Tag Archives: Engineering

Conversations about engineering over dinner and a haircut

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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.

Happy New Year!

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Decorative photograph of sculpture of a skeletal person leading a skeletal dinosaurThis year I have written about 20,000 words in 52 posts (including this one); and, since this is the last post of the year, I thought I would take a brief look back at what has preoccupied me in 2021.  Perhaps, not surprisingly the impact of the coronavirus on our lifestyle has featured regularly – almost every week for a month between mid-March and mid-April when we were in lockdown in the UK.  However, the other topics that I have written about frequently are my research on the dynamics of nanoparticles and, in the last six months, on dealing with uncertainty in digital engineering and decision making.  I have also returned several times to innovation processes and transitioning lab-based research into industry.  While following the COP26 in early November, I wrote a series of three posts focussed on energy consumption and the paradigm shifts required to slow down climate change.  There are some connections between these topics: viruses are nanoparticles whose transport and dynamics we do not fully understand; and, digital engineering tools are being used to explore zero-carbon approaches to, for example, energy generation and air transport.  The level of complexity, innovation and urgency associated with developing solutions to these challenges mean that there are always some unknowns and uncertainty when making associated decisions.

The links below are grouped by the topics mentioned above.  I expect there will be more on all of these topics in 2022; however, the topic of next week’s post is unknown because I have not written any posts in advance.  I hope that the uncertainty about the topic of the next post will keep you reading in 2022! 

Coronavirus pandemic: ‘Distancing ourselves from each other‘ on January 13th, 2021; ‘On the impact of writing on well-being‘ on March 3rd, 2021; ‘Collegiality as a defence against pandemic burnout‘ on March 24th, 2021; ‘It’s tiring looking at yourself‘ on March 31st, 2021; ‘Switching off and walking in circles‘ on April 7th, 2021; ‘An upside to lockdown‘ on April 14th, 2021; ‘A brief respite in a long campaign to overcome coronavirus‘ on June 23rd, 2021; and ‘It is hard to remain positive‘ November 3rd 2021.

Energy and climate change: ‘When you invent the ship, you invent the shipwreck‘ on August 25th, 2021; ‘It is hard to remain positive‘ November 3rd 2021; ‘Where we are and what we have‘ on November 24th, 2021; ‘Disruptive change required to avoid existential threats‘ on December 1st, 2021; and ‘Bringing an end to thermodynamic whoopee‘ on December 8th, 2021.

Innovation processes: ‘Slowly crossing the valley of death‘ on January 27th, 2021; ‘Out of the valley of death into a hype cycle?‘ on February 24th, 2021; ‘Innovative design too far ahead of the market?‘ on May 5th, 2021 and ‘Jigsaw puzzling without a picture‘ on October 27th, 2021.

Nanoparticles: ‘Going against the flow‘ on February 3rd, 2021; ‘Seeing things with nanoparticles‘ on March 10th, 2021; and ‘Nano biomechanical engineering of agent delivery to cells‘ on December 15th, 2021.

Uncertainty: ‘Certainty is unattainable and near-certainty is unaffordable‘ on May 12th, 2021; ‘Neat earth objects make tomorrow a little less than certain‘ on May 26th, 2021; ‘Negative capability and optimal ambiguity‘ on July 7th, 2021; ‘Deep uncertainty and meta ignorance‘ on July 21st, 2021; ‘Somethings will always be unknown‘ on August 18th, 2021; ‘Jigsaw puzzling without a picture‘ on October 27th, 2021; and, ‘Do you know RIO?‘ on November 17th, 2021.

Nano biomechanical engineering of agent delivery to cells

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figure 1 from [1] with text explanationWhile many of us are being jabbed in the arm to deliver an agent that stimulates our immune system to recognize the coronavirus SARS-CoV-2 as a threat and destroy it, my research group has been working, in collaboration with colleagues at the European Commission Joint Research Centre, on the dynamics of nanoparticles [1] [see ‘Size matters‘ on October 23rd, 2019] which could be used as carriers for the targeted delivery of therapeutic, diagnostic and imaging agents in the human body [2].  The use of nanoparticles to mechanically stimulate stem cells to activate signalling pathways and modulate their differentiation also has some potential [3]. In studies of the efficacy of nanoparticles in these biomedical applications, the concentration of nanoparticles interacting with the cell is a primary factor influencing both the positive and negative effects.  Such studies often involve exposing a monolayer of cultured cells adhered to the bottom of container to a dose of nanoparticles and monitoring the response over a period of time.  Often, the nominal concentration of the nanoparticles in biological medium supporting the cells is reported and used as the basis for determining the dose-response relationships.  However, we have shown that this approach is inaccurate and leads to misleading results because the nanoparticles in solution are subject to sedimentation due to gravity, Brownian motion [see ‘Slow moving nanoparticles‘ on December 13th, 2017] and inter-particle forces [see ‘ Going against the flow‘ on February 3rd, 2021] which affect their transport within the medium [see graphic] and the resultant concentration adjacent to the monolayer of cells.  Our experimental results using the optical method of caustics [see ‘Holes in fluids‘ on October 22nd, 2014] have shown that nanoparticle size, colloidal stability and solution temperature influence the distribution of nanoparticles in solution.  For particles larger than 60 nm in diameter (about one thousandth of the diameter of a human hair) the nominal dose differs significantly from the dose experienced by the cells.  We have developed and tested a theoretical model that accurately describes the settling dynamics and concentration profile of nanoparticles in solution which can be used to design in vitro experiments and compute dose-response relationships.

