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/

Clueless on leadership style

Sunset from Peppercombe beachStrategic leadership is widely defined as the ability to influence others to voluntarily make decisions that enhance the prospects of the organisation’s success.  In learning and teaching, you could substitute or supplement organisation’s success with the students’ success.   I believe that this is achieved by creating an environment in which your colleagues can thrive and contribute; so, I see leadership of an academic community as being primarily a service involving the creation and maintenance of a culture of scholarship and excellence.

I have led academic departments on both sides of the Atlantic, university-industrial research programmes and various other organisations and initiatives.  However, the standard interview question about my leadership style still tends to stump me – I struggle to identify a consistent approach to my leadership and I am nervous that too much analysis could undermine my ability to lead.  However, by chance, I recently came across Daniel Goleman’s work.  His research has shown that the use of a collection of leadership styles (he identifies six styles), each at the right time and in the right amount, produces the most effective outcomes.  In other words, effective leadership is about being pragmatic and adjusting your approach to suit the circumstances. What’s more, Goleman found that most successful business leaders who followed this pragmatic approach had no idea how they selected the right style for the right time.

Goleman’s work implies that you do not have to conform to one leadership model.  Instead, you can roam across a number of leadership styles and select the right one, for the right situation and use it in just the right amount.  It sounds straightforward but this flexibility is tough to put into action.  Of course, that’s not easy to teach because most of us don’t know how or why we make those decisions but it is related to emotional intelligence and leadership competencies, which we do know how to teach.

Bibliography:

Goleman D, Boyatzis R & McKee, The new leaders: transforming the art of leadership into the science of results, London: Sphere, 2002.

Goleman D, Leadership that get results, Harvard Business Review, 78(2):4-17, 2000.

 

Uncertainty about Bayesian methods

I have written before about why people find thermodynamics so hard [see my post entitled ‘Why is thermodynamics so hard?’ on February 11th, 2015] so I think it is time to mention another subject that causes difficulty: statistics.  I am worried that just mentioning the word ‘statistics’ will cause people to stop reading, such is its reputation.  Statistics is used to describe phenomena that do not have single values, like the height or weight of my readers.  I would expect the weights of my readers to be a normal distribution, that is they form a bell-shaped graph when the number of readers at each value of weight is plotted as a vertical bar from a horizontal axis representing weight.  In other words, plotting weight along the x-axis and frequency on the y-axis as in the diagram.

The normal distribution has dominated statistical practice and theory since its equation was first published by De Moivre in 1733.  The mean or average value corresponds to the peak in the bell-shaped curve and the standard deviation describes the shape of the bell, basically how fat the bell is.  That’s why we learn to calculate the mean and standard deviation in elementary statistics classes, although often no one tells us this or we quickly forget it.

If all of you told me your weight then I could plot the frequency distribution described above.  And, if I divided the y-axis, the frequency values, by the total number of readers who sent me weight information then the graph would become a probability density distribution [see my post entitled ‘Wind power‘ on August 7th, 2013].  It would tell me the probability that the reader I met last week had a weight of 70.2kg – the probability would be the height of the bell-shaped curve at 70.2kg.  The most likely weight would correspond to the peak value in the curve.

However, I don’t need any of you to send me your weights to be reasonably confident that the weight of the reader I talked to last week was 70.2kg!  I cannot be certain about it but the probability is high.  The reader was female and lived in the UK and according to the Office of National Statistics (ONS) the average weight of women in the UK is 70.2kg – so it is reasonable to assume that the peak in the bell-shaped curve for my female UK readers will coincide with the national distribution, which makes 70.2kg the most probable weight of the reader I met last week.

However, guessing the weight of a reader becomes more difficult if I don’t know where they live or I can’t access national statistics.  The Reverend Thomas Baye (1701-1761) comes to the rescue with the rule named after him.  In Bayesian statistics, I can assume that the probability density distribution of readers’ weight is the same as for the UK population and when I receive some information about your weights then I can update this probability distribution to better describe the true distribution.  I can update as often as I like and use information about the quality of the new data to control its influence on the updated distribution.  If you have got this far then we have both done well; and, I am not going lose you now by expressing Baye’s law in terms of probability, or talking about prior (that’s my initial guess using national statistics) or posterior (that’s the updated one) distributions; because I think the opaque language is one of the reasons that the use of Bayesian statistics has not become widespread.

By the way, I can never be certain about your weight; even if you tell me directly, because I don’t know whether your scales are accurate and whether you are telling the truth!  But that’s a whole different issue!

Listening with your eyes shut

I am in the London Underground onboard a train on my way to a conference on ‘New Approaches to Higher Education’ organised by the Institution of Engineering and Technology and the Engineering Professors’ Council.  The lady opposite has her eyes closed but she is not asleep because she opens them periodically as we come into stations to check whether it’s her stop.  I wonder if she is trying to reproduce John Hull’s experience of the depth of sounds as a blind person [see my post entitled ‘Rain brings out the contours in everything‘ on February 22, 2017].  For the second time in recent weeks, I close my eyes and try it for myself.  It is surprising how in a crowded train, I can’t hear anyone, just the noise made by the train.  It’s like a wobble board that’s joined by a whole percussion section of an orchestra when we go around a bend or over points.  The first time I closed my eyes was at a concert at the Philharmonic Hall in Liverpool.  My view of the orchestra was obstructed by the person in front of me so, rather than stare at the back of their head, I closed my eyes and allowed the music to dominate my mind.  Switching off the stream of images seemed to release more of my brain cells to register the depth and richness of Bach’s Harpsichord Concerto No. 5.  I was classified as tone deaf at school when I was kicked out of the choir and I learned no musical instruments, so the additional texture and dimensionality in the music was a revelation to me.

Back to the London Underground – many of my fellow passengers were plugged into their phones or tablets via their ears and eyes.  I wondered if any were following the MOOC on Understanding Super Structures that we launched recently.  Unlikely I know, but it’s a bit different, because it is mainly audio clips and not videos.  We’re trying to tap into some of the time many people spend with earbuds plugged into their ears but also make the MOOC more accessible in countries where internet access is mainly via mobile phones.  My recent experiences of listening with my eyes closed, make me realize that perhaps we should ask people to close their eyes when listening to our audio clips so that they can fully appreciate them.  If they are sitting on the train then that’s fine but not recommended if you are walking across campus or in town!

Tsundoku

I used to suffer from tsundoku but now I am almost cured…  Tsundoku is a Japanese word meaning ‘the constant act of buying books but never reading them’.  I still find it hard to walk into a good bookshop and leave without buying a small pile of books.  I did it early this month in the Camden Lock Books and left with ‘The New Leaders‘ by Daniel Goleman, ‘What we talk about when we talk about love‘ by Raymond Carver and ‘The Fires of Autumn‘ by Irène Némirowsky.  I will probably read all of these three books over the coming months so it was not really an act of tsundoku.  But, it’s perhaps only because there are so few really good bookshops left that I don’t  buy more in a year than I can read.  Although this is not quite true in my professional life, because I have started buying books on-line and the pile of unread books in my office is growing; so I am not completely cured of tsundoku.  Actually, all researchers are probably suffering from it because we collect piles of research papers that we never read – in part because we can’t keep up with the 2.5 million papers published every year.  And, it’s growing by about 5% per annum, according to Sarah Boon; perhaps, because there are more than 28,000 scholarly journals publishing peer-reviewed research.  Of course, that’s what happens if you measure research productivity in terms of papers published – it’s a form of Goodhart’s law [see my post entitled ‘Goodhart’s Law‘ on August 6th, 2014].