Tag Archives: power stations

The disrupting benefit of innovation

Most scientific and technical conferences include plenary speeches that are intended to set the agenda and to inspire conference delegates to think, innovate and collaborate.  Andrew Sherry, the Chief Scientist of the UK National Nuclear Laboratory (NNL) delivered a superb example last week at the NNL SciTec 2018 which was held at the Exhibition Centre Liverpool on the waterfront.  With his permission, I have stolen his title and one of his illustrations for this post.  He used a classic 2×2 matrix to illustrate different types of change: creative change in the newspaper industry that has constantly redeveloped its assets from manual type-setting and printing to on-line delivery via your phone or tablet; progressive change in the airline industry that has incrementally tested and adapted so that modern commercial aircraft look superficially the same as the first jet airliner but represent huge advances in economy and reliability; inventive change in Liverpool’s Albert Dock that was made redundant by container ships but has been reinvented as a residential, tourism and business district.  The fourth quadrant, he reserved for the civil nuclear industry in the UK which requires disruptive change because its core assets are threatened by the end-of-life closure of all existing plants and because its core activity, supplying electrical power, is threatened by cheaper alternatives.

At the end of last year, NNL brought together all the prime nuclear organisations in the UK with leaders from other sectors, including aerospace, construction, digital, medical, rail, robotics, satellite and ship building at the Royal Academy of Engineering to discuss the drivers of innovation.  They concluded that innovation is not just about technology, but that successful innovation is driven by five mutually dependent themes that are underpinned by enabling regulation:

  1. innovative technologies;
  2. culture & leadership;
  3. collaboration & supply chain;
  4. programme and risk management; and
  5. financing & commercial models.

SciTec’s focus was ‘Innovation through Collaboration’, i.e. tackling two of these themes, and Andrew tasked delegates to look outside their immediate circle for ideas, input and solutions [to the existential threats facing the nuclear industry] – my words in parentheses.

Innovative technology presents a potentially disruptive threat to all established activities and we ignore it at our peril.  Andrew’s speech was wake up call to an industry that has been innovating at an incremental scale and largely ignoring the disruptive potential of innovation.  Are you part of a similar industry?  Maybe it’s time to check out the threats to your industry’s assets and activities…

Sources:

Sherry AH, The disruptive benefit of innovation, NNL SciTec 2018 (including the graphic & title).

McGahan AM, How industries change, HBR, October 2004.

Getting smarter

A350 XWB passes Maximum Wing Bending test [from: http://www.airbus.com/galleries/photo-gallery%5D

Garbage in, garbage out (GIGO) is a perennial problem in computational simulations of engineering structures.  If the description of the geometry of the structure, the material behaviour, the loading conditions or the boundary conditions are incorrect (garbage in), then the simulation generates predictions that are wrong (garbage out), or least an unreliable representation of reality.  It is not easy to describe precisely the geometry, material, loading and environment of a complex structure, such as an aircraft or a powerstation; because, the complete description is either unavailable or too complicated.  Hence, modellers make assumptions about the unknown information and, or to simplify the description.  This means the predictions from the simulation have to be tested against reality in order to establish confidence in them – a process known as model validation [see my post entitled ‘Model validation‘ on September 18th, 2012].

It is good practice to design experiments specifically to generate data for model validation but it is expensive, especially when your structure is a huge passenger aircraft.  So naturally, you would like to extract as much information from each experiment as possible and to perform as few experiments as possible, whilst both ensuring predictions are reliable and providing confidence in them.  In other words, you have to be very smart about designing and conducting the experiments as well as performing the validation process.

Together with researchers at Empa in Zurich, the Industrial Systems Institute of the Athena Research Centre in Athens and Dantec Dynamics in Ulm, I am embarking on a new EU Horizon 2020 project to try and make us smarter about experiments and validation.  The project, known as MOTIVATE [Matrix Optimization for Testing by Interaction of Virtual and Test Environments (Grant Nr. 754660)], is funded through the Clean Sky 2 Joint Undertaking with Airbus acting as our topic manager to guide us towards an outcome that will be applicable in industry.  We held our kick-off meeting in Liverpool last week, which is why it is uppermost in my mind at the moment.  We have 36-months to get smarter on an industrial scale and demonstrate it in a full-scale test on an aircraft structure.  So, some sleepness nights ahead…

Bibliography:

 

ASME V&V 10-2006, Guide for verification & validation in computational solid mechanics, American Society of Mech. Engineers, New York, 2006.

European Committee for Standardisation (CEN), Validation of computational solid mechanics models, CEN Workshop Agreement, CWA 16799:2014 E.

Hack E & Lampeas G (Guest Editors) & Patterson EA (Editor), Special issue on advances in validation of computational mechanics models, J. Strain Analysis, 51 (1), 2016.

http://www.engineeringvalidation.org/

Can you trust your digital twin?

