Tag Archives: research

Slow-motion multi-tasking leads to productive research

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Most of my academic colleagues focus their research activity on a relatively narrow field and many have established international reputations in their chosen field of study.  However, my own research profile is broad, including recently-published studies on the motion of nanoparticles, damage propagation in composites and stress analysis in aerospace components  as well as current research on the fidelity and credibility simulations and tests (FACTS) in the aerospace, biomedical and nuclear industries.  My breadth of interests makes it difficult to categorise me or to answer the inevitable question about what research I do.  And, I have always felt the need to excuse or apologise for the breadth and explain by making  tenuous connections between my diverse research activities. However, apparently my slow-motion multi-tasking is a characteristic of many high-performing artists and scientists.  Mihaly Csikszentmihalyi has proposed that slowly changing back and forth between different projects is a standard practice amongst people with high levels of originality and creativity.  Scientists that work on several problems at once and frequently refocus their research tend to enjoy the longest and most productive careers according to another study by Bernice Eiduson.

So, no more excusing or apologising for my range of research interests.  It is merely slow-motion multi-tasking to achieve a long and productive career characterised by original and creative research!

Sources:

Tim Harford, Holidays hold the secret to unleashing creativity, FT Weekend, Opinion 25/26 August 2018.

Root‐Bernstein RS, Bernstein M, Gamier H. Identification of scientists making long‐term, high‐impact contributions, with notes on their methods of working. Creativity Research Journal.  6(4):329-43, 1993.

On the state of universities

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In the UK we are limbering up for the Research Excellence Framework 2021 (REF 2021) which is the process of expert review of research activity in UK universities that is conducted periodically by the government – the last one was in 2014.  The outcome influences the allocation of government funding for research as well as providing accountability for public investment in research and benchmarking information.

Earlier this month I received an email inviting me to contribute to a government consultation on the draft guidance and criteria for REF 2021.  It reminded me of a description of Abraham Flexner’s 1910 report for the Carnegie Foundation on medical schools in the US that I read in an essay by Robbert Dijkgraaf.  Flexner branded many of the 155 medical schools in the USA as ‘frauds and irresponsible profit machines’.  Hopefully that will not the outcome of REF2021!

Source:

Robbert Dijkgraaf, ‘The World of Tomorrow’ in The Usefulness of Useless Knowledge by A.Flexner, Princeton University Press, Princeton, NJ, 2015.

Establishing fidelity and credibility in tests & simulations (FACTS)

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A month or so ago I gave a lecture entitled ‘Establishing FACTS (Fidelity And Credibility in Tests & Simulations)’ to the local branch of the Institution of Engineering Technology (IET). Of course my title was a play on words because the Oxford English Dictionary defines a ‘fact’ as ‘a thing that is known or proved to be true’ or ‘information used as evidence or as part of report’.   One of my current research interests is how we establish predictions from simulations as evidence that can be used reliably in decision-making.  This is important because simulations based on computational models have become ubiquitous in engineering for, amongst other things, design optimisation and evaluation of structural integrity.   These models need to possess the appropriate level of fidelity and to be credible in the eyes of decision-makers, not just their creators.  Model credibility is usually provided through validation processes using a small number of physical tests that must yield a large quantity of reliable and relevant data [see ‘Getting smarter‘ on June 21st, 2017].  Reliable and relevant data means making measurements with low levels of uncertainty under real-world conditions which is usually challenging.

These topics recur through much of my research and have found applications in aerospace engineering, nuclear engineering and biology. My lecture to the IET gave an overview of these ideas using applications from each of these fields, some of which I have described in past posts.  So, I have now created a new page on this blog with a catalogue of these past posts on the theme of ‘FACTS‘.  Feel free to have a browse!

Fourth industrial revolution

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Have you noticed that we are in the throes of a fourth industrial revolution?

The first industrial revolution occurred towards the end of the 18th century with the introduction of steam power and mechanisation.  The second industrial revolution took place at the end of the 19th and beginning of the 20th century and was driven by the invention of electrical devices and mass production.  The third industrial revolution was brought about by computers and automation at the end of the 20th century.  The fourth industrial revolution is happening as result of combining physical and cyber systems.  It is also called Industry 4.0 and is seen as the integration of additive manufacturing, augmented reality, Big Data, cloud computing, cyber security, Internet of Things (IoT), simulation and systems engineering.  Most organisations are struggling with the integration process and, as a consequence, are only exploiting a fraction of the capabilities of the new technology.  Revolutions are, by their nature, disruptive and those organisations that embrace and exploit the innovations will benefit while the existence of the remainder is under threat [see [‘The disrupting benefit of innovation’ on May 23rd, 2018].

Our work on the Integrated Nuclear Digital Environment, on Digital Twins, in the MOTIVATE project and on hierarchical modelling in engineering and biology is all part of the revolution.

Links to these research posts:

‘Enabling or disruptive technology for nuclear engineering?’ on January 28th, 2015

‘Can you trust your digital twin?’ on November 23rd, 2016

‘Getting Smarter’ on June 21st, 2017

‘Hierarchical modelling in engineering and biology’ [March 14th, 2018]

 

Image: Christoph Roser at AllAboutLean.com from https://commons.wikimedia.org/wiki/File:Industry_4.0.png [CC BY-SA 4.0].