Tag Archives: power stations

Aorta: structure to rupture

Decorative image from a video showing predicted flow through aortic valve and resultant stress in leaflets of valveRegular readers have probably already realised that I have very broad interests in engineering from aircraft and power stations [see ‘Conversations about engineering over dinner and haircut‘ on February 16th, 2022] to nanoparticles interacting with cells [see ‘Fancy a pint of science‘ on April 27th, 2022].  So, it will come as no surprise to hear that I gave a welcome address to a workshop on ‘Aorta: Structure to Rupture‘ last week.  The workshop was organised in Liverpool by one of my colleagues, with sponsorship from the British Heart Foundation, and I was invited to welcome delegates in my capacity as Dean of the School of Engineering.  It was exciting on two levels: speaking, for the first time in more than two years, to an audience who had travelled from around the world to discuss research. And because the topic was closely associated with cardiac dynamics, which is a field that I worked in for nearly twenty years until around 2006.  I was part of an interdisciplinary team modelling the fluid-structure interaction in the aortic valve as it opens when blood is pumped through it by the heart and then closes to prevent back flow into the heart.  The team dispersed after I moved to the USA in 2004.  So speaking to the workshop last week was something of a trip down memory lane for me and led me to look up our last publication in the field.  I was surprised to find it was cited seven times last year.

The image in the thumbnail is a snapshot from a video showing the predicted time-varying distribution of blood flow through the aortic valve and the resultant distribution of stress in the leaflets of the valve during a heart beat.  The simultation is described in our last publication in cardiac dynamics: Carmody, C. J., Burriesci, G., Howard, I. C., & Patterson, E. A.,  An approach to the simulation of fluid–structure interaction in the aortic valve. J. Biomechanics, 39(1), 158-169, 2006.

Conversations about engineering over dinner and a haircut

For decorative purposes: colour contour map of a face mask produced using fringe projectionRecently, over dinner, someone I had just met asked me what type of engineering I do. I always find this a difficult question to answer because I am sure that they are just being polite and do not want to hear any technical details but I find it hard to give an interesting answer without diving into details. Earlier the same day I had given a lecture on thermodynamics to about 300 undergraduate students so I told my inquisitor about this experience and explained that thermodynamics was the science of energy and its transformation into different forms. Then, I muttered something about being interested in making and using measurements to ensure that computational models of aircraft and nuclear power stations are reliable and the conversation quickly moved on. A week or so earlier, I was having my hair cut when the barber asked me a similar question about what I did and I told him that I was a professor of engineering which led to a conversation about robots. We speculated about whether we would ever lose our jobs to robots and decided that we were both fairly secure against that threat. There is a high degree of creativity in both of our roles – while I always ask for the same haircut, my hair is in a different state every time I visit the barbers’ and I leave looking slightly different every time. I don’t think that I would like the uniformity that a row of robots in the barbers’ shop might produce. And, then there is the conversation during the haircut. A robot would need to pass the Turing test, i.e., to exhibit intelligent behaviour indistinguishable from a human, which no computer has yet achieved or is likely to do so in our lifetime, at least not a cost that would allow them to replace barbers. The same holds for professors – the shift to delivering lectures online during the pandemic might have made some professors worry that their jobs were at risk as recorded lectures replaced live performances; however, student feedback tells us that students have a strong preference for on-campus teaching and the high turnout for my thermodynamics lectures supports that conclusion.

Footnotes:

For a new website I was asked to describe my research interests in about 25 words and used the following: ‘the acquisition of information-rich measurement data and its use to develop digital representations of complex systems in the aerospace, biological and energy sectors’.  Fine for a website but not dinner conversation! 

There have been some attempts to build a robot that cut your hair, for example see this video

Image shows a colour contour map describing the shape of a facemask produced using fringe projection which could be used as part of the vision system for a robotic barber.  For more information on fringe projection see: Ortiz, M. H., & Patterson, E. A. (2005). Location and shape measurement using a portable fringe projection system. Experimental mechanics, 45(3), 197-204 or watch this video from the INDUCE project that was active from 1998 to 2001.

