Tag Archives: simulation

35 years later and still working on a PhD thesis

It is about 35 years since I graduated with my PhD.  It was not ground-breaking although, together with my supervisor, I did publish about half a dozen technical papers based on it and some of those papers are still being cited, including one this month which surprises me.  I performed experiments and computer modelling on the load and stress distribution in threaded fasteners, or nuts and bolts.  There were no digital cameras and no computer tomography; so, the experiments involved making and sectioning models of nuts and bolts in transparent plastic using three-dimensional photoelasticity [see ‘Art and Experimental Mechanics‘ on July 17th, 2012].  I took hundreds of photographs of the sections and scanned the negatives in a microdensitometer.  The computer modelling was equally slow and laborious because there were no graphical user interfaces (GUI); instead, I had to type strings of numbers into a terminal, wait overnight while the calculations were performed, and then study reams of numbers printed out on long rolls of paper.  The tedium of the experimental work inspired me to work on utilising digital technology to revolutionise the field of experimental mechanics over the following 15 to 20 years.  In the past 15 to 20 years, I have moved back towards computer modelling and focused on transforming the way in which measurement data are used to improve the fidelity of computer models and to establish confidence in their predictions [see ‘Establishing fidelity and credibility in tests and simulations‘ on July 25th, 2018].  Since completing my PhD, I have supervised 32 students to successful completion of their PhDs.  You might think that was a straightforward process of an initial three years for the first one to complete their research and write their thesis, followed by one graduating every year.  But that is not how it worked out, instead I have had fallow years as well as productive years.  At the moment, I am in a productive period, having graduated two PhD students per year since 2017 – that’s a lot of reading and I have spent much of the last two weekends reviewing a thesis which is why PhD theses are the topic of this post!

Footnote: the most cited paper from my thesis is ‘Kenny B, Patterson EA. Load and stress distribution in screw threads. Experimental Mechanics. 1985 Sep 1;25(3):208-13‘ and this month it was cited by ‘Zhang D, Wang G, Huang F, Zhang K. Load-transferring mechanism and calculation theory along engaged threads of high-strength bolts under axial tension. Journal of Constructional Steel Research. 2020 Sep 1;172:106153‘.

Tacit hurdle to digital twins

Tacit knowledge is traditionally defined as knowledge that is not explicit or that is difficult to express or transfer from someone else.  This description of what it is not makes the definition itself tacit knowledge which is not very helpful.  Management guides resolve this by giving examples, such as aesthetic sense, or innovation and leadership skills which are elusive skills that are hard to explain [see ‘Innovation out of chaos‘ on June 29th 2016 and  ‘Clueless on leadership style‘ on June 14th, 2017].  In engineering, there are a series of skills that are hard to explain or teach, including creative problem-solving [see ‘Learning problem-solving skills‘  on October 24th, 2018], artful design [see ‘Skilled in ingenuity‘ on August 19th, 2015] and elegant modelling [see ‘Credibility is in the eye of the beholder‘ on April 20th, 2016].  In a university course we attempt to lay the foundations for this tacit engineering knowledge; however, much of it is gained in work through experience and becomes regarded by organisations as part of their intellectual assets – the core of their competitiveness and source of their sustainable technology advantage.  In our work on integrated nuclear digital environments, from which digital twins can be spawned, we would like to capture both explicit and tacit knowledge about complex systems throughout their life cycle which will extend beyond the working lives of their designers, builders and operators.  One of the potential advantages of digital twins is as a knowledge management system by duplicating the life of the physical system and thus allowing its safer and cheaper operation in the long-term as well as its eventual decommissioning.   However, besides the very nature of tacit knowledge that makes its capture difficult, we are finding that its perceived value as an intellectual asset renders stakeholders reluctant to discuss it with us; never mind consider how it might be preserved as part of a digital twin.  Research has shown that tacit knowledge sharing is influenced by environmental factors including national culture, leadership characteristics and social networks [Cai et al, 2020].  I suspect that all of these factors were present in the heyday of the UK civil nuclear power industry when it worked together to construct advanced and complex systems; however, it has not built a power station since 1995 and, at the moment, new power stations are cancelled more often than built, which has almost certainly depressed all of these factors.  So, perhaps we should not be surprised by the difficulties encountered in establishing an integrated nuclear digital environment despite its importance for the future of the industry.

