Tag Archives: Engineering

Floods: an everyday example

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floodingI wrote this post before going to the concert at the Philharmonic Hall which inspired the post on February 5th [Rhapsody in Blue].  So, this post is not quite as timely as planned originally but it is still raining frequently here and the Somerset levels remain flooded.

Since before Christmas news bulletins in the US and UK have been dominated by reports of extreme weather events.  Earlier this month the sea on the south coast of the England swept away a substantial length of the main railway line between London and the South-West of the country.  Large areas of the south of the UK have been flooded by storms that rolled across the Atlantic having first caused disruption in North America.  There seems to be plenty of everyday evidence from these events that our climate is changing and this appears to have been confirmed by the Chief Scientist at the UK Metrological Office.

The Intergovernmental Panel on Climate Change has stated ‘Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia.  The atmosphere and oceans have warmed, the amounts of snow and ice diminished, sea level has risen, and the concentrations of greenhouse gases have increased.’  They go on to say ‘It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-twentieth century’.  Despite these assertions, our governments have been unable to make significant progress towards limiting global warming to 2 degrees Celsius compared to pre-industrial levels.  The delegations from most of the developed countries walked out of talks at the Warsaw climate conference last November, followed by representatives from the Green groups and NGOs the next day.  As a consequence, Kofi Annan [Climate crisis: Who will act? in International NYT  November 25, 2013] has called for a global grass-roots movement to tackle climate change and its consequences.  We need to act as individuals whenever we can to reduce global warming and mitigate its impact both directly in our personal and professional lives and indirectly by lobbying our political and industrial/commercial leaders.

In the UK, politicians and the media are beginning to talk about the need for engineers to protect us against flooding and some engineers are responding by highlighting that the cost will be very high and that if climate change continues then we will have consider abandoning some areas.

At a simpler level, those us working in the classroom can use the flooded roads and overwhelmed drainage systems to create topical, and perhaps increasingly everyday, examples focused on flow in drainage ditches, gutters etc., as in the lesson plan below.

5EplanNoF10_open_channel_flow

See also the Everyday Examples page on this blog for more lesson plans and more background on Everyday Examples.

Mining data

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Random winter scene: Old Mission Point Light, MI, USA

Random winter scene:
Old Mission Point Light, MI, USA

Last week I went to a one-day conference in London on High Performance Computer and Big Data.  We were talking about computers with 96,000 processors and datasets in the exascale, which means the number of pieces of data they contain is one with eighteen noughts after it.  We were just across the street from the Houses of Parliament and David Willetts, the UK Minister of Universities and Science, addressed us and told us that ‘future scientific advances are dependent on our ability to accumulate and analyse big data’.  For the industrialists amongst us the slogan from the Director of the UK’s biggest computer was ‘to compute is to out compete’.

Suzy Moat and Tobias Preis of Warwick Business School made a great presentation about the link between online behaviour and economic decision making around the globe.  They have found that the frequency terms such as ‘debt’, ‘stocks’ and ‘portfolio’ are predictors of subsequent stock market movement.  They performed some of their research by mining data available from Google Trends – if you have never visited this bit of Google’s domain then I recommend a visit, its interesting at all sorts of levels.

Another Google data-miner is Seth Stephens-Davidowitz who has revealed that American parents want their boys to be smart and their girls skinny.  Parents are two and half times more likely to ask Google ‘Is my son gifted?’ than ‘Is my daughter gifted?’ despite the fact that in American schools girls are 11 percent more likely to be in gifted programs.  And conversely, parents are twice as likely to ask ‘Is my daughter overweight?’ than ‘Is my son overweight?’ even though roughly equal proportions of girls and boys are overweight in the USA.  In his article in the NYT, Seth concludes by asking ‘How would girls’ lives be different if parents were half as concerned about their bodies and twice as intrigued about their minds?’

Perhaps, one answer is that they would be more likely to opt for what are perceived at school as the hard subjects, i.e. mathematics and physics.  See my earlier post entitled ‘Chemical Imbalance’ on October 2nd, 2013, in which I bemoaned the low proportion of girls taking A-level Physics at school.  As professional engineers and university teachers many of us are working hard to redress the gender imbalance in engineering but now I wonder if we are have identified a new handicap, i.e. parents are undermining their daughters’ confidence to enter the ‘problem-solving’ professions.