Bringing an end to thermodynamic whoopee

Two weeks ago I used two infographics to illustrate the dominant role of energy use in generating greenhouse gas emissions and the disportionate production of greenhouse gas emission by the rich [see ‘Where we are and what we have‘ on November 24th, 2021].  Energy use is responsible for 73% of global greenhouse gas emissions and 16% of the world’s population are responsible for 38% of global CO2 emissions.  Today’s infographics illustrate the energy flows from source to consumption for the USA (above), UK and Europe (thumbnails below).  In the USA fossil fuels (coal, natural gas and petroleum) are the source of nearly 80% of their energy, in the UK it is a little more than 80% and the chart for Europe is less detailed but the proportion looks similar. COP 26 committed countries to ending ‘support for the international unabated fossil fuel energy sector by the end of 2022’ and recognised ‘investing in unabated fossil-related energy projects increasingly entails both social and economic risks, especially through the form of stranded assets, and has ensuing negative impacts on government revenue, local employment, taxpayers, utility ratepayers and public health.’  However, to reduce our dependency on fossil fuels we need a strategy, a plan of action for a fundamental change in how we power industry, heat our homes and propel our vehicles.  A hydrogen economy requires the production of hydrogen without using fossil fuels, electric cars and electric domestic heating requires our electricity generating capacity to be at least trebled by 2050 in order to hit the net zero target. This scale and speed of  transition to zero-carbon sources is such that it will have to be achieved using an integrated blend of green energy sources, including solar, wind and nuclear energy.  For example, in the UK our current electricity generating capacity is about 76 GW and 1 GW is equivalent to 3.1 million photovoltaic (PV) panels, or 364 utility scale wind turbines [www.energy.gov/eere/articles/how-much-power-1-gigawatt] so trebling capacity from one of these sources alone would imply more than 700 million PV panels, or one wind turbine every square mile.  It is easy to write policies but it is much harder to implement them and make things happen especially when transformational change is required.  We cannot expect things to happen simply because our leaders have signed agreements and made statements.  Now, national plans are required to ween us from our addiction to fossil fuels – it will be difficult but the alternative is that global warming might cause the planet to become uninhabitable for us.  It is time to stop ‘making thermodynamic whoopee with fossil fuels’ to quote Kurt Vonnegut [see ‘And then we discovered thermodynamics‘ on February 3rd, 2016].

 

 

 

 

 

 

 

 

 

Sources:

Kurt Vonnegut, A Man without a Country, New York: Seven Stories Press, 2005.  He wrote ‘we have now all but destroyed this once salubrious planet as a life-support system in fewer than two hundred years, mainly by making thermodynamic whoopee with fossil fuels’.

US Energy flow chart: https://flowcharts.llnl.gov/commodities/energy

EU Energy flow chart: https://ec.europa.eu/eurostat/web/energy/energy-flow-diagrams

UK Energy flow chart: https://www.gov.uk/government/collections/energy-flow-charts#2020

Reduction in usefulness of reductionism

decorative paintingA couple of months ago I wrote about a set of credibility factors for computational models [see ‘Credible predictions for regulatory decision-making‘ on December 9th, 2020] that we designed to inform interactions between researchers, model builders and decision-makers that will establish trust in the predictions from computational models [1].  This is important because computational modelling is becoming ubiquitous in the development of everything from automobiles and power stations to drugs and vaccines which inevitably leads to its use in supporting regulatory applications.  However, there is another motivation underpinning our work which is that the systems being modelled are becoming increasingly complex with the likelihood that they will exhibit emergent behaviour [see ‘Emergent properties‘ on September 16th, 2015] and this makes it increasingly unlikely that a reductionist approach to establishing model credibility will be successful [2].  The reductionist approach to science, which was pioneered by Descartes and Newton, has served science well for hundreds of years and is based on the concept that everything about a complex system can be understood by reducing it to the smallest constituent part.  It is the method of analysis that underpins almost everything you learn as an undergraduate engineer or physicist. However, reductionism loses its power when a system is more than the sum of its parts, i.e., when it exhibits emergent behaviour.  Our approach to establishing model credibility is more holistic than traditional methods.  This seems appropriate when modelling complex systems for which a complete knowledge of the relationships and patterns of behaviour may not be attainable, e.g., when unexpected or unexplainable emergent behaviour occurs [3].  The hegemony of reductionism in science made us nervous about writing about its short-comings four years ago when we first published our ideas about model credibility [2].  So, I was pleased to see a paper published last year [4] that identified five fundamental properties of biology that weaken the power of reductionism, namely (1) biological variation is widespread and persistent, (2) biological systems are relentlessly nonlinear, (3) biological systems contain redundancy, (4) biology consists of multiple systems interacting across different time and spatial scales, and (5) biological properties are emergent.  Many engineered systems possess all five of these fundamental properties – you just to need to look at them from the appropriate perspective, for example, through a microscope to see the variation in microstructure of a mass-produced part.  Hence, in the future, there will need to be an increasing emphasis on holistic approaches and systems thinking in both the education and practices of engineers as well as biologists.

For more on emergence in computational modelling see Manuel Delanda Philosophy and Simulation: The Emergence of Synthetic Reason, Continuum, London, 2011. And, for more systems thinking see Fritjof Capra and Luigi Luisi, The Systems View of Life: A Unifying Vision, Cambridge University Press, 2014.

References:

[1] Patterson EA, Whelan MP & Worth A, The role of validation in establishing the scientific credibility of predictive toxicology approaches intended for regulatory application, Computational Toxicology, 17: 100144, 2021.

[2] Patterson EA &Whelan MP, A framework to establish credibility of computational models in biology. Progress in biophysics and molecular biology, 129: 13-19, 2017.

[3] Patterson EA & Whelan MP, On the validation of variable fidelity multi-physics simulations, J. Sound & Vibration, 448:247-258, 2019.

[4] Pruett WA, Clemmer JS & Hester RL, Physiological Modeling and Simulation—Validation, Credibility, and Application. Annual Review of Biomedical Engineering, 22:185-206, 2020.