Reference: Cai, Y., Song, Y., Xiao, X. and Shi, W., 2020. The Effect of Social Capital on Tacit Knowledge-Sharing Intention: The Mediating Role of Employee Vigor. SAGE Open, 10(3), p.2158244020945722.

Physical actions to inhibit COVID-19 infection

Figure 4 from Ai & Melikov, 2017

Politicians in many countries are fond of claiming that they are following scientific advice when telling us what we can or cannot do in an effort to prevent the spread of the coronavirus, COVID-19.  However, neither they nor the journalists who report their statements tell us what scientists have actually established.  So, I have been reading some of the literature.

A paper by Leung et al [1] published this month in Nature Medicine reports that surgical face masks could prevent transmission of human coronavirus and influenza viruses from symptomatic individuals.  Their conclusions were based on a study of 246 individuals ranging in age from 11 to more than 65 years old of which 59% were female.  Sande et al [2] in 2008, found that any type of general mask is likely to decrease viral exposure and infection risk on a population level; with surgical masks being more effective than home-made masks and children being less well protected.  The relative ineffectiveness of fabrics used in home-made masks, including sweatshirts, T-shirts, towels and scarfs, was demonstrated in 2010 by Rengasamy et al [3], who found that these fabrics had 40-97% instantaneous penetration for monodisperse aerosol particles in the 20 to 1000 nm range.  While in the same year, Cowling et al [4] conducted a systematic review of the subject and concluded there was some evidence to support the wearing of masks or respirators during illness to protect others, and public health emphasis on mask wearing during illness may help reduce influenza virus transmission.  There were fewer data to support the use of masks or respirators to prevent becoming infected.  So, the rational conclusion appears to be that we should wear face masks to protect society as a whole and remember they do not necessarily protect us as individuals.

The emphasis on social distancing is causing widespread economic distress and also appears to be causing a decrease in mental health.  It perhaps should be called physical distancing because that is what we asked to do – to keep 2 m apart or 1.5 m in some places.  In 2017, a team of engineers from the University of Hong Kong and Aalborg University in Denmark [5], concluded that a threshold distance of 1.5 m distinguished between two basic transmission processes of droplets, i.e. a short-range mode and a long-range airborne route.  They reviewed the literature, conducted experiments and performed computational simulations before concluding the risk of infection arising from person-to-person interactions was significantly reduced when people were more than 1.5 m apart because droplets greater than 60 microns in diameter are not transmitted further than 1.5 m; however, smaller droplets are carried further.  In the same year, Ai & Melikov [6] reviewed the airborne spread of expiratory droplets in indoors environments; they found inconsistent results due to different boundary conditions used in computer models and the available instrumentation being too slow to provide accurate time-dependent measurements.  However, it would appear, based on several investigations, that the risk of cross-infection is decreased sharply at distances of 0.8 to 1.5 m (see graphic).  Indoors, the flow interactions in the human microenvironment dominate airborne transmission over short distances (<0.5 m) while the general ventilation flow is more important over longer distances.  Hence, at short distances, the posture and orientation of individuals is important; while at longer distances, if the rate of change of air in the room is high enough then the risk of cross-infection is low.

These findings would seem to suggest that there is some scope to balance restarting social and economic activity with protecting people from the coronavirus by relaxing ‘social’ distancing from 2 m to 1.5 m unless you are  wearing a mask.  After all, we would simply following the example of Taiwan where there are almost no new cases.

