Tag Archives: innovation

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.


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:




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

Our last DIMES

Photograph of wing test in AWICThirty-three months ago (see ‘Finding DIMES‘ on February 6th, 2019) we set off at a gallop ‘to develop and demonstrate an automated measurement system that integrates a range of measurement approaches to enable damage and cracks to be detected and monitored as they originate at multi-material interfaces in an aircraft assembly’. The quotation is taken directly from the aim of the DIMES project which was originally planned and funded as a two-year research programme. Our research, in particular the demonstration element, has been slowed down by the pandemic and we resorted to two no-cost extensions, initially for three months and then for six months to achieve the project aim.   Two weeks ago, we held our final review meeting, and this week we will present our latest results in the third of a series of annual workshops hosted by Airbus, the project’s topic manager.   The DIMES system combines visual and infrared cameras with resistance strain gauges and fibre Bragg gratings to detect 1 mm cracks in metals and damage indications in composites that are only 6 mm in diameter.  We had a concept design by April 2019 (see ‘Joining the dots‘ on July 10th, 2019) and a detailed design by August 2019.  Airbus supplied us with a section of A320 wing, and we built a test-bench at Empa in Zurich in which we installed our prototype measurement system in the last quarter of 2019 (see ‘When seeing nothing is a success‘ on December 11th, 2019).  Then, the pandemic intervened and we did not finish testing until May 2021 by which time, we had also evaluated it for monitoring damage in composite A350 fuselage panels (see ‘Noisy progressive failure of a composite panel‘ on June 30th, 2021).  In parallel, we have installed our ‘DIMES system’ in ground tests on an aircraft wing at Airbus in Filton (see image) and, using a remote installation, in a cockpit at Airbus in Toulouse (see ‘Most valued player performs remote installation‘ on December 2nd, 2020), as well as an aircraft at NRC Aerospace in Ottawa (see ‘An upside to lockdown‘ on April 14th 2021).   Our innovative technology allows condition-led monitoring based on automated damage detection and enables ground tests on aircraft structures to be run 24/7 saving about 3 months on each year-long test.

The University of Liverpool is the coordinator of the DIMES project and the other partners are Empa, Dantec Dynamics GmbH and Strain Solutions Ltd.

The DIMES 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. 820951.

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.