Tag Archives: wind turbine

Bringing an end to thermodynamic whoopee

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Two weeks ago I used two infographics to illustrate the dominant role of energy use in generating greenhouse gas emissions and the disportionate production of greenhouse gas emission by the rich [see ‘Where we are and what we have‘ on November 24th, 2021].  Energy use is responsible for 73% of global greenhouse gas emissions and 16% of the world’s population are responsible for 38% of global CO2 emissions.  Today’s infographics illustrate the energy flows from source to consumption for the USA (above), UK and Europe (thumbnails below).  In the USA fossil fuels (coal, natural gas and petroleum) are the source of nearly 80% of their energy, in the UK it is a little more than 80% and the chart for Europe is less detailed but the proportion looks similar. COP 26 committed countries to ending ‘support for the international unabated fossil fuel energy sector by the end of 2022’ and recognised ‘investing in unabated fossil-related energy projects increasingly entails both social and economic risks, especially through the form of stranded assets, and has ensuing negative impacts on government revenue, local employment, taxpayers, utility ratepayers and public health.’  However, to reduce our dependency on fossil fuels we need a strategy, a plan of action for a fundamental change in how we power industry, heat our homes and propel our vehicles.  A hydrogen economy requires the production of hydrogen without using fossil fuels, electric cars and electric domestic heating requires our electricity generating capacity to be at least trebled by 2050 in order to hit the net zero target. This scale and speed of  transition to zero-carbon sources is such that it will have to be achieved using an integrated blend of green energy sources, including solar, wind and nuclear energy.  For example, in the UK our current electricity generating capacity is about 76 GW and 1 GW is equivalent to 3.1 million photovoltaic (PV) panels, or 364 utility scale wind turbines [www.energy.gov/eere/articles/how-much-power-1-gigawatt] so trebling capacity from one of these sources alone would imply more than 700 million PV panels, or one wind turbine every square mile.  It is easy to write policies but it is much harder to implement them and make things happen especially when transformational change is required.  We cannot expect things to happen simply because our leaders have signed agreements and made statements.  Now, national plans are required to ween us from our addiction to fossil fuels – it will be difficult but the alternative is that global warming might cause the planet to become uninhabitable for us.  It is time to stop ‘making thermodynamic whoopee with fossil fuels’ to quote Kurt Vonnegut [see ‘And then we discovered thermodynamics‘ on February 3rd, 2016].

 

 

 

 

 

 

 

 

 

Sources:

Kurt Vonnegut, A Man without a Country, New York: Seven Stories Press, 2005.  He wrote ‘we have now all but destroyed this once salubrious planet as a life-support system in fewer than two hundred years, mainly by making thermodynamic whoopee with fossil fuels’.

US Energy flow chart: https://flowcharts.llnl.gov/commodities/energy

EU Energy flow chart: https://ec.europa.eu/eurostat/web/energy/energy-flow-diagrams

UK Energy flow chart: https://www.gov.uk/government/collections/energy-flow-charts#2020

When will you be replaced by a computer?

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I have written before about extending our minds by using external computing power in our mobile phones [see ‘Science fiction becomes virtual reality‘ on October 12th, 2016; and ‘Thinking out of the skull‘ on March 18th, 2015]; but, how about replacing our brain with a computer?  That’s the potential of artificial intelligence (AI); not literally replacing our brain, but at least taking over jobs that are traditionally believed to require our brain-power.  For instance, in a recent test, an AI lawyer found 95% of the loopholes in a non-disclosure agreement in 22 seconds while a group of human lawyers found only 88% in 90 minutes, according to Philip Delves Broughton in the FT last weekend.

If this sounds scary, then consider for a moment the computing power involved.  Lots of researchers are interested in simulating the brain and it has been estimated that the computing power required is around hundred peta FLOPS (FLoating point Operations Per Second), which conveniently, is equivalent to the world’s most powerful computers.  At the time of writing the world’s most powerful computer was ‘Summit‘ at the US Oak Ridge National Laboratory, which is capable of 200 petaFLOPS.  However, simulating the brain is not the same as reproducing its intelligence; and petaFLOPS are not a good measure of intelligence because while ‘Summit’ can multiply many strings of numbers together per second, it would take you and me many minutes to multiply two strings of numbers together giving us a rating of one hundredth of a FLOP or less.

