Archive for March, 2022

COVID-19: My insights have become irrelevant.

March 27, 2022

Click Image for a larger version. Logarithmic graph showing positive caseshospital admissions and deaths since the start of the pandemic. Numbers in panels highlight the numbers at the peak of Wave#2 in January 2021 and the peak of Wave#3 in January 2022. This pandemic has been going on for a long time and my ability to say anything useful about it is declining.

Friends, it has been just over two years since the UK locked-down in order to minimise the harm caused by the first wave of COVID-19.

Then…

In the following months I retired from my job, and devoted some of my new-found time to charting the development of the pandemic.

Since then I have intermittently deployed my scientific superpower to try to understand what was happening.

My ‘superpower’ was the ability to plot data on logarithmic graphs and spot exponential growth and decline in disease indicators: cases, hospital admissions, and deaths.

In the early data, the number of positive COVID tests was a good predictor for the number of COVID hospital admissions about 10 days later. And admissions were in turn a a good predictor for the number of COVID deaths about two weeks later still.

As the pandemic dragged on through 2020, I felt that what I was doing was helpful. At least, one or two people told me so.

Now… 

But two years on, things are more complicated.

After multiple vaccinations, multiple variants and strains, the different partial immunity that people have acquired from vaccination or prior infection, and very wide variation in people’s behaviour, there is no longer a simple story to tell about the dynamics of the disease.

For example, the disease is now as prevalent in the UK as it is has ever been – roughly 1 in 25 people are currently infected. Hospital admissions have been rising for roughly 3 weeks and are now around 2,000 people per day.

And finally, after a long delay, the rate at which people are dying is increasing: currently roughly 130 people are dying from the disease each day.

The new flu?… 

A few weeks ago, I asked if COVID had become ‘like flu’. And concluded that in terms of deaths it had. The death rate from COVID is around 10% of the roughly 10,000 people that die each week from all causes.

But at this time of year, we would expect flu cases to be reducing.

And flu does not cause ‘long flu’ in the way that COVID gives rise to Long-COVID. I have not studied Long-COVID statistics, but post-viral ‘syndromes’ are actually not unusual.

For example, I had not understood that when people are infected with polio virus, around 70% of infections are asymptomatic. And in those with symptoms, the disease is usually mild. The paralytic complications which in my understanding defined the disease, arise in just a small percentage of cases, when the viral infection finds a way out of the intestines into the central nervous system.

Similarly, I had not understood that infection with the Epstein Barr virus is usually harmless, but in certain circumstances can give rise to glandular fever, and has now been identified as one of the causal factors in Multiple Sclerosis.

So, the idea that a viral infection which primarily affects the airways can, in a small percentage of cases, affect other parts of the body is actually not unusual.

So… 

Friends, I just don’t know what to make of what is happening now.

The Government seem to have yet again abandoned their public health responsibilities, and by ending free-testing, is obliging us all to manage our own risks – and our risks to those around us – by nothing more than guesswork.

I can’t see what will happen in the coming months, but in the last two years infections have fallen from a winter peak only with the aid of severe lockdowns. But I sense no public appetite for the re-introduction of strong restrictions. And I too am enjoying visiting the folk club and other music venues.

In both 2020 and 2021 cases, admissions and deaths all began to rise again after restrictions were lifted. The rises started in June or July indicating that COVID (unlike most flu strains) can still thrive in a British summer. And in both years, the prevalence grew through the summer and autumn and went on to cause serious winter crises.

So viral prevalence may rise, or it may eventually begin to recede.

The situation with re-infection of previously infected or vaccinated people is complex. but recent evidence seems to point towards vaccination being much more effective than prior infection. And so we will likely rely on boosters to minimise deaths and hospital admissions.

I will keep monitoring things, and if I can think of something helpful to say I will. But for the time being, I will just try to say as little as I can.

Stay Safe.

Tee Shirt available here

 

Analysis of 16 years of Solar PV data.

March 16, 2022

A friend from North London kindly allowed me to analyse the data they had collected on the performance of their solar PV installation over the last 16 years.

What an opportunity to discover how solar PV panels behave over the long term!

Let me tell you what I found:

The System

Installed in July 2006, the system consisted of 16 Sanyo PV panels, each 0.88 m x 1.32 m with a nominal peak output of 210 W. This implies the panels output was initially ~180 watts per square metre.

They were installed on two adjacent roofs with a tilt of about 30° and facing 25° East of South and with no nearby trees or shading structures on the horizon other than their neighbour’s house.

The data set consisted of roughly 700 readings of the solar generation meter, most of them taken weekly but with a couple of gaps for a few months, and few points that were clearly in error. Rather than try to be sophisticated, I simply omitted points that were obviously in error.

Click image for a larger version. The ‘cleaned up’ data set.

Annual Analysis

One of things I was most anxious to search for was evidence of a year-on-year decline. The annual results are shown below:

Click image for a larger version. Graph of the Annual Output (kWh) of a North London PV system from 2006 to 2021. The dotted line is a linear fit to the data showing a systematic year-on-year decline in output.

It’s clear that there is a systematic year-on-year decline. If we re-plot the data to express this as a percentage we can compare it with what we might expect.

Click image for a larger version. The same data as in the previous graph but expressed as a fraction of the average output over the years 2007 and 2008. The dotted line is a linear fit to the data showing a systematic year-on-year decline in output.

This decline is – sadly – inevitable, arising as I understand it from atomic defects created in the silicon cells by exposure to the UV radiation in sunlight. These defects trap electrons which would otherwise reach an external contact if the crystal had been undamaged.

A decline of 6.1% per decade (0.61% per year) is quite competitive. Older panels showed higher declines (link) and more modern cells claim better performance, but not much better.

For example a 2020 Q-Cells Duo panel (link) specifies 0.54%/year decline for up to 10 years,  i.e. 5.4% per decade.

Click image for a larger version. Extract from a Q-cells data sheet showing expected decline in panel output over 25 years.

Variability

In addition to a linear decline in output the data also shows significant year-to-year variability. I wondered whether this variability arose from the natural variability of available sunshine, or some other factor.

To check this I exploited the EU Photovoltaic Geographical Information System (a.k.a. a ‘Sunshine Database’) which allows the calculation of the output of PV cells at any point in Europe or Africa over the period 2005 to 2016.

I had previously used this database to model the year-to-year variability of sunshine in West London when I was planning a battery installation.

