Category Archives: electrical engineering

Innovative design too far ahead of the market?

computer rendering of street with kerbstones fitted for chraging electric vehiclesThe forthcoming COP26 conference in Glasgow is generating much discussion about ambitions to achieve net zero carbon emissions. These ambitions tend to be articulated by national governments or corporate leaders and there is less attention paid to the details of achieving zero emissions at the mundane level of everyday life. For instance, how to recharge an electric car if you live in an apartment building or a terraced house without a designated parking space. About six years ago, I supervised an undergraduate engineering student who designed an induction pad integrated into a kerbstone for an electric vehicle.  The kerbstone looked the same as a conventional one, which it could replace, but was connected to the mains electricity supply under the pavement.  A primary coil was integrated into the kerbstone and a secondary coil was incorporated into the side skirt of the vehicle, which could be lowered towards the kerbstone when the vehicle was parked.  The energy transferred from the primary coil in the kerbstone to the secondary coil in the vehicle via a magnetic field that conformed to radiation safety limits for household appliances.  Payment for charging was via a passive RFID card that connected to an app on your mobile phone.  The student presented her design at the Future Powertrain Conference (FCP 2015)  where her poster won first prize and we discussed spinning out a company to develop, manufacture and market the design.  However, a blue-chip engineering company offered the student a good job and we decided that the design was probably ahead of its time so it has remained on the drawing board.  Our technopy, or technology entropy was too high, we were ahead of the rate of change in the marketplace and launching a new product in these conditions can be disastrous.  Maybe the market is catching up with our design?

For more on technopy see Handscombe RD and Patterson EA ‘The Entropy Vector: Connecting Science and Business‘, World Scientific, Singapore, 2004.

 

 

 

 

Million to one

‘All models are wrong, but some are useful’ is a quote, usually attributed to George Box, that is often cited in the context of computer models and simulations.  Working out which models are useful can be difficult and it is essential to get it right when a model is to be used to design an aircraft, support the safety case for a nuclear power station or inform regulatory risk assessment on a new chemical.  One way to identify a useful model to assess its predictions against measurements made in the real-world [see ‘Model validation’ on September 18th, 2012].  Many people have worked on validation metrics that allow predicted and measured signals to be compared; and, some result in a statement of the probability that the predicted and measured signal belong to the same population.  This works well if the predictions and measurements are, for example, the temperature measured at a single weather station over a period of time; however, these validation metrics cannot handle fields of data, for instance the map of temperature, measured with an infrared camera, in a power station during start-up.  We have been working on resolving this issue and we have recently published a paper on ‘A probabilistic metric for the validation of computational models’.  We reduce the dimensionality of a field of data, represented by values in a matrix, to a vector using orthogonal decomposition [see ‘Recognizing strain’ on October 28th, 2015].  The data field could be a map of temperature, the strain field in an aircraft wing or the topology of a landscape – it does not matter.  The decomposition is performed separately and identically on the predicted and measured data fields to create to two vectors – one each for the predictions and measurements.  We look at the differences in these two vectors and compare them against the uncertainty in the measurements to arrive at a probability that the predictions belong to the same population as the measurements.  There are subtleties in the process that I have omitted but essentially, we can take two data fields composed of millions of values and arrive at a single number to describe the usefulness of the model’s predictions.

Our paper was published by the Royal Society with a press release but in the same week as the proposed Brexit agreement and so I would like to think that it was ignored due to the overwhelming interest in the political storm around Brexit rather than its esoteric nature.

Source:

Dvurecenska K, Graham S, Patelli E & Patterson EA, A probabilistic metric for the validation of computational models, Royal Society Open Science, 5:1180687, 2018.

Happenstance, not engineering?

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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.

 

Electron uncertainty

daisyMost of us are uncomfortable with uncertainty.  Michael Faraday’s ability to ‘accept the given – certainties and uncertainties’ [see my post entitled ‘Steadiness and placidity’ on July 18th, 2016] was exceptional and perhaps is one reason he was able to make such outstanding contributions to science and engineering.  It has been said that his ‘Expts. on the production of Electricity from Magnetism, etc. etc.’ [Note 148 from Faraday’s notebooks] on August 29th 1831  began the age of electricity.  Electricity is associated with the flow of electric charge, which is often equated with the flow of electrons and electrons are subatomic particles with a negative elementary charge and a mass that is approximately 1/1836 atomic mass units.  A moving electron, and it is difficult to find a stationary one, has wave-particle duality – that is, it simultaneously has the characteristics of a particle and a wave.  So, there is uncertainty about the nature of an electron and most of us find this concept difficult to handle.

An electron is both matter and energy.  It is a particle in its materialisation as matter but a wave in its incarnation as energy.  However, this is probably too much of a reductionist description of a systemic phenomenon.  Nevertheless let’s stay with it for a moment, because it might help elucidate why the method of measurement employed in experiments with electrons influences whether our measurements reflect the behaviour of a particle or a wave.  Perhaps when we design our experiments from an energy perspective then electrons oblige by behaving as waves of energy and when we design from a matter perspective then electrons materialise as particles.

All of this leads to a pair of questions about what is matter and what is energy?  But, these are enormous questions, and even the Nobel Laureate Richard Feynman said ‘in physics today, we have no knowledge of what energy is’, so I’m going to leave them unanswered.  I’ve probably already riled enough physicists with my simplistic discussion.

Note: an atomic mass unit is also known as a Dalton and is equivalent to 1.66×10-27kg

Source:

Hamilton, J., A life of discovery: Michael Faraday, giant of the scientific revolution. New York: Random House, 2002.

Pielou EC, The Energy of Nature [the epilogue], Chicago: The University of Chicago Press, 2001.