So, raw computing power does not appear to equate to intelligence, instead intelligence seems to be related to our ability to network our neurons together in massive assemblies that flicker across our brain interacting with other assemblies [see ‘Digital hive mind‘ on November 30th, 2016]. We have about 100 billion neurons compared with the ‘Summit’ computer’s 9,216 CPUs (Central Processing Unit) and 27,648 GPUs (Graphic Processing Units); so, it seems unlikely that it will be able to come close to our ability to be creative or to handle unpredictable situations even accounting for the multiple cores in the CPUs.  In addition, it requires a power input of 13MW or a couple of very large wind turbines, compared to 80W for the base metabolic rate of a human of which the brain accounts for about 20%; so, its operating costs render it an uneconomic substitute for the human brain in activities that require intelligence.  Hence, while computers and robots are taking over many types of jobs, it seems likely that a core group of jobs involving creativity, unpredictability and emotional intelligence will remain for humans for the foreseeable future.

Sources:

Max Tegmark, Life 3.0 – being human in the age of artificial intelligence, Penguin Books, 2018.

Philip Delves Broughton, Doom looms over the valley, FT Weekend, 16 November/17 November 2019.

Engelfriet, Arnoud, Creating an Artificial Intelligence for NDA Evaluation (September 22, 2017). Available at SSRN: https://ssrn.com/abstract=3039353 or http://dx.doi.org/10.2139/ssrn.3039353

See also NDA Lynn at https://www.ndalynn.com/

Wind power

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Winds are generated by uneven heating of the earth’s atmosphere by the sun, which causes hotter, less dense air to rise and more dense, colder air to be pulled into replace it.  Of course, land masses, water evaporation over oceans, and the rotation of the earth amongst other things added to the complexity of weather systems.  However, essentially weather systems are driven by natural convection, a form of heat or energy transfer, as I hinted in my recent post entitled ‘On the beach’ [24th July, 2013].

If you are thinking of building a wind turbine to extract some of the energy present in the wind, then you would be well-advised to conduct some surveys of the site to assess the potential power output.  The power output of a wind turbine [P] can be defined as a half of the product of the air density [d] multiplied by the area swept by the blades [A] multiplied by the cube of the velocity [v].  So the wind velocity dominates this relationship [P = ½dAv3] and it is important that a site survey assesses the wind velocity.  But the wind velocity is constantly changing so how can this be done meaningfully?

Engineers might tackle this problem by measuring the wind speed for ten minute intervals, or some other relatively short time period, and calculating the average speed for the period.  This process would be repeated over a long period of time, perhaps weeks or months and the results plotted as frequency distribution, i.e. the results would be assigned to ‘bins’ labelled for instance 0.0 to 1.9 m/s, 2.0 to 3.9 m/s, 4.0 to 5.9 m/s etc and then the number of results in each bin plotted to create a bar chart.  The number of results in a bin divided by the total number of results provides the probability that a measurement taken at any random moment would yield a wind speed that would be assigned to that bin.  Consequently, the mathematical function used to describe such a bar chart is called a probability density function.  Now returning to the original relationship, P = ½dAv3 and using the probability density function instead of the wind velocity yields a power density function that can be used to predict the annual output of the turbine taking account of the constantly changing wind velocity.

If you struggled with my very short explanation of probability density functions, then you might try the Khan Academy video on the topic found on Youtube at http://www.youtube.com/watch?v=Fvi9A_tEmXQ

Engineers use probability density functions to process information about lots of random or stochastic events such as forces ocean waves interacting with ships and oil-rigs, flutter in aircraft wings, the forces experienced by a car as its wheels bounce along a road or the motion of an artificial heart valve.  These are all activities for which the underlying mechanics are understood but there is an element of randomness in their behaviour, with respect to time, that means we cannot predict precisely what will be happening at an instant in time; and yet engineers are expected to achieve reliable performance in designs which will encounter stochastic events.  Frequency distributions and probability density functions are one popular approach used by engineers.  Traditionally engineers have studied applied mathematics that was equated to mechanics in high school but increasing they need to understand statistics.