To see if this was the cause of the year-to-year variability I plotted two quantities on the same graph:

  • The so-called ‘residuals’ of the fit to the data in the second graph above.
  • The variability of EU-database data.

The results are shown below.

Click image for a larger version. The variability of the North London PV data and the natural variability of sunshine as retro-dicted by the EU sunshine database

It is clear that in the years for which the two datasets overlap they agree well, suggesting that the variability observed is not due to some other poorly understood factor.

Upgrade?

My North London friend had one final question. Would they avoid more carbon dioxide emissions if they upgraded to modern panels?

To answer this I made two models:

  • The first model assumed that they did not upgrade and the existing panels were used to out to 2050.
  • The second model assumed they were replaced in 2022 with panels which operated with an efficiency of around 200 W/m^2 at peak illumination. This is about 20% more than the panels currently generate.

I assumed that the new panels would embody around 2 tonnes of CO2 emissions because Q-cells suggest their latest panels embody 400 kgCO2 per kWp.

I then assumed that 50% of the generated electricity was exported and 50% used domestically. As the grid currently functions:

  • Exported electricity reduces gas-fired generation which emits 450 gCO2/kWhe.
  • Domestic use avoids consumption of grid electricity with a carbon intensity of around 220 gCO2/kWhe in 2022.

Based on these assumptions, there is small advantage to replacing the panels, but this would not be realised until 2035.

Click image for a larger version. Does it make carbon-sense to replace existing PV cells with new more efficient cells?

One can model variations of these parameters, but the basic result is not affected: the carbon advantage is marginal.

My friend would help the climate more effectively by allocating his capital expenditure to something which might have more impact on CO2 emissions, perhaps buying shares in a wind farm?

But the result that really struck me from this modelling was how great the solar panels were in the first place!

Installed in 2006 and given minimal maintenance, it looks like the existing cells will avoid almost 30 tonnes of CO2 emissions by 2050. Not many technologies can achieve results like that as easily as that.

Heating Degree Days:4:Three numbers you need to know about your home

March 15, 2022

Friends, after the previous three posts (1, 2, 3) about Heating Degree Days, you may be wondering:

  • Is Michael OK? He seems to be obsessed with Heating Degree Days?
  • Hasn’t he been keeping an eye on the COVID figures?

Well, I have indeed been focussed on Heating Degree Days, and in this short (!) article I would like to summarise why.

The Heating Degree Day (HDD) concept enables two calculations for numbers you really should know about your dwelling:

  • It’s thermal leakiness: technically its heat transfer coefficient (HTC)
  • The size of heat pump your dwelling requires.

When combined with an estimate for how good the insulation is, you will be in a great position to make rational choices about improving the thermal performance of your dwelling.

Here are the three calculations:

#1:Heat Transfer Coefficient.

How much does heating power does it take to make your dwelling 1 °C warmer?

The answer to this question is known as the Heat Transfer Coefficient (HTC) for a dwelling.

A first estimate of your HTC can be made by dividing your annual gas consumption (in kWh) by 57.3:

Note: This formula was revised on 21/3/2022 due to a typo in the original text.

This assumes your dwelling (flat or house) is in the southern half of the UK (i.e. South of Manchester) and that you set your thermostat to 20 °C.

  • If you live between Manchester and Edinburgh, reduce your estimate of HTC by 10%.
  • For each 1 °C above 20 °C that you set your thermostat, reduce the first estimate of HTC by 10%.
  • For each 1 °C below 20 °C that you set your thermostat, increase the first estimate of HTC by 10%.

#2:Heat Pump Size.

How big a heat pump do I need?

It’s the question everyone wants an answer to!

A first estimate of the size of heat pump you require can be made by dividing your annual gas consumption (in kWh) by 2,900.

This assumes your dwelling (flat or house) is in the southern half of the UK (i.e. South of Manchester) and that you set your thermostat to 20 °C.

  • If you live between Manchester and Edinburgh, increase your estimate of heat pump power by 10%.
  • For each 1 °C above 20 °C that you set your thermostat, increase your estimate of heat pump power by 10%.

#3:Insulation

Do I need more insulation?

If your home is a house (rather than a flat), then you can assess how good your home is compared to the best possible as follows.

Divide your annual gas consumption (in kWh) by the floor area of all the floors in your home that live in i.e. include the loft if its part of the domestic space but not if it’s just used for storage.

  • The best possible is < 15 kWh/m^2/year: this is the Passivhaus standard
  • The best possible retrofit is < 25 kWh/m^2/year: this is the Enerphit retrofit standard
  • The AECB retrofit standard is < 50 kWh/m^2/year.

My house was ~ 90 kWh/m^2/year before external wall insulation and triple-glazing reduced it to around 45 kWh/m^2/year. The only way to significantly improve on this would be with underfloor insulation and air-tightness work.

If the figure for your home is very much above 100 kWh/m^2/year then I would suggest you consider insulation work.

Summary

If you know these numbers – even approximately – for your home, then you will be in a position to make reasonable choices about what to do next.

Please bear in mind that all the figures are approximate. I can see ways in which they could be wrong by 10%, but I would be surprised if they were 20% wrong.

Heating Degree Days:3: How do they vary?

March 15, 2022

Friends, having read the previous two posts (1, 2) about Heating Degree Days, you may be wondering.

  • How carefully do I need to be in choosing the baseline temperature?
  • How do heating degree days vary around the UK?
  • How do heating degree days vary from year-to-year?

If you were wondering things, then the text below should provide the answers you seek.

Seek on!

Choice of Base Temperature

Click on Image for a larger version. The graph shows annual running average of the number of heating degree days for the London St. James Park Weather station. Each curve corresponds to the number of heating degree days with a difference base-temperature. For each degree Celsius increase in the internal temperature, the heating demand increase by approximately 260 °C-days.

The choice of the base temperature is important when estimating heating demand.

The evidence in the previous article is that a ‘rule of thumb’ for choosing a base temperature is to pick a value 3.5 °C below the internal thermostat setting is probably OK.

  • 19 °C thermostat setting: use a base temperature of 15.5
  • 20 °C thermostat setting: use a base temperature of 16.5
  • 21 °C thermostat setting: use a base temperature of 17.5

Using data from St James Park in London, each 1 °C change in base temperature changes the annual degree-day estimate by roughly 260 °C-days/year. So if we estimate an average value of HDD(17.5 °C) is ~ 2,100, then turning down the thermostat by 1 °C would reduce heating demand (and hence gas consumption) by ~260/2,100 = 12.4%.