Author's digital twin?

Author’s digital twin?

There is about a 3% probability that you have a twin. About 32 in 1000 people are one of a pair of twins.  At the moment an even smaller number of us have a digital twin but this is the direction in which computational biomedicine is moving along with other fields.  For instance, soon all aircraft will have digital twins and most new nuclear power plants.  Digital twins are computational representations of individual members of a population, or fleet, in the case of aircraft and power plants.  For an engineering system, its computer-aided design (CAD) is the beginning of its twin, to which information is added from the quality assurance inspections before it leaves the factory and from non-destructive inspections during routine maintenance, as well as data acquired during service operations from health monitoring.  The result is an integrated model and database, which describes the condition and history of the system from conception to the present, that can be used to predict its response to anticipated changes in its environment, its remaining useful life or the impact of proposed modifications to its form and function. It is more challenging to create digital twins of ourselves because we don’t have original design drawings or direct access to the onboard health monitoring system but this is being worked on. However, digital twins are only useful if people believe in the behaviour or performance that they predict and are prepared to make decisions based on the predictions, in other words if the digital twins possess credibility.  Credibility appears to be like beauty because it is in eye of the beholder.  Most modellers believe that their models are both beautiful and credible, after all they are their ‘babies’, but unfortunately modellers are not usually the decision-makers who often have a different frame of reference and set of values.  In my group, one current line of research is to provide metrics and language that will assist in conveying confidence in the reliability of a digital twin to non-expert decision-makers and another is to create methodologies for evaluating the evidence prior to making a decision.  The approach is different depending on the extent to which the underlying models are principled, i.e. based on the laws of science, and can be tested using observations from the real world.  In practice, even with principled, testable models, a digital twin will never be an identical twin and hence there will always be some uncertainty so that decisions remain a matter of judgement based on a sound understanding of the best available evidence – so you are always likely to need advice from a friendly engineer   🙂

Sources:

De Lange, C., 2014, Meet your unborn child – before it’s conceived, New Scientist, 12 April 2014, p.8.

Glaessgen, E.H., & Stargel, D.S., 2012, The digital twin paradigm for future NASA and US Air Force vehicles, Proc 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA paper 2012-2018, NF1676L-13293.

Patterson E.A., Feligiotti, M. & Hack, E., 2013, On the integration of validation, quality assurance and non-destructive evaluation, J. Strain Analysis, 48(1):48-59.

Patterson, E.A., Taylor, R.J. & Bankhead, M., 2016, A framework for an integrated nuclear digital environment, Progress in Nuclear Energy, 87:97-103.

Patterson EA & Whelan MP, 2016, A framework to establish credibility of computational models in biology, Progress in Biophysics & Molecular Biology, doi: 10.1016/j.pbiomolbio.2016.08.007.

Tuegel, E.J., 2012, The airframe digital twin: some challenges to realization, Proc 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.

Happenstance, not engineering?

okemos-art-2extract

A few weeks ago I wrote that ‘engineering is all about ingenuity‘ [post on September 14th, 2016] and pointed out that while some engineers are involved in designing, manufacturing and maintaining engines, most of us are not.  So, besides being ingenious, what do the rest of us do?  Well, most of us contribute in some way to the conception, building and sustaining of networks.  Communication networks, food supply networks, power networks, transport networks, networks of coastal defences, networks of oil rigs, refineries and service stations, or networks of mines, smelting works and factories that make everything from bicycles to xylophones.  The list is endless in our highly networked society.  A network is a group of interconnected things or people.  And, engineers are responsible for all of the nodes in our networks of things and for just about all the connections in our networks of both things and people.

Engineers have been constructing networks by building nodes and connecting them for thousands of years, for instance the ancient Mesopotamians were building aqueducts to connect their towns with distance water supplies more than four millenia ago.

Engineered networks are so ubiquitous that no one notices them until something goes wrong, which means engineers tend to get blamed more than praised.  But apparently that is the fault of the ultimate network: the human brain.  Recent research has shown that blame and praise are assigned by different mechanisms in the brain and that blame can be assigned by every location in the brain responsible for emotion whereas praise comes only from a single location responsible for logical thought.  So, we blame more frequently than we praise and we tend to assume that bad things are deliberate while good things are happenstance.  So reliable networks are happenstance rather than good engineering in the eyes of most people!

Sources:

Ngo L, Kelly M, Coutlee CG, Carter RM , Sinnott-Armstrong W & Huettel SA, Two distinct moral mechanisms for ascribing and denying intentionality, Scientific Reports, 5:17390, 2015.

Bruek H, Human brains are wired to blame rather than to praise, Fortune, December 4th 2015.