Sources:

Seth Stephens-Davidovitz, ‘Is my son a genius?’ in the International New York Times on Monday 20th January, 2014. www.nytimes.com/2014/01/19/opinion/sunday/google-tell-me-is-my-son-a-genius.html?_r=0

Pries, T., Moat, H.S., Stanley, H.E., 2013, Quantifying trading behaviour in financial markets using Google Trends, Scientific Reports 3, 1684. www.nature.come/strep/2013/130425/srep01684/pdf/srep01684.

Setting standards

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cenLast week I wrote about digital image correlation as a method for measuring surface strain and displacement fields.  The simplicity and modest cost of the equipment required combined with the quality and quantity of the results is revolutionizing the field of experimental mechanics.  It also has the potential to do the same in computational mechanics by enabling more comprehensive validation of models and thus enhancing the credibility and confidence in engineering simulations.  I have written and lectured on this topic many times, see for instance my post of September 17th, 2012 entitled ‘Model credibility’ or  http://sdj.sagepub.com/content/48/1.toc

At the moment, I am chair of a CEN workshop WS71 that is developing a precursor to a standard on validation of computational solid mechanics models.  To inform our deliberations, we have organised an Inter-Laboratory Study (ILS) to allow people to try out the proposed validation protocol and give us feedback.   If you would like to have a go then download the information pack.  You don’t need to do any experiments or modelling, just try the validation procedure with some of the data sets provided.  The more engineers that participate in the ILS then the better that the final CEN document is likely to be; so if you know someone who might be interested then forward this blog to them or just send them the link.

Displacement field measured using image correlation for metal wedge indenting a rubber block

Displacement field measured using digital image correlation for a metal wedge indenting a rubber block

CEN WS71: http://www.cen.eu/cen/Sectors/TechnicalCommitteesWorkshops/Workshops/Pages/WS71VANESSA.aspx

EU FP7 project VANESSA: www.engineeringvalidation.org

For information on the data field shown to the right see: http://sdj.sagepub.com/content/49/2/112.abstract

256 shades of grey

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bonnet panelEngineers are increasingly using digital photographs with 256 shades of grey to measure displacement of structural components.  The technique is known as Digital Image Correlation and is the most common way to measure the deformation of engineering structures and components in a laboratory, and increasingly in the field.  DIC provides maps of the displacement of the component surface from which the strain field can be calculated and which in turn allows engineers to assess the behaviour and likely failure modes of the component.  DIC is beginning to revolutionise the way in which we validate computational mechanics models.

DIC involves capturing ‘before’ and ‘after’ images of the component surface while load is applied.  If the surface has a random pattern, which is often created by spray-painting black speckles onto a white background, then it is possible to track the movement of the pattern as the surface moves and deforms.  The images are usually recorded as intensity maps defined by 256 shades of grey or grey levels from white through to black.  A mathematical signature is assigned to facets or sub-images of the intensity map in the ‘before’ image and a correlation algorithm uses the signature to recognise the facet in the ‘after’ image.  The positions of the centre of the facet in the ‘before’ and ‘after’ images indicates the displacement of the corresponding area of the component surface.  Two cameras can be used to provide stereoscopic vision and information on displacements in all directions.

The picture shows a car bonnet or hood panel in a test frame in a laboratory prior to an impact test with a random speckle pattern on the surface to allow DIC to be performed using high-speed cameras. For more details see: Burguete et al , 2013, J. Strain Analysis, doi:10.1177/0309324713498074 at http://sdj.sagepub.com/content/early/2013/09/19/0309324713498074.full.pdf+html

For detailed explanations of DIC try the monograph by Professor Mike Sutton and his colleagues [link.springer.com/content/pdf/bfm%3A978-0-387-78747-3%2F1.pdf] or the chapter on DIC in Optical Methods for Solid Mechanics by Pramod Rastogi and Erwin Hack [http://eu.wiley.com/WileyCDA/WileyTitle/productCd-3527411119.html].

For some applications see the special issue on DIC of the Journal of Strain Analysis for Engineering Design [http://sdj.sagepub.com/content/43/8.toc].