Variability over Time

Click on Image for a larger version. The graph shows annual running average of the number of heating degree days based on a 16.5 °C base temperature for London Heathrow Airport (in black). The dotted lines show the 20-year average and ± 1 standard deviation. Also shown are the monthly degree day totals (in purple) from which the annual averages are derived.

Looking at the data from Heathrow – which has a longer HDD record than most stations

  • The average number of HDD(16.5)s is 2053 °C-days/year and the standard deviation is roughly 8%.
  • The average number of HDDs(15.5)s is 1778 °C-days/year and the standard deviation is roughly 9%.

First we notice that the difference between HDD(15.5) and HDD(16.5) is 275 °C-days/year, similar to the 260 °C-days/year that we deduced from looking at the St James’s Park data.

Considering the variability, a standard deviation of 8% or 9% suggests that once in 20 years or so one might expect winters which have 16% or 18% more heating demand.

Variability with Location

The number of HDDs varies from place to place. The figure and table below show the number of HDDs based on a 16.5 °C base temperature averaged over the last 3 years.

  • A wide swathe of southern England, from Manchester southward, has heating demand within approximately 16% of the heating demand at Heathrow.
    • i.e. in the range 2,150 ± 150 °C-days/year
  • In Yorkshire, the North East, and Central Scotland, heating demand is about 25% greater than Heathrow.
    • i.e. ~ 2,500 °C-days/year

Click on Image for a larger version. The annual number of heating degree days based on a 16.5 °C base temperature for various UK locations averaged over the 3-year period from 1/3/2019 – 28/2/2022. The data are also shown as deviations from the number of HDDs(16.5 °C) at Heathrow Airport.

Click on Image for a larger version. Summary of the results in the previous figure.

In addition to large scale variations across the UK, there are smaller variations due to local factors, notably elevation and the city heating effect.

Based on the typical decline in temperature with height (typically 6.5 °C/km) then each 100 m of additional elevation would be approximately 0.65 °C colder. This will result in additional HDDs roughly equivalent to 275 x 0.65 °C or 165 °C-days/year.

To look at the urban heat island effect, I downloaded data from 4 locations around London.

Click on Image for a larger version. The annual number of heating degree days based on a 15.5 °C base temperature 4 locations around London.

Compared to data at Heathrow, there are significant changes in heating demand, with the centre of London being significantly warmer, and Gatwick Airport – just 38 km from the centre of London – being significantly colder.

Variability Summary

The heating demand at Heathrow Airport with base temperature of 16.5 °C (i.e. a likely thermostat temperature of 20 °C) is very roughly 2,000 °C-days per year.

This 2000 °C-day/year varies by typically:

  • 10% in nearby locations depending on more or less urban heating.
  • 12% to 15% per °C change in base temperature
  • -3% to + 15% over England and Wales south of the latitude of Manchester.
  • Up to 30% as far north as Aberdeen
  • Year to year variability of ±9% with occasional excursions to ±18%

So if one could not look up the number of degree days for a particular location (which one can easily at Degree Days!) one could characterise heating demand against a base temperature of 16.5 °C as likely to be within 15% of 2,300 °C-days per year almost anywhere in the  UK.

Heating Degree Days:2: Do they work?

March 15, 2022

Friends, in the last article I explained how the concept of Heating Degree Days (HDDs) allowed one to estimate the Heat Transfer Coefficient (HTC) for a house (a.k.a. its ‘thermal leakiness’) in a simple way.

  • Find out how many kWh per year it takes to keep a dwelling warm.
    • For gas users, use the number of kWh of gas consumed each year
    • For oil users, multiply the volume of oil used annually (in litres) by 10.
  • Find the number of HDDs for your locale,
    • or use 2,150±150 °C-days per year as a guess for most of the southern UK
    • or use 2,350 ± 150 °C-days per year as a guess for most of the northern UK.
  • And then divide, the number of kWh/year by the number of HDDs per year to yield the overall HTC for your dwelling.

In this article I want to explain how I checked this calculation using a much more complicated process. Read on if you want to know the gory details!

Basic Observations

The reason I love the idea of HDDs so much is because I spent such a long time – several years! – trying to work out the heat transfer coefficient (HTC) for my home the long way.

Click on Image for a larger version. The graph shows weekly measurements over the last three years. In light blue, the graph shows weekly gas consumption in kWh. In green, the graph shows the difference between the internal temperature and the external temperature. In dark blue, the graph shows weekly heat output from the heat pump in kWh. It’s clear that gas consumption and heat pump output follow the heating demand quite closely.

For me it all started back in late 2018 when I bought a weather station. Fired by ‘new toy’ enthusiasm, I recorded the average daily and weekly temperatures, and wondered whether the gas consumption increased as the outside temperature fell. I started to read the gas meter, at first daily, but then settled down to reading it weekly.

Although it is completely obvious, I felt surprised to ‘discover’ that gas consumption did indeed increase as the outside temperature fell.

On the graph above I have plotted temperature ‘demand‘ (the difference between the inside and outside temperatures) and gas consumption (kWh/day) on the same graph. The data on this has been smoothed, plotting the average of ±2 weeks around each data point.

You can see quite clearly that gas consumption follows temperature demand. The Heat Transfer Coefficient (HTC) is the constant of proportionality between these two quantities. But you can see that (as a result of the new glazing and insulation) the HTC changes through the years.

For example, the graph below shows the same data as in the graph above but highlights the effect of the new glazing and insulation. The heating demand in Jan/Feb 2021 was greater than in Jan/Feb 2019 but the gas consumption was only about half that in Jan/Feb 2019. In other words. In other words, I had reduced the HTC by about half.

Click on Image for a larger version. This is the same data as in the graph above but highlighting the effect of the new glazing and insulation. The heating demand in Jan/Feb 2021 was greater than in
Jan/Feb 2019 but the gas consumption was only about half that in Jan/Feb 2019.

The four phases

The graphs above cover 4 distinct phases of the work on the house.

Click on Image for a larger version. This same data as in the graph above but highlighting the four phases of the refurbishment.

  • Phase#1 is the period before works began.
  • Phase#2 is the period after the main Triple-Glazing work was done
  • Phase#3 is the period after the final Triple-Glazing was done and the External Wall Insulation was applied.

In each of these phases, we should expect a distinctly different proportionality between heating demand and gas consumption – i.e. they each have a distinct HTC.

In Phase 3 we have data for both gas consumption (Phase#3A) and for heat pump use (Phase#3B). These should both have the same HTC – the insulation was the same – but the data is acquired in quite different ways.

