Tag Archives: innovation

Happy New Year!

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.

Disruptive change required to avoid existential threats

Decorative ink drawing by Zahrah Resh 2005It is easy for ideas or plans for transformational change to transition into transactional processes that deliver only incremental change.  Transformational change is about major shifts in culture, strategy or technology that causes substantial alterations in structure, organisation, behaviour and performance; whereas transactional changes occur within the existing structure and organisation.  Leading transformational change is hard and requires courage, vision, a willingness to listen to all stakeholders, decisiveness and communication, i.e. procedural justice and fair processes [see ‘Advice to abbots and other leaders‘ on November 13th, 2019].  If any of these components are absent, especially courage, vision and decisiveness, then transformational change can transition to a transactional process with incremental outcomes.  When the need to change becomes urgent due to existential threats, the focus should be on disruptive change [see ‘The disruptive benefit of innovation‘ on May 23rd 2018] but there is a tendency to avoid  such transformations and retreat into transactional processes that provide the illusion of progress.  Perhaps this is because transformational change requires leaders to be selfless, courageous and to do the right thing not just the easy thing [see ‘Inspirational leadership‘ on March 22nd, 2017]; whereas transactional processes occur within existing frameworks and hence minimise psychological entropy and stress [see ‘Psychological entropy increased by ineffectual leaders‘ on February 10th, 2021].  This tendency to avoid disruptive change happens at all levels in society from individual decisions about lifestyle, through product development in companies, to global conferences on climate change [see ‘Where we are and what we have‘ on November 24th, 2021].

Image: Ink drawing by Zahrah Resh, 2005. See ‘Seasons Greetings in 2020‘ on December 23rd, 2020.

Acknowledgement: thank you to a regular reader of this blog for the stimulating this post with a comment about transformational change left to the last minute becoming transactional.

 

Follow your gut

Decorative image of a fruit fly nervous system Albert Cardona HHMI Janelia Research Campus Welcome Image Awards 2015Data centres worldwide consume about 1% of global electricity generation, that’s 200-250 TWh (Masenet et al, 2020), and if you add in mining of cryptocurrencies then consumption jumps by about 50% (Gallersdörfer et al, 2020). Data transmission consumes about 260-340 TWh or at least another 1% of global energy consumption (IEA, 2020).  The energy efficiency of modern computers has been improving; however, their consumption is still many millions times greater than the theoretical limit defined by Landauer’s principle which was verified in 2012 by Bérut et al.  According to Landauer’s principle, a computer operating at room temperature would only need 3 zJ (300 billion billionths of a Joule) to erase a bit of information.  The quantity of energy used by modern computers is many millions times the Landauer limit.  Of course, progress is being made almost continuously, for example a team at EPFL in Lausanne and ETH Zurich recently described a new technology that uses only a tenth of the energy of current transistors (Oliva et al 2020).  Perhaps we need turn to biomimetics because Escherichia Coli, which are bacteria that live in our gut and have to process information to reproduce, have been found to use ten thousand times less energy to process a bit of information than the average human-built device for processing information (Zhirnov & Cavin, 2013).  So, E.coli are still some way from the Landauer limit but demonstrate that there is considerable potential for improvement in engineered devices.

References

Bérut A, Arakelyan A, Petrosyan A, Ciliberto S, Dillenschneider R & Lutz E. Experimental verification of Landauer’s principle linking information and thermodynamics. Nature, 483: 187–189, 2012.

IEA (2021), Data Centres and Data Transmission Networks, IEA, Paris https://www.iea.org/reports/data-centres-and-data-transmission-networks

Gallersdörfer U, Klaaßen L, Stoll C. Energy consumption of cryptocurrencies beyond bitcoin. Joule. 4(9):1843-6, 2020.

Masanet E, Shehabi A, Lei N, Smith S, Koomey J. Recalibrating global data center energy-use estimates. Science. 367(6481):984-6, 2020.

Oliva N, Backman J, Capua L, Cavalieri M, Luisier M, Ionescu AM. WSe 2/SnSe 2 vdW heterojunction Tunnel FET with subthermionic characteristic and MOSFET co-integrated on same WSe 2 flake. npj 2D Materials and Applications. 4(1):1-8, 2020.

Zhirnov VV, Cavin RK. Future microsystems for information processing: limits and lessons from the living systems. IEEE Journal of the Electron Devices Society. 1(2):29-47, 2013.

Jigsaw puzzling without a picture

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