Tag Archives: Engineering

Reduction in usefulness of reductionism

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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.

Slowly crossing the valley of death

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A view of a valleyThe valley of death in technology development is well-known amongst research engineers and their sponsors. It is the gap between discovery and application, or between realization of an idea in a laboratory and its implementation in the real-world. Some of my research has made it across the valley of death, for example the poleidoscope about 15 years ago (see ‘Poleidoscope (=polariscope+kaleidoscope)‘ on October 14th, 2020).  Our work on quantitative comparisons of data fields from physical measurements and computer predictions is about three-quarters of the way across the valley.  We published a paper in December (see Dvurecenska et al, 2020) on its application to a large panel from the fuselage of an aircraft based on work we completed as part of the MOTIVATE project.  I reported the application of the research in almost real-time in a post in December 2018 (see ‘Industrial Uncertainty‘ on December 12th, 2018) and in further detail in May 2020 as we submitted the manuscript for publication (‘Alleviating industrial uncertainty‘ on May 13th, 2020).  However, the realization in the laboratory occurred nearly a decade ago when teams from Michigan State University and the University of Liverpool came together in the ADVISE project funded by EU Framework 7 programme (see Wang et al, 2011). Subsequently, the team at Michigan State University moved to the University of Liverpool and in collaboration with researchers at Empa developed the technique that was applied in the MOTIVATE project (see Sebastian et al 2013). The work published in December represents a step into the valley of death; from a university environment into a full-scale test laboratory at Empa using a real piece of aircraft.  The MOTIVATE project involved a further step to a demonstration on an on-going test of a cockpit at Airbus which was also reported in a post last May (see ‘The blind leading the blind‘ on May 27th, 2020).  We are now working with Airbus in a new programme to embed the process of quantitative comparison of fields of measurements and predictions into their routine test procedures for aerospace structures.  So, I would like to think we are climbing out of the valley.

Image: not Death Valley but taken on a road trip in 2008 somewhere between Moab, UT and Kanab, UT while living in Okemos, MI.

Sources:

Dvurecenska, K., Diamantakos, I., Hack, E., Lampeas, G., Patterson, E.A. and Siebert, T., 2020. The validation of a full-field deformation analysis of an aircraft panel: A case study. The Journal of Strain Analysis for Engineering Design, p.0309324720971140.

Sebastian, C., Hack, E. and Patterson, E., 2013. An approach to the validation of computational solid mechanics models for strain analysis. The Journal of Strain Analysis for Engineering Design, 48(1), pp.36-47.

Wang, W., Mottershead, J.E., Sebastian, C.M. and Patterson, E.A., 2011. Shape features and finite element model updating from full-field strain data. International Journal of Solids and Structures, 48(11-12), pp.1644-1657.

For more posts on the MOTIVATE project: https://realizeengineering.blog/category/myresearch/motivate-project/

The MOTIVATE project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 754660 and the Swiss State Secretariat for Education, Research and Innovation under contract number 17.00064.

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

We are drowning in information while starving for wisdom

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Decorative image: Lake Maggiore from AngeraThe title of this post is a quote from Edward O. Wilson’s book ‘Consilience: The Unity of Knowledge‘. For example, if you search for scientific papers about “Entropy” then you will probably find more than 3.5 million. An impossible quantity for an individual to read and even when you narrow the search to those about “psychological entropy”, which is a fairly niche topic, you will still find nearly 500 papers – a challenging reading list for most people.  The analysis of the trends embedded in scientific papers has become a research activity in its own right, see for example Basurto-Flores et al 2018 on papers about entropy; however, this type of analysis seems to generate yet more information rather than wisdom.  In this context, wisdom is associated with insight based on knowledge and experience; however the quality of the experiences is important as well as the processes of self-reflection (see Nicholas Weststrate’s PhD thesis).  There are no prizes for wisdom and we appoint and promote researchers based on their publication record; hence it is unsurprising that editors of journals are swamped by thousands of manuscripts submitted for publication with more than 2 million papers published every year.  The system is out of control driven by authors building a publication list longer than their competitors for jobs, promotion and grant funding and by publishers seeking larger profits from publishing more and bigger journals.  There are so many manuscripts submitted to journals that the quality of the reviewing and editing is declining leading to both false positive and false negatives, i.e. papers being published that contain little, if any, original content or lacking sufficient evidence to support their conclusions  and highly innovative papers being rejected because they are perceived to be wrong rather than simply deviating from the current paradigm. The drop in quality and rise in quantity of papers published makes keeping up with the scientific literature both expensive and inefficient in terms of time and energy, which slows down acquisition of knowledge and leaves less time for reflection and gaining experiences that are prerequisites for wisdom. So what incentives are there for a scientist or engineer to aspire to be wise given the lack of prizes and career rewards for wisdom?  In Chinese thought wisdom is perceived as expertise in the art of living, the ability to grasp what is happening, and to adjust to the imminent future (Simandan, 2018).  All of these attributes seem to be advantageous to a career based on solving problems but you need the sagacity to realise that the rewards are indirect and often intangible.

References:

Basurto-Flores, R., Guzmán-Vargas, L., Velasco, S., Medina, A. and Hernandez, A.C., 2018. On entropy research analysis: cross-disciplinary knowledge transfer. Scientometrics, 117(1), pp.123-139.

Simandan, D., 2018. Wisdom and foresight in Chinese thought: sensing the immediate future. Journal of Futures Studies, 22(3), pp.35-50.

Nicholas M Weststrate, The examined life: relations amoong life experience, self-reflection and wisdom, PhD Thesis, University of Toronto, 2017.

Edward O. Wilson, Consilience: the unity of knowledge, London, Little Brown and Company, 1998.

Predicting the future through holistic awareness

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Decorative picture: view along hotel corridorIt is traditional at the start of the year to speculate on what will happen in the new year.  However, as Niels Bohr is reputed to have said ‘Prediction is very difficult, especially about the future’.  Some people have suggested that our brains are constantly predicting the future. We weigh up the options for what might happen next before choosing a course of action. Our ancestors might have watched a fish swimming near a river bank and predicted where it would be a moment later when their spear entered the water. Or on a longer timescale, they predicted that seeds planted at a particular time of year would yield a crop some months later. Our predictions are not always correct but our life depends on enough of them being reliable that we have evolved to be good predictors of the immediate future. In Chinese thought, a distinction is made between predicting the near and distant future because the former is possible and latter is impossible, at least with any degree of confidence (Simandon, 2018).  Wisdom can be considered to be understanding the futility of trying to predict the distant future while being able to sense the near future through an acute awareness and immersion in one’s surroundings. This implies that a wise person can go beyond the everyday predictions of the immediate future, made largely unconsciously by our brains, and anticipate events on a slightly longer timescale, the near future. In engineering terms, events in the near future are short-term behaviour dominated by the current status of the system whereas events in the distant future are largely determined by external interactions with the system. This seems entirely consistent with the Chinese concept of wisdom arising from ‘vanishing into things’ which means to become immersed in a situation and hence to be able sense the current status of the system and reliably anticipate the near future. Some engineers might call it intuition which has been defined as ‘judgments that arise through rapid, non-conscious and holistic associations’ (Dane & Pratt, 2007).  So, in 2021 I hope to continue to exercise my intuition and remain immersed in a number of issues but I am not going to attempt to predict any distant events.

References:

Dane, E. and Pratt, M.G., 2007. Exploring intuition and its role in managerial decision making. Academy of management review, 32(1), pp.33-54.

Simandan, D., 2018. Wisdom and foresight in Chinese thought: sensing the immediate future. Journal of Futures Studies, 22(3), pp.35-50.