I took the data in each of the phases and plotted average daily gas consumption versus temperature demand. The graphs for phases 1, 2 and 3A are plotted below.

The graphs all have the same vertical and horizontal scales and you can see that as the works progressed, the slope of the data has decreased. In other words, as the re-furbishment progressed, it took fewer kWh of gas per day to keep the house warm.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#1 i.e. before I made any changes.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#2 of the refurbishment i.e. after the house was triple-glazed.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#3A of the refurbishment i.e. after the external wall insulation.

These graphs are fascinating. Firstly we note that graphs consist of two regions:

  • At low heating demand, there is no temperature-dependence of the heating demand – the graph is flat at about 5 kWh/day. This is because during the summer, the heating is used for domestic hot water and cooking only.
  • At high heating demand the data fit plausibly to a straight line, but not one that goes through the origin. The slope intercepts the 5 kWh line at roughly 3.5±0.5 °C of demand. This slope is just the HTC that we are looking for. It tells us how many extra kWh/day it takes to warm the house for each extra °C of temperature demand

The fact that the gas consumption doesn’t start to increase immediately the outside temperature falls below the thermostat set-temperature is because there are other sources of heat in the house.

  • All the electrical items in our house typically consume around 200 W continuously – or 4.8 kWh/day.
  • And each person in the house contributes around 100 W continuously, so my wife and I contribute another 4.8 kWh/day.

I investigated this phenomenon in quite some detail in these articles (1, 2), but the upshot is that the heating in my home doesn’t switch itself on until the external temperature falls about 3.5±0.5 °C below the thermostat temperature.

Looking at the slopes of the graphs, I can plot them to show how the Heat Transfer Coefficient has been reduced as a result of my refurbishments.

The Red Circles on the graph below show estimates assuming that the gas boiler is 100% efficient i.e. all the energy of burning the gas is retained within my home. A more realistic estimate is that only 90% of the heat is retained within the house. The estimate of the HTC using this assumption is showing blue.

Click on Image for a larger version. Graph showing the Heat Transfer Coefficient for my home deduced from the  slopes of the previous three graphs. The Red Circles show estimates assuming that the gas boiler was 100% efficient. The Blue Circles show more realistic estimates assuming that the gas boiler was only 90% efficient. The left hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

I have gone through this calculation of the HTC in some detail to show just how difficult it is. Now let’s look and see how much easier it is using degree days.

Calculation Using Degree Days

To calculate the same estimates for HTC I need to do the following:

  • Look up my records to find out how many kWh of gas I used in each of the three phases. All it requires is a single reading of the gas meter at the start and end of each phase. To gain extra accuracy one can assume that at best 90% of these kWh of gas consumed resulting in heating kWh.
  • Look up the Degree Days Website to find out the number of heating degree days in each of the three phases.
  • Divide the gas consumption by the number of HDDs.

During Phases #1, #2 and #3A, our thermostat was set to 19.0 °C, and so I used HDDs with a base temperature of 15.5 °C i.e. 3.5 °C lower than 19.0 °C.

The Calculational Steps outlined in the bullet points are summarised in the table below.

Phase Gas Consumption (kWh) Heating (kWh) at 90% Efficiency HDD15.5s

(°C-days)

HTC
(kWh/day/°C)
HTC
(W/°C)
1 13,323 11,991 1,430 8.4 349
2 13,756 12,380 1,787 6.9 288
3A 6,902 6,212 1,773 3.5 146

The resulting HTC estimates are compared with those previously calculated using the long-winded method in the Graph below.

Click on Image for a larger version. Graph showing estimates for Heat Transfer Coefficient for my home during the three phases of refurbishment. The blue circles are the same data plotted in teh previous graph assuming that the gas boiler was 90% efficient. The Blue Circles. The Green Squares show the result of the same calculation using HDD15.5s. The agreement is striking. The left-hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

The agreement between the two methods of calculating the HTC is striking.

What this means is that instead of having to record external temperatures and gas consumption week-by-week as I did for three years, one can get equivalent results by using HDDs and just one or two gas meter records.

A final test: Phase#3B

I can check the calculational method and some of my assumptions by comparing the HTC in Phases 3A and 3B. There were no changes to the insulation in these phases: what changed was that I switched from heating using a gas boiler to heating with a heat pump.

So HTC should be the same in Phases 3A and 3B.

However there was one change that arose from etc heat pump switch. As I learned to use the controls of the heat pump, I eventually stuck with settings that resulted in the house being warmer (~20.5 °C) than it had been previously (~19 °C).

For this reason I calculated the number of degree-days in Phase#3B using a base temperature of 17.0 °C rather than 15.5 °C. The result is plotted in purple on the graph below.

Click on Image for a larger version. The same graph as shown previously, but now with the calculation for Phase 3B shown as a filled purple circle. The left-hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

All three calculations of the HTC in Phase#3 agree within a range of 10%, which is pretty much as good as any calculation or measurement of HTC can hope for.

This gives me confidence that the HDD method does indeed work, and that the likely boiler efficiency in Phases #1, #2 and #3A was probably not very different from 90%.

Summary

Apologies for this very long article. You may be asking, as I am, “Why did I write this?”

The answer is that being able to calculate the HTC for a dwelling is important. And if using HDDs makes the calculation simpler, then maybe more people will do the calculation.

And this should enable more people to rationally plan their home refurbishment and estimate the size of the heat pump they require.

And it’s all thanks to the kind people over at Heating Degree Days.

In the next article I’ll look at how HDD’s vary with:

  • choice of base temperature,
  • location in the UK, and
  • from year-to-year.

 

Heating Degree Days:1: A Brilliant Idea

March 15, 2022

Friends, on learning recently about the wonderful idea behind Heating Degree Days (HDDs) I found myself torn between two conflicting emotions.

  • On the one hand, I feel delighted at the cleverness of the concept and I rejoice in my new-found ability to save so much time on calculations about heating houses.
  • But on the other hand, I feel like an idiot for not having known about the idea previously!

Being the positive person that I am, I am writing this gripped by the more positive sentiment and have written four articles on the subject. This is first article in which I try to keep things simple-ish. I deal with complicated questions is this next article and the one that follows that.  In the final article I summarise the calculations that HDDs make easy.

HDDs and HTCs?

Heating Degree Days (HDDs) make it easy way to calculate the ‘thermal leakiness’ of a dwelling – a quantity technically called its overall Heat Transfer Coefficient (HTC).

