Month: September 2012

More material

In previous posts I have mentioned the need for ‘more material’ in order to reduce the probability of failure.  This is a little sloppy, since there are, at least, two options buried in these statements.  Namely, the simple one, which is to add a greater mass of material; and the alternative, which is to use a stronger but lighter material, i.e. a more sophisticated material, e.g. a composite.  These are usually also more expensive but can also provide opportunities to incorporate sustainability via bio-based recyclability [for information on bio-based composites see http://www.ag.ndsu.edu/bioepic/documents/symposium/NDS%20Bio-BasedMaterials-DRZAL-10-07-final.pdf%5D.

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Risk definition

A section from a photoelastic model of turbine disc with a single blade viewed in polarised light to reveal the stress distribution.

Risk is defined as the possibility of something happening multiplied by the consequences when it does happen.  The public understanding of risk sometimes only extends to the first half of this definition.  Engineers seek to reduce the risks associated with component failure.  This means accepting a non-zero probability of failure happening and then designing for least catastrophic consequences.  So for instance in a jet engine, this implies designing so that if a crack develops it is in a blade rather than the disc to which all of the blades are attached.  The engine casing can be designed to contain a single blade breaking off and thus protect the rest of the plane from flying debris, but not to contain the rupture of an entire disc and set of blades.

For more information on the photoelastic stress analysis techniques used to generate the image, see http://www.experimentalstress.com

Unlikely failure

High magnification image of a crack in polycarbonate viewed in polarised light which reveals fringes that a proportional to the stress in the material.

I used the term ‘unlikely to fail’ in my last post, in the context of engineering designs.  This might appear alarming, since people might assume that engineers design things to never fail.  However it is impossible to design with a certainty that failure will not happen.  There are several reasons for this, including: the conflicting requirements of less material to reduce cost and achieve sustainability and of more material to protect against failure; our lack of knowledge about in-service and exceptional operating conditions; and the extent, or otherwise, that the computational model used in the design analysis represents the real world.

Hence the phrase ‘unlikely to fail’.  We can reduce the likelihood, or probability, of failure, usually at additional financial and resource cost, but we can never reduce the probability to zero, i.e. there is always a risk of failure, although we do our best to ensure that designs are ‘unlikely to fail’.

Model validation

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

Why is validation important?  Validation of computational mechanics models is defined as ‘determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model’, according to  ASME V&V 10-2006.  So, the validation of models of structural integrity for engineering design provides information about the degree to which the simulation results from the model can be believed.  This in turn helps in making decisions about how little material, and in what configuration, should be used to create elegant, sustainable designs that are unlikely to fail. So validation of computational mechanics models is an essential step in solving the ‘two earths’ dilemma (see post on August 13th, 2012).

Model credibility

Last week I spoke at the annual conference of the Associazione Italiana per ‘Analisi dell Sollecitazioni in Vicenza, Italy on the role of experimental mechanics in the validation of computational models used in engineering simulations.  We discussed the conflict between reducing cost and energy consumption and increasing performance and reliability of engineering machines and vehicles.  Generally, the former implies using less material more efficiently, while the latter tends to require the use of more material.  Engineers resolve this conflict by using computational models when optimising designs to simulate engineering behaviour.  The development of elegant and successful designs requires a high level of credibility in the models.  This credibility can be established by comparing the results from models with those from specially-conducted experiments; a process that is known as ‘validation’.