Author Archives: Niko Porjo

About Niko Porjo

Joys: comedy, cycling and spotting argumentation errors in live speech. Physicist and thinker, a little bit of an amateur philosopher. I like to build things but when they work that’s enough, no polishing.

Nuclear propulsion

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I’ve been designing a Mars mission with a nuclear rocket. Admittedly this might be a bit much for a one man operation. It grew out of desire to render a NERVA II rocket engine with blender. Although I’m not known to be detail oriented the things that I try to model should look at least a little bit like they might look if they were actually made some day, so I used “existing” hardware to estimate weight of a spaceship and then plug the numbers in to the rocket equation. After some tuning I came up with a two stage space tug that has about 1.5 Gg of mass at low earth orbit. This contraption should be able to transfer five BA 330 modules and 200 Mg of cargo to Mars orbit.

The propulsion unit for my design. It needs six.

A propulsion unit for my design, it needs six.

Before anyone gives harsh critique on the numbers: this is a very notional design: I’d be happy if the numbers are within an order of magnitude of the correct ones. And did I mention that much of the stuff is sort of vaporware or less real.

Close up of the NERVA II. To show the scale, the big cube is 10^3 m3, the green speck is 0.1^3 m3. The Hydrogen tank is almost 47 m long.

Close up of the NERVA II. To show the scale, the big cube is 10^3 m3, the green speck is 0.1^3 m3. The Hydrogen tank is almost 47 m long.

I like physics, I like rockets and almost anything space related, so for me this type of thinking by doing is fun. What is more surprising is that some quite serious people have thought that this could actually be done. Nuclear rocket engines have been proposed and tested ( “direct” nuclear jet engines too).

“Steady progress was made in engine efficiency and controllability, and in lowering the release of radioactivity” [from here]. Just to make it clear these beasts were no sissy nuclear electrics, the idea was to spray a hot reactor core with hydrogen. Several designs were tested in the atmosphere. The word that surely comes to mind when thinking about this sort of engine test is erosion. One would expect that active parts of the core would be spewed out of the hot end even in normal operation.

There is always the possibility of the not so unlikely turbopump failure. While my limited knowledge suggests that because there is no need for an oxidizer it is a bit easier to design one, the eventual pump failure could still lead to a loss of coolant. Not to worry, they tested (KIWI TNT) what happens if you stop the coolant. Boom.

While my design sketch is a space tug, i.e. it would never be used in the atmosphere thus limiting the release of radioactive substances to the biosphere, these engines were also suggested as upper stages for chemical rockets to boost performance. Then there is of course Project Orion, which from the current viewpoint boggles the mind.

No point, just some perspective.

Niko Porjo on Google+

Billionaire maps

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Wikipedia lists the number of billionaires by country. Map 1 shows how these are distributed around the world. Note that billionaires are not all the same, some only have one, others may have many billions of personal wealth, but this is disregarded here. Also, heads of countries are not taken into account regardless of their degree of omnipotence.

In earlier maps I divided the countries in to ten equal sized groups but in this case there were so few values that it didn’t work well. So in this case I made a list of all the different values and divided those to ten about equal sized groups and then populated those groups with the countries that had that number of billionaires. This results in groups that are not equal in size nor do they cover the same portion of the total range, but they do have about the same number of different values in them.

Billionares_MDecilesMap 1. Number of billionaires in a country. Wikipedia (Feb 2013).

Map 1 tells mostly that there aren’t that many billionaires in Africa, which fits well with my preconceptions. It’s quite obvious that billionaires are at the tail of the wealth distribution. It’s therefore quite natural to think that there are more billionaires if there are a lot of people and/or if the GDP (nominal, the IMF numbers) is high (both Wikipedia links accessed Feb 2013). For GDP I used the nominal one as  going for a purchasing power parity correction didn’t seem relevant from a billionaires point of view.

Figure 1 shows the relationship between number of billionaires and the population of a country. Sure enough if there are more people there are more billionaires, but there is also a lot of noise. Figure 2 shows the same for GDP,  the linear fit is much better. For completeness’ sake Figure 3 shows the relationship between GDP and population.

NumberOfBillionares_vs_PopulationFig 1.

NumberOfBillionaires_vs_GDPFig. 2

GDP_vs_PopulationFig 3.

Maps 2 through 4 show how the situation changes when size of a country’s population is accounted for, when the GDP is accounted for and when both of the are taken in to account.

Number of billionaires per GDP could be considered to be a rough indication of extremeness of wealth distribution, if for a fairly small GDP there are a lot of billionaires much of the money is in only a few hands. it should also show countries where billionaires go after they have made their fortune.

Number of billionaires per both population and GDP seems a like weird quantity. That is, because GDP times population doesn’t seem to have an obvious explanation. But it could be considered as an indication of political power, as richness and a lot of people both indicate that the country in question likely will be listened to when it has concerns. Number of billionaires per this quantity would then be the inverse of political power per billionaire, which if you think that money equals also political power and not only influence on people is surely something interesting.

 

Billionares per 100k population_MDecilesMap 2. Number of billionaire per 100 k people.

Billionares per GDP_MDecilesMap 3. Number of billionaires per GDP [G$].

Billionares per 100k population per GDP times 1k_MDecilesMap 4. Number of billionaires divided by both GDP [G$] and population of the country times 1000.

NumberOfBillionares_HISTFig 4. Histogram of the number of billionaires per