The HTC is the most important number to know if you are considering any type of retrofit – insulation, draught-proofing or installing a heat pump. It allows you to answer the question:

When it’s (say) 8 °C outside, how much heating power (in watts or kWh/day) do I need to keep my house at (say) 20 °C“.

The answer is just the temperature difference (12 °C in this example) multiplied by the HTC.

So if the HTC of a dwelling is 300 W/°C then it would require 12 °C x 300 W/°C = 3,600 W or 3.6 kW to keep that dwelling warm.

But how do you find the HTC? This is normally quite hard work. It usually requires either extensive surveys and calculations or or prolonged measurements. But the idea of heating degree days HDDs makes it really easy. There is just one sum to do. Let me explain.

Degree Days in General

The idea of degree-days  is commonplace in agriculture.

For example, in viticulture, the number of Growing Degree Days (GDD) is calculated to allow farmers to estimate when the grapes will flower, or ripen, and when certain pests will emerge.

Click on Image for a larger version. Illustration of the concept of Growing Degree Days (GDDs). See text for more details.

GDDs are calculated as follows:

  • If the average daily temperature is below some Base Temperature – usually 10 °C – then one adds 0 to the number of GDDs
  • If the average daily temperature on a day is above the Base Temperature, then one subtracts the base temperature from the average temperature, and adds the result to the number of GDDs.
    • So if the average temperature on a particular day is 15 °C, and the base temperature is 10 °C, then one adds 5 °C to the GDD total.

Each geographic region has a characteristic number of GDDs available per year, and each grape-type requires a certain number of GDDs for a successful harvest. So using GDDs is a simple way to match vines to regions,

Alternatively, in any particular year, one can use the number of GDDs to discuss whether the grapes are likely to mature earlier or later.

Heating Degree Days

Heating Degree Days (HDDs) work in a similar way to GDDs, but count days when the temperature falls below a base temperature.

Over a winter season, the number of HDDs provides an estimate for the overall ‘heating demand’ that you want your heating system to meet.

Click on Image for a larger version. Illustration of the concept of Heating Degree Days (HDDs). See text for more details.

To keep things specific in this article I will mainly work with a base temperature of 16.5 °C, and the heating degree days are then known as HDD(16.5)s.

I’ll explain the choice of base temperature in the next article, but the choice corresponds to a thermostat setting of approximately 20 °C which is typical of many UK dwellings.

  • For much of the south of the UK – basically anywhere south of Manchester – the number of HDD(16.5)s per year typically lies in the range 2,150 ± 150 °C-days/year.
  • For regions north of Manchester up to Edinburgh in Scotland, the number of HDD(16.5)s per year typically lies in the range 2,350 ± 150 °C-days/year.
  • You can look up the exact number of HDD16.5s for your location for the last three years using the outstanding Heating Degree Days web site. At my home in Teddington, the annual number HDD(16.5) is typically 2,000 °C-days/year

What now?

In order to estimate the heat leak from a dwelling – its Heat Transfer Coefficient (HTC) – you also need to know one more number: how many kWh of heating the dwelling requires in a year.

  • If it’s heated with gas, you can use the annual number of kWh of gas used.
  • If it’s heated with oil, multiply the number of litres of oil used annually by 10.
    • e.g. 2,500 litres of heating oil per year is ~25,000 kWh.

So for example, before I did any work on our home, we used 15,000 kWh of gas each year. I looked up the annual number

So to calculate the HTC for my home I divide 15,000 kWh/year by 2,000 °C days/year to give 7.5 kWh/day/°C. This tells me that:

  • To heat my home 1 °C above the outside temperature required an additional 7.5 kWh of gas per day.
  • Or if I reduced the temperature in my home by 1 °C, I would save 7.5 kWh of gas per day.

Equivalently, if we divide by 24 and multiply by 1000, we can convert 7.5 kWh/day/°C into the more common units of watts i.e. 313 watts/°C.

  • So to heat my home 1 °C above the outside temperature required an additional continuous 313 W of heating.

Thinking about a heat pump?

Knowing the HTC, one can change a qualitative sense that “it’s a really cold house” into a quantitative measurement “It has a HTC of 400 W/°C“. that can help one to choose which refurbishments are likely to be effective.

Suppose, for example, we want to work out the size of heat pump required to heat our dwelling in the depths of winter.

Typically the coldest temperatures encountered routinely in the UK are around -3.5 °C i.e. around 20 °C colder than the base temperature.

So to estimate the heat pump power required for my house before insulation, I would simply multiply the heating demand (20 °C) by the HTC (313 W/°C) to yield 6.26 kW.

Additionally, if we make changes to the dwelling, such as adding triple-glazing, we can estimate the change in HTC by dividing fuel use (in kWh) by the number of HDD16.5s – a number which can be found for any location at the Heating Degree Days web site.

Summary

This article introduced the idea of using Heating Degree Days as an estimate of overall demand.

When combined with a measure of heating energy supplied over the same period, dividing one by the other magically yields the Heat Transfer Coefficient (HTC) for a dwelling.

Knowing the HTC one can measure the effect of any improvements one makes – such as triple-glazing or installation. And additionally, one can calculate the amount of heating required on a cold winter day.

But you may have some questions. For example:

  • I set my thermostat to 20 °C: Why did I recommend using 16.5 °C as a base temperature?
  • Does it really work?
  • How do HDD(16.5)s vary from one location to another and from year-to-year?

Another reason to stop using gas

March 6, 2022

Friends, many people are considering reducing, or stopping entirely, their use of natural gas for heating and cooking. Perhaps you are one of these people.

It may be that your motivation is because burning this gas is altering the climate of our planet.

Or may be that your motivation is because buying the gas supports murderous and megalomaniacal regimes across the plant.

But perhaps these motivation aren’t quite enough. If so, then please consider this:

  • Cooking with gas is poisoning you and your family

Yes, cooking with gas emits nitrogen oxide (NO) and nitric oxide (NO2) into your kitchen. Collectively these gases are known NOx.

When NOx reaches the membranes of your skin or nose, it quickly forms nitric acid, which irritates the membranes and can cause asthma and sensitise people to other allergens.

If you are concerned about air pollution in cities, then before worrying about vehicle emissions, you should probably first focus your attention on your own home where NOx levels are likely to be very much higher.

Let me explain

Air consists of very roughly 80% nitrogen (N2) and 20% oxygen (O2).

When burning natural gas, methane (CH4), in air, the majority of the combustion products (water (H2O) and carbon dioxide (CO2)) arise from reactions between methane and oxygen.

