DATA ANALYSIS: MATERIALS
Material values
Felix Grant on the application of statistical packages to materials science
Early in the film Up in the Air[1], characters played by George Clooney and Vera Farmiga start a casual relationship whose emptiness is emphasised in a pointedly tragicomic scene. Picking up one of Clooney’s airline loyalty cards, Farmiga asks ‘what is that, carbon fibre? ... I love the weight!’ and follows up shortly thereafter with ‘pretty sexy’. It’s a whole new way of looking at materials science. Carbon fibre, now so ubiquitous that it can
be wasted in Clooney’s loyalty card, was of course a fairly early stage in the down-scale migration of materials science. What was considered daringly tiny when I learned my chemistry is now referred to as ‘bulk scale’, the nanoscale has become every day, and quantum effects are the new hunting ground. At the same time, however, the quest
for the small has not replaced the need for the large or medium-sized view of things. Carbon fibre first emerged nearly 50 years ago as an industrially viable patent from the Royal Aircraft Establishment at Farnborough, to see immediate application in Rolls Royce aero engines. As Steinhauser and Hiermaier[2] put it: ‘Some of the most fascinating problems in all fields of science
involve multiple temporal or spatial scales. Many processes occurring at a certain scale govern the behaviour of the system across several (usually larger) scales.’ Fitzgerald et al[3] comment more pragmatically, in a survey of cross scale computational methods, that ‘methods are now available that are capable of modelling hundreds of thousands of atoms, and the results can have a significant impact on real-world engineering ... the methods of molecular modelling are being used to solve engineering problems, despite the fact that they typically operate on comparatively short length and time scales.’ We now design and study materials at the submolecular level, but we still apply them at all stages up to the macroscale. The properties of those materials must bridge that divide – as must the simulation and analysis development loops which produce them. Analyses, whether on test results or as part
of in silico experiment, occur on a range of platforms from the highly specialised bespoke to the generic – the latter increasingly predominating. A quick dip into the literature on medical application of nanomaterials turns up, for example, a multi-level tendon deformation study, the effect of gamma
irradiant sterilisation on polycaprolactone scaffolding and self assembling aggregates as an antigen marker, all in SigmaPlot, plus a study of joint replacement wear prediction in its stable mate TableCurve3D. Moving from medicine to archaeology, for a change, their elder sibling Systat examines the formative structure of thousand-year-old ceramic pots. Golden Software’s Surfer, usually associated with geology, crops up in more than one nanofilm distortion study for its ability to rapidly visualise large and complex data sets. Wolfram Mathematica is often the enabling environment behind the virtual modelling and experimentation phases that precede physical development, from military aerospace design in the USAF’s Wright- Patterson research laboratory through thin films to cosmetics. Computation volume in materials science is
high, even by modern data analytic standards. Developing strategies for computation across multiscale ranges is highly management intensive, and as much attention is needed to keeping track of analyses as to the analyses themselves. Another illustration of the analysis volume problem is an important strand in materials building, down at the molecular level: detailed control of polymerisation to grow specific and repeatable structures for applications from lubricants through clay-based nanocomposites to biomedical
CSIRO scientists discussing RAFT. (Image supplied by CSIRO)
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SCIENTIFIC COMPUTING WORLD APRIL/MAY 2010
www.scientific-computing.com
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