References

[1] Leung NH, Chu DK, Shiu EY, Chan KH, McDevitt JJ, Hau BJ, Yen HL, Li Y, Ip DK, Peiris JM, Seto WH. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nature Medicine. 2020 Apr 3:1-5.

[2] van der Sande M, Teunis P, Sabel R. Professional and home-made face masks reduce exposure to respiratory infections among the general population. PLoS One. 2008;3(7).

[3] Rengasamy S, Eimer B, Shaffer RE. Simple respiratory protection—evaluation of the filtration performance of cloth masks and common fabric materials against 20–1000 nm size particles. Annals of occupational hygiene. 2010 Oct 1;54(7):789-98.

[4] Cowling BJ, Zhou YD, Ip DK, Leung GM, Aiello AE. Face masks to prevent transmission of influenza virus: a systematic review. Epidemiology & Infection. 2010 Apr;138(4):449-56.

[5] Liu L, Li Y, Nielsen PV, Wei J, Jensen RL. Short‐range airborne transmission of expiratory droplets between two people. Indoor Air. 2017 Mar;27(2):452-62.

[6] Ai ZT, Melikov AK. Airborne spread of expiratory droplet nuclei between the occupants of indoor environments: A review. Indoor air. 2018 Jul;28(4):500-24.

Modelling from the cell through the individual to the host population

During the lock-down in the UK due to the coronavirus pandemic, I have been reading about viruses and the modelling of them.  It is a multi-disciplinary and multi-scale problem; so, something that engineers should be well-equipped to tackle.  It is a multi-scale because we need to understand the spread of the virus in the human population so that we can control it, we need to understand the process of infection in individuals so that we can protect them, and we need to understand the mechanisms of virus-cell interaction so that we can stop the replication of the virus.  At each size scale, models capable of representing the real-world processes will help us explore different approaches to arresting the progress of the virus and will need to be calibrated and validated against measurements.  This can be represented in the sort of model-test pyramid shown in the top graphic that has been used in the aerospace industry [1-2] for many years [see ‘Hierarchical modelling in engineering and biology’ on March 14th, 2018] and which we have recently introduced in the nuclear fission [3] and fusion [4] industries [see ‘Thought leadership in fusion engineering’ on October 9th, 2019].  At the top of the pyramid, the spread of the virus in the population is being modelled by epidemiologists, such as Professor Neil Ferguson [5], using statistical models based on infection data.  However, I am more interested in the bottom of the pyramid because the particles of the coronavirus are about the same size as the nanoparticles that I have been studying for some years [see ‘Slow moving nanoparticles’ on December 13th, 2017] and their motion appears to be dominated by diffusion processes [see ‘Salt increases nanoparticle diffusion’ on April 22nd, 2020] [6-7].  The first step towards virus infection of a cell is diffusion of the virus towards the cell which is believed to be a relatively slow process and hence a good model of diffusion would assist in designing drugs that could arrest or decelerate infection of cells [8].  Many types of virus on entering the cell make their way to the nucleus where they replicate causing the cell to die, afterwhich the virus progeny are dispersed to repeat the process.  You can see part of this sequence for coronavirus (SARS-COV-2) in this sequence of images. The trafficking across the cytoplasm of the cell to the nucleus can occur in a number of ways including the formation of a capsule or endosome that moves across the cell towards the nuclear membrane where the virus particles leave the endosome and travel through microtubules into the nucleus.  Holcman & Schuss [9] provide a good graphic illustrating these transport mechanisms.  In 2019, Briane et al [10] reviewed models of diffusion of intracellular particles inside living eukaryotic cells, i.e. cells with a nuclear enclosed by a membrane as in all animals.  Intracellular diffusion is believed to be driven by Brownian motion and by motor-proteins including dynein, kinesin and myosin that enable motion through microtubules.  They observed that the density of the structure of cytoplasm, or cytoskeleton, can hinder the free displacement of a particle leading to subdiffusion; while, cytoskeleton elasticity and thermal bending can accelerate it leading to superdiffusion.  These molecular and cellular interactions are happening at disparate spatial and temporal scales [11] which is one of the difficulties encountered in creating predictive simulations of virus-cell interactions.  In other words, the bottom layers of the model-test pyramid appear to be constructed from many more strata when you start to look more closely.  And, you need to add a time dimension to it.  Prior to the coronavirus pandemic, more modelling efforts were perhaps focussed on understanding the process of infection by Human Immunodeficiency Virus (HIV), including by a multi-national group of scientists from Chile, France, Morocco, Russia and Spain [12-14].  However, the current coronavirus pandemic is galvanising researchers who are starting to think about novel ways of building multiscale models that encourage multidisciplinary collaboration by dispersed groups, [e.g. 15].