The nitrogen molecules – despite making up the bulk of the air – are relatively inert. But they are not completely inert.

At the high temperatures – approaching 2000 °C – of a methane flame, the nitrogen and oxygen molecules dissociate into atomic nitrogen and oxygen and in this state they react to form oxides of nitrogen, primarily NO.

This NO then converts to NO2 over a time frame that depends on what else is in the atmosphere. Thus even when the amount of NOx is constant, the fractions of NO and NO2 are likely to change over time.

When methane combustion takes place in a boiler, none of the combustion products enter your home.

But when you cook with gas, the combustion products are all vented directly into your home. Including the NO and NO2 i.e indoor NOx pollution.

Is this really a problem?

I don’t know for sure, but I suspect it must be.

Whereas professional kitchens frequently have strong extraction over open burners and ovens, domestic kitchens often do not. And where extraction is present, it is often not used, and when it is used, it only covers burners and not ovens.

Concentrations of NOx are difficult to measure for several reasons.

Firstly a meter to measure NO2 costs thousands of pounds versus a hundred pounds or so for a CO2 meter, and so there are very few reported measurements in kitchens.

And secondly, the ratio of NO to NO2 is generally not well-known in any particular circumstance.

Consequently using measurements of NO2 to estimate NOx will always give an underestimate of the NOx level.

One measurement in kitchens is in this article.  It shows measurements of NO2 during an evening of cooking in one US household. I have reproduced the figure below.

Click on figure for a larger version. While cooking with gas in this US household, NO2 levels rose to almost 300 ppb. This figure is modified from the linked article.

In the UK exposure limits for NO2 are an annual average exposure to 40 μg/m^3 with less than 18 exposures per year to peaks above 200 μg/m^3 averaged over 1 hour.

So it looks like the occupants of this household are being exposed to very high levels of NO2. But the  NO levels close to the cooker are likely to be even higher.

My Measurements and Calculations

I wondered if the measurements above were plausible. The peak did not have the shape I would have expected: it seems to fall very rapidly suggesting there was strong airflow through the house.

Unfortunately, I can’t measure NO or NO2 directly but I routinely monitor CO2 in the central part of the house, well away from the oven and hob. Nonetheless I regularly see the CO2 levels rise to over 1000 ppm during cooking. For this article I also took measurements with the detector at roughly head height next to the hob.

Click on figure for a larger version. The location of the CO2 meter relative to the hob for the  measurements in red in the graph below.

The graph below shows the CO2 data.

  • For the detector near the hob, the burner was on for 15 minutes and the CO2 levels rose immediately.
  • For the detector in the neighbouring room, the burner was on for 17 minutes and there was a delay of many minutes before CO2 levels began to rise.

Click on figure for a larger version. The rise in carbon dioxide concentration above background resulting from a single gas burner on teh hob. The measurements in black were measured several metres away in a different room. The measurements in red were measure at head height above the hob..

What is clear from both these measurements is that CO2 concentrations of at least 500 ppm above background are likely to be commonplace in all the rooms in homes which use gas hobs, ovens or grills.

I wondered if the ratio of production of NO to CO2 might occur at a fixed ratio. If so, that would allow me to use measurements of CO2 concentration to estimate likely levels of NO.

I wasn’t quite sure how to do this but an old friend suggested using the free and excellent GasEq software to calculate the likely combustion products and their relative concentrations.

Using the methane combustion in air example, I calculated the ratio of the NO in the exhaust gases to CO2. Then from measuring the CO2 rise due to combustion, I could estimate the NO concentration in the house.

Click on figure for a larger version. Logarithmic graph showing estimates of the NO concentration in parts per billion (ppm) assuming 500 ppm CO2 concentration from combustion and 400 ppm CO2 background concentration i.e. a measured CO2 concentration of around 900 ppm. See text for further details.

At first I was shocked. The calculation suggested that NO levels of several thousand ppm were likely. But this was based on two assumptions: that the gas flame was adiabatic and stoichiometric. What wonderful words.

  • Adiabatic means that no heat is lost from the flame and so the products would be at their maximum possible temperature, approximately 2225 K. However in a domestic gas burner, heat will be lost to both the burner itself, and saucepans which typically only reach 250 °C. So I repeated the calculation for lower temperatures.
  • Stoichiometric means that exactly the right amount of oxygen was mixed with the methane so that all of the methane and oxygen reacted.
    • If the gas mixture has excess methane (a so-called fuel-rich mixture) then less oxygen will be available to react with the nitrogen, and NO production will be reduced.
    • Similarly If the gas mixture does not quite enough methane (a so-called fuel-lean mixture) then some un-reacted oxygen will be available to react with the nitrogen, and NO production will be increased.

So I repeated calculations for a range of stoichimetries (±5% and ±10% from ideal) and a range of temperatures, extending down to more than 200 °C below the adiabatic flame temperature.

My conclusion from this calculation is that even with very conservative assumptions, when CO2 levels from combustion rise 500 ppm above background, the levels of NO in the air is likely to be several hundred ppb. Eventually some fraction of this NO will convert to NO2 and yield NO2 levels well-above safe exposure levels.

Of course without direct measurements, I don’t know this for sure, but I am surprised that this issue is not discussed more.

Summary

My conclusion is simple. Based on measurements of CO2 concentration in my own home, and calculations of the likely ratio of NO to CO2, I think that NOx exposure in UK households with open gas hobs, ovens, and grills is likely to routinely exceed exposure guidelines.

For people standing over a hob, or people routinely working in a domestic kitchen, exposure levels could easily be dramatically higher.

If anyone has problems with asthma or is concerned about their own – or their children’s exposure to air pollution – then it is likely that the best thing people can do is to stop using gas for cooking, and to instead use microwaves, electric ovens and induction hobs.

This archaic ‘burning’ technology is funding Putin’s war machine, changing the Earth’s climate. AND polluting my home!

Personally, I just can’t wait to get rid of this gas hob as soon as possible.

Will aviation eventually become electrified?

March 2, 2022

Friends. I ‘have a feeling’ that aviation will eventually become electrified. At first sight this seems extraordinarily unlikely, but I just have this feeling…

Obviously, I could just be wrong, but let me explain my thinking.

Basics 

The current technology for aviation – jet engines on aluminium/composite frames with wings – relies on the properties of jet fuel – kerosene.

There are two basic parameters for aviation ‘fuel’.