References

[1] Giorgi F, Macko P, Curran JM, Whelan M, Worth A & Patterson EA. 2021 Settling dynamics of nanoparticles in simple and biological media. Royal Society Open Science, 8:210068.

[2] Daraee H, Eatemadi A, Abbasi E, Aval SF, Kouhi M, & Akbarzadeh A. 2016 Application of gold nanoparticles in biomedical and drug delivery. Artif. Cells Nanomed. Biotechnol. 44, 410–422. (doi:10.3109/21691401.2014.955107)

[3] Wei M, Li S, & Le W. 2017 Nanomaterials modulate stem cell differentiation: biological
interaction and underlying mechanisms. J. Nanobiotechnol. 15, 75. (doi:10.1186/s12951-
017-0310-5)

Jigsaw puzzling without a picture

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A350 XWB passes Maximum Wing Bending test

A350 XWB passes Maximum Wing Bending test

Research sometimes feels like putting together a jigsaw puzzle without the picture or being sure you have all of the pieces.  The pieces we are trying to fit together at the moment are (i) image decomposition of strain fields [see ‘Recognising strain’ on October 28th 2015] that allows fields containing millions of data values to be represented by a feature vector with only tens of elements which is useful for comparing maps or fields of predictions from a computational model with measurements made in the real-world; (ii) evaluation of the variation in measurement uncertainty over a field of view of measured displacements or strains in a large structure [see ‘Industrial uncertainty’ on December 12th 2018] which provides information about the quality of the measurements; and (iii) a probabilistic validation metric that provides a measure of how well predictions from a computational model represent measurements made in the real world [see ‘Million to one’ on November 21st 2018].  We have found some of the missing pieces of the jigsaw, for example we have established how to represent the distribution of measurement uncertainty in the feature vector domain [see ‘From strain measurements to assessing El Niño events’ on March 17th 2021] so that it can be used to assess the significance of differences between measurements and predictions represented by their feature vectors – this connects (i) and (ii) together.  Very recently we have demonstrated a generic technique for performing image decomposition of irregularly shaped fields of data or data fields with holes [see Christian et al, 2021] which extends the applicability of our method for comparing measurements and predictions to real-world objects rather than idealised shapes.  This allows (i) to be used in industrial applications but we still have to work out how to connect this to the probabilistic metric in (iii) while also incorporating spatially-varying uncertainty.  These techniques can be used in a wide range of applications, as demonstrated in our recent work on El Niño events [see Alexiadis et al, 2021], because, by treating all fields of data as images, the techniques are agnostic about the source and format of the data.  However, at the moment, our main focus is on their application to ground tests on aircraft structures as part of the Smarter Testing project in collaboration with Airbus, Centre for Modelling & Simulation, Dassault Systèmes, GOM UK Ltd, and the National Physical Laboratory with funding from the Aerospace Technology Institute.  Together we are working towards digital continuity across virtual and physical testing of aircraft structures to provide live data fusion and enable condition-led inspections, test control and validation of computational models.  We anticipate these advances will reduce time and costs for physical tests and accelerate the development of new designs of aircraft that will contribute to global sustainability targets (the aerospace industry has committed to reduce CO2 emissions to 50% of 2005 levels by 2050).  The Smarter Testing project has an ambitious goal which reveals that our pieces of the jigsaw puzzle belong to a small section of a much larger one.

For more on the Smarter Testing project see:

https://www.aerospacetestinginternational.com/news/structural-testing/smarter-testing-research-program-to-link-virtual-and-physical-aerospace-testing.html

https://www.aerospacetestinginternational.com/opinion/how-integrating-the-virtual-and-physical-will-make-aerospace-testing-and-certification-smarter.html

References

Alexiadis A, Ferson S, Patterson EA. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society open science. 8(3):201086, 2021.

Christian WJ, Dean AD, Dvurecenska K, Middleton CA, Patterson EA. Comparing full-field data from structural components with complicated geometries. Royal Society open science. 8(9):210916, 2021.

Image: http://www.airbus.com/galleries/photo-gallery