References

[1] Harris GL, Computer models, laboratory simulators, and test ranges: meeting the challenge of estimating tactical force effectiveness in the 1980’s, US Army Command and General Staff College, May 1979.

[2] Trevisani DA & Sisti AF, Air Force hierarchy of models: a look inside the great pyramid, Proc. SPIE 4026, Enabling Technology for Simulation Science IV, 23 June 2000.

[3] Patterson EA, Taylor RJ & Bankhead M, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103, 2016.

[4] Patterson EA, Purdie S, Taylor RJ & Waldon C, An integrated digital framework for the design, build and operation of fusion power plants, Royal Society Open Science, 6(10):181847, 2019.

[5] Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, Ferguson NM, Estimates of the severity of coronavirus disease 2019: a model-based analysis., Lancet Infectious Diseases, 2020.

[6] Coglitore D, Edwardson SP, Macko P, Patterson EA, Whelan MP, Transition from fractional to classical Stokes-Einstein behaviour in simple fluids, Royal Society Open Science, 4:170507, 2017.

[7] Giorgi F, Coglitore D, Curran JM, Gilliland D, Macko P, Whelan M, Worth A & Patterson EA, The influence of inter-particle forces on diffusion at the nanoscale, Scientific Reports, 9:12689, 2019.

[8] Gilbert P-A, Kamen A, Bernier A & Garner A, A simple macroscopic model for the diffusion and adsorption kinetics of r-Adenovirus, Biotechnology & Bioengineering, 98(1):239-251,2007.

[9] Holcman D & Schuss Z, Modeling the early steps of viral infection in cells, Chapter 9 in Stochastic Narrow Escape in Molecular and Cellular Biology, New York: Springer Science+Business Media, 2015.

[10] Braine V, Vimond M & Kervrann C, An overview of diffusion models for intracellular dynamics analysis, Briefings in Bioinformatics, Oxford University Press, pp.1-15, 2019.

[11] Holcman D & Schuss Z, Time scale of diffusion in molecular and cellular biology, J. Physics A: Mathematical and Theoretical, 47:173001, 2014.

[12] Bocharov G, Chereshnev V, Gainov I, Bazhun S, Bachmetyev B, Argilaguet J, Martinez J & Meyerhans A, Human immunodeficiency virus infection: from biological observations to mechanistic mathematical modelling, Math. Model. Nat. Phenom., 7(5):78-104, 2012.

[13] Bocharov G, Meyerhans A, Bessonov N, Trofimchuk S & Volpert V, Spatiotemporal dynamics of virus infection spreading in tissues, PLOS One, 11(12):e)168576, 2016.

[14] Bouchnita A, Bocharov G, Meyerhans A & Volpert V, Towards a multiscale model of acute HIV infection, Computation, 5(6):5010006, 2017.

[15] Sego TJ, Aponte-Serrano JO, Ferrari-Gianlupi J, Heaps S, Quardokus EM & Glazier JA, A modular framework for multiscale spatial modeling of viral infection and immune respons in epithelial tissue, bioRxiv. 2020.