  • Energy density – which characterises the volume required to carry fuel with a certain energy content. It can be expressed in units of megajoules per litre (MJ/l).
  • Specific energy – which characterises the mass required to carry fuel with a certain energy content. It can be expressed in units of megajoules per kilogram (MJ/kg).

Wikipedia have helpfully charted these quantities for a wide range of ‘fuels’ and this figure is shown above with five technologies highlighted:

  • Lithium batteries,
  • Liquid and Gaseous Hydrogen,
  • Kerosene and diesel.

Click on image for a larger version. Chart from Wikipedia showing the specific energy and energy density of various fuels enabled energy technologies.

A general observation is that hydrocarbon fuels  have a much higher density and specific energy than any current battery technology. Liquid Hydrogen on the other hand has an exceptionally high specific energy, but poor energy density: better than batteries but much worse than hydrocarbon fuels.

Lessons from the EVs transition:#1

I think the origin of my feeling about the aviation transition stems from the last 20 years of watching the development of battery electric vehicles (BEVs). What is notable is that the pioneers of BEVs – Tesla and Chinese companies such as Xpeng or BYD – are “all in” on BEV’s – they have no interest in Internal Combustion Engine (ICE) vehicles or hybrids. They have no legacy market in ICE vehicles to protect.

‘Legacy Auto’ (short hand for VW, GM, Ford, Toyota etc) had poked their toe in the waters of alternative drive-trains quite a few years ago. GM’s Volt and Bolt were notable and Toyota’s Mirai hydrogen fuel cell car was a wonder. But Legacy Auto were comfortable manufacturing ICE vehicles and making profits from it, and saw these alternative energy projects as ‘insurance’ in case things eventually changed.

As I write in early 2022, all the legacy auto makers are in serious trouble. They can generally manufacture BEVs, but not very well – and none of them are making money from BEVs. Aside from Tesla, they have very poor market penetration in China, the world’s largest EV car-market. In contrast Tesla are popular in China and America and Europe and make roughly 20% profit on every car they sell.

So one lesson from the BEV transition is that the legacy industry who have invested billions in an old technology, may not be the pioneers of a new way of doing things.

Lessons from the EVs transition:#2

How did BEV’s overcome the awesome advantages of hydrocarbon fuels over lithium batteries in terms of energy density and specific energy?

First of all, ICEs throw away about 75% of their advantage because of the way they operate as heat engines. This reduces their energy density advantage over batteries to just a factor 10 or so.

Secondly, there is the fact that ICE cars contain many heavy components – such as engines, gearboxes, and transmissions that aren’t needed in a BEV.

But despite this, BEV cars still generally have a weight and volume disadvantage compared to ICE cars. But this disadvantage has been overcome by careful BEV-specific design.

By placing the large, heavy, battery pack low down, designers can create pleasant vehicle interiors with good handling characteristics. And because the ability to draw power rapidly from batteries is so impressive, the extra mass doesn’t materially affect the acceleration of the vehicle.

EV range is still not as good as a diesel car with a full tank. But it is now generally ‘good enough’.

And once EVs became good enough to compete with ICE vehicles, the advantages of EVs could come to the fore – their ability to charge at home, low-running costs, quietness, potential low carbon emissions and of course, zero in situ emissions.

And significantly, BEV’s are now software platforms and full electronic control of the car allows for some capabilities that ICE vehicles will likely never have.

Lessons from the EV transition:#3

Despite Toyota’s massive and long-term investment in Hydrogen Fuel Cell (HFC) cars, it is now clear that hydrogen will be irrelevant in the transition away from ICE vehicles. Before moving on to look at aviation, it is interesting to look at why this is so.

The reason was not technological. HFC cars using compressed hydrogen fuel were excellent – I have driven one – with ranges in excess of 320 km (200 miles). And they were excellent long before BEVs were excellent. But the very concept of re-fuelling with hydrogen was the problem. Hydrogen is difficult to deal with, and fundamentally if one starts with a certain amount of electrical power – much less of it gets to the wheels with a HFC-EV than with a BEV.

The very idea of a HFC car is – I think – a product of imagining that there would be companies akin to petrochemical companies who could sell ‘a commodity’ in something like the way Oil Companies sold petrol in the 20th Century. BEV’s just don’t work that way.

Interestingly, the engineering problems of handling high-pressure hydrogen were all solved in principle. But this just became irrelevant.

Cars versus Aeroplanes

So let’s look at how energy density and specific energy affect the basic constraints on designs of cars and aeroplanes.

50 litres of diesel contains roughly 1,930 MJ of energy. The table below shows the mass and volume of other fuels or batteries which contains this same energy.

Mass (kg) Volume (l)
Kerosene 45 55
Diesel 42 50
Hydrogen HP 14 364
Hydrogen Liquid 14 193
Lithium Battery 4,825 1,930

We see that batteries look terrible – the equivalent energy storage would require 4.8 tonnes of batteries occupying almost 2 cubic metres! Surely BEVs are impossible?!

But as I mentioned earlier, internal combustion engines waste around 75% of their fuel’s embodied energy in the form of heat. So a battery with the required stored energy would only need 25% of the mass and volume in the table above.

Mass (kg) Volume (l)
Lithium Battery 1206 483

So, we see that the equivalent battery pack is about a tonne heavier than the fuel for a diesel car.

But this doesn’t include the engine required to make the diesel fuel work. So one can see how by clever design and exploiting the fact that electric motors are lighter than engines, one can create a BEV that, while heavier than an ICE car, is still competitive.

Indeed, BEVs now outperform ICE cars on almost every metric that anyone cares about, and will continue to get better for many years yet.

Let’s do the same analysis for aeroplanes. A modern jet aeroplane typically carries 100 tonnes of kerosene with an energy content of around 43 x 105 MJ. This is sufficient to fly a modern jet (200 tonnes plus 30 tonnes of passengers) around 5,000 miles or so.

The table below shows the mass and volume other fuels or batteries which contains this same energy. Notice that the units are no longer kilograms and litres but tonnes and cubic metres.

Mass (tonnes) Volume (m^3)
Kerosene 100 123
Diesel 94 111
Hydrogen HP 31 811
Hydrogen Liquid 31 430
Lithium Battery 10,750 4,300

Now things look irrecoverably impossible for batteries! The batteries would weigh 10,000 tonnes! And occupy a ridiculous volume. Also, turbines are more thermodynamically efficient than ICEs, so assuming say 50% efficiency, batteries would still weigh ~5,000 tonnes and occupy 2,000 m3.

