Last month was #NoMowMay during which we were encouraged to let the grass grow and allow bees, butterflies and other wildlife to thrive unmolested by your lawnmower. Our townhouse in the centre of Liverpool does not have enough space for a lawn so I have not mown a lawn since we moved here from the USA nearly a decade ago. In the USA we followed the convention and maintained our front lawn as manicured green carpet by watering daily, mowing weekly and feeding it monthly during the summer. An automatic sprinkler system looked after the watering and a lawn service provided monthly doses of chemicals; however, we walked up and down behind the lawnmower each week. Much to my disappointment, our garden was not really large enough to justify a garden tractor or sit-on mower which has been a dream since I learnt my first self-taught engineering by ‘repairing’ my father’s green ATCO lawnmower when I was about 10 or 12. I was not allowed lift the bonnet or hood of the family car; and so as the only other piece of mechanical engineering in the garage that has an engine, the lawnmower became the focus of my attention. I suspect that old lawnmower did not run any better as a result of my ministrations but I certainly understood how an internal combustion engine worked by the time I went to university. I am an enthusiastic supporter of letting the grass grow, perhaps with a mown pathway so that the lawnmower has to be re-assembled periodically by whichever budding engineer has dismantled your lawnmower.
Digital twins are becoming ubiquitous in many areas of engineering [see ‘Can you trust your digital twin?‘ on November 23rd, 2016]. Although at the same time, the terminology is becoming blurred as digital shadows and digital models are treated as if they are synonymous with digital twins. A digital model is a digitised replica of physical entity which lacks any automatic data exchange between the entity and its replica. A digital shadow is the digital representation of a physical object with a one-way flow of information from the object to its representation. But a digital twin is a functional representation with a live feedback loop to its counterpart in the real-world. The feedback loop is based on a continuous update to the digital twin about the condition and performance of the physical entity based on data from sensors and on analysis from the digital twin about the performance of the physical entity. This enables a digital twin to provide a service to many stakeholders. For example, the users of a digital twin of an aircraft engine could include the manufacturer, the operator, the maintenance providers and the insurers. These capabilities imply digital twins are themselves becoming products which exist in a digital context that might connect many digital products thus forming an integrated digital environment. I wrote about integrated digital environments when they were a concept and the primary challenges were technical in nature [see ‘Enabling or disruptive technology for nuclear engineering?‘ on January 28th, 2015]. Many of these technical challenges have been resolved and the next set of challenges are economic and commercial ones associated with launching digital twins into global markets that lack adequate understanding, legislation, security, regulation or governance for digital products. In collaboration with my colleagues at the Virtual Engineering Centre, we have recently published a white paper, entitled ‘Transforming digital twins into digital products that thrive in the real world‘ that reviews these issues and identifies the need to establish digital contexts that embrace the social, economic and technical requirements for the appropriate use of digital twins [see ‘Digital twins could put at risk what it means to be human‘ on November 18th, 2020].
Cars that run on air might seem like a fairy tale or an April Fools story; but it is possible to use air as a medium for storing energy by compressing it or liquifying it at -196°C. The MDI company in Luxembourg has been developing and building a compressed air engine which powers a small car, or Airpod 2.0 and a new industrial vehicle, the Air‘Volution. When the compressed air is allowed to expand, the energy stored in it is released and can be used to power the vehicle. The Airpod 2.0 weighs only 350 kg, has seats for two people, 400 litres of luggage space and an urban cycle range of 100 to 120 km at a top speed of 80 km/h. So, it is an urban runabout with zero emissions and no requirement for lithium, nickel or cobalt for batteries but a limited range. A couple of years ago I tasked an MSc student with a project to consider the practicalities of a car running on liquid air, based on the premise that it should be possible to store a higher density of energy in liquified air (about 290 kJ/litre) than in compressed air (about 100 kJ/litre). His concept design used a rolling piston engine to power a family car capable of carrying 5 passengers and 346 litres of luggage over a 160 km. So, his design carried a bigger payload for further than the Airpod 2.0; however, like the electric charging system described a few weeks ago [see ‘Innovative design too far ahead of the market’ on May 5th, 2021], the design never the left the drawing board.
The economists John Kay and Mervyn King assert in their book ‘Radical Uncertainty – decision-making beyond numbers‘ that ‘economic forecasting is necessarily harder than weather forecasting’ because the world of economics is non-stationary whereas the weather is governed by unchanging laws of nature. Kay and King observe that both central banks and meteorological offices have ‘to convey inescapable uncertainty to people who crave unavailable certainty’. In other words, the necessary assumptions and idealisations combined with the inaccuracies of the input data of both economic and meteorological models produce inevitable uncertainty in the predictions. However, people seeking to make decisions based on the predictions want certainty because it is very difficult to make choices when faced with uncertainty – it raises our psychological entropy [see ‘Psychological entropy increased by ineffective leaders‘ on February 10th, 2021]. Engineers face similar difficulties providing systems with inescapable uncertainties to people desiring unavailable certainty in terms of the reliability. The second law of thermodynamics ensures that perfection is unattainable [see ‘Impossible perfection‘ on June 5th, 2013] and there will always be flaws of some description present in a system [see ‘Scattering electrons reveal dislocations in material structure‘ on November 11th, 2020]. Of course, we can expend more resources to eliminate flaws and increase the reliability of a system but the second law will always limit our success. Consequently, to finish where I started with a quote from Kay and King, ‘certainty is unattainable and the price of near-certainty unaffordable’ in both economics and engineering.