre reinforced composites because kinks or waves in the fibres can cause structural weaknesses.  As part of his PhD, supported by Airbus and the UK Engineering and Physical Sciences Research Council (EPSRC), Will Christian developed an innovative technique to generate defects in our lab so that we can gain a better understanding of them. Read the article or the press release to find out more!

Image shows fracture through a waviness-defect in the top-ply of a carbon-fibre laminate observed in a microscope following sectioning after failure.

Reference:

Christian WJR, DiazDelaO FA, Atherton K & Patterson EA, An experimental study on the manufacture and characterisation of in-plane fibre-waviness defects in composites, R. Soc. open sci. 5:180082, 2018.

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.

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.