Even with a factor 10 increase in battery energy density – which is just about conceivable but not happening any time soon – the battery would still weigh 1,000 tonnes!.

Does it get any better for shorter ranges? Not much. Consider how much energy is stored in 10 tonnes of kerosene (~43 x 104 MJ). This is sufficient to fly a modern jet – weighing around 50 tonnes unladen and carrying 20 tonnes of passengers around 500 miles or so.

Mass (tonnes) Volume (m^3)
Kerosene 10 12
Diesel 9 11
Hydrogen HP 3 81
Hydrogen Liquid 3 43
Lithium Battery 1,075 430

Even assuming 50% jet efficiency, batteries with equivalent energy would still weigh ~500 tonnes and occupy 200 m3. Even after a factor 10 increase in battery energy density, things still look pretty hopeless.

So can we conclude that battery electric aviation is impossible? Surprisingly, No.

And yet, it flies.

Jet engines burning kerosene have now reached an astonishing state of technological refinement.

But jet engines are also very expensive, which makes the economics of airlines challenging. And despite improvements, jets are also noisy. And of course, they emit CO2 and create condensation trails that affect the climate.

In contrast, electric motors are relatively cheap, which means that electric aeroplanes (if they are possible) would be much cheaper, and require dramatically less engine maintenance. These features are very attractive for airlines – the people who buy planes. And the planes would be quiet and have zero emissions – attractive for people who fly or live near airports.

And several companies are seeking to exploit these potential advantages. Obviously, given the fundamental problem of energy density I outlined above, all the projects have limitations. Mostly the aeroplanes proposed have limited numbers of passengers and limited range. But the companies all impress me as being serious about the engineering and commercial realities that they face. And I have been surprised by what appears to be possible.

Here are a few of the companies that have caught my attention.

Contenders

In the UK, Rolls Royce and partners have built an impressive single-engined aircraft which flies much faster than equivalent ICE powered aircraft.

Their Wikipedia page states the batteries have a specific energy of 0.58 MJ/kg, about 50% higher than I had assumed earlier in the article. The range of this plane is probably tiny – a few 10’s of kilometres – but this number will only increase in the coming years.

This aeroplane is really a technology demonstrator rather than a seedling commercial project. But I found it striking to see the plane actually flying.

In Sweden, Heart Aerospace have plans for a 19-seater short-hop passenger craft with 400 km of range. Funded by Bill Gates amongst others, they have a clear and realistic engineering target.

In an interview, the founder explained that he was focussing on the profitability of the plane. In this sense the enterprise differs from the Rolls Royce project. He stated that as planned, 2 minutes in the air will require 1 minute of re-charging. He had clear markets in mind in (Sweden, Norway, and New Zealand) where air travel involves many ‘short hops’ via transport hubs. And the expected first flights will be soon – 2025 if I have it correct.

In Germany, Lillium are building innovative ducted-fan planes. Whereas Heart’s planes and Rolls Royce’s demonstrator projects are conventional air-frames powered by electric motors, Lillium have settled on completely novel engineering possibilities enabled by electrical propulsion technology. Seeing their ‘Star Wars’ style aircraft take off and land is breathtaking.

Back in the UK, the Electric Aviation Group are advertising HERA as a 90-seater short route airliner with battery and hydrogen fuel-cell technology (not a turbine). This doesn’t seem to be as advanced as the other projects I have mentioned but illustrates the way that different technologies may be incorporated into electric aviation.

What about Hydrogen Turbines?

Legacy Aeromaker Airbus are advertising development of a hydrogen turbine demonstrator. It’s a gigantic A380 conventional jet airliner with a single hydrogen turbine attached. (Twitter video)

Stills from a video showing how the hydrogen turbine demonstrator will work. A single turbine will attached to kerosene driven aeroplane by 2035.

The demonstrator looks very clever, but I feel deeply suspicious of this for two sets of reasons: Technical reasons and ‘Feelings’.

Technical.

  • Fuel Volume: To have the same range and capabilities as an existing jet – the promise that seems to be being advertised – the cryogenic (roughly -250 °C) liquid hydrogen would occupy 4 times the volume of the equivalent kerosene. It likely could not be stored in the wings because of heat leakage, and so a big fraction of the useful volume within an aeroplane would be sacrificed for fuel.
  • Fuel Mass: Although the liquid hydrogen fuel itself would not weigh much, the tanks to hold it would likely be much heavier than their kerosene equivalents. My guess is that that there would not be much net benefit in terms of mass.
  • Turbine#1: Once the stored liquid hydrogen is evaporated to create gas, its energy density plummets. To operate at a similar power level to a conventional turbine, the volume of hydrogen entering the combustion chamber per second will have to be (very roughly) 40 times greater.
  • Turbine#2: Hydrogen burns in a different way from kerosene. For example embrittlement issues around the high pressure, high temperature hydrogen present at the inlets to the combustion chamber are likely to be very serious.
  • I don’t doubt that a hydrogen turbine is possible, but the 2035 target advertised seems about right given the difficulties.
  • Performance: And finally, assuming it all works as planned, the aircraft will still emit NOx, and will still be noisy.

Feelings.

  • I feel this a legacy aero-maker trying to create a future world in which they are still relevant despite the changed reality of climate crisis.
  • I suspect these engines assuming the technical problems are all solved –  will be even more expensive than current engines.
  • I feel the timeline of 2035 might as well be ‘never’. It allows Airbus to operate as normal for the next 13 years – years in which it is critical that we cut CO2 emissions.
  • I suspect that in 13 years – if electric aviation ‘gets off the ground’ (sorry!) – then it will have developed to the point where the short-haul end of the aviation business will be in their grasp. And once people have flown on smaller quieter aircraft, they will not want to go back.
  • Here is Rolls Royces short ‘Vision’ video.

And so…

I wrote this article in response to a Twitter discussion with someone who was suggesting that cryogenic liquid-hydrogen-fuelled jets would be the zero-emission future of aviation.

I feel that that the idea of a cryogenic hydrogen aircraft is the last gasp of a legacy engine industry that is trying to stay relevant in the face of a fundamentally changed reality.

In contrast, electrical aviation is on the verge of becoming a reality: planes are already flying. And motors and batteries will only get better over coming decades.

At some point, I expect that electrical aviation will reach the point where its capabilities will make conventional kerosene-fuelled aeroplanes uneconomic, first on short-haul routes, and then eventually – though I have no idea how! – on longer routes.

But…

…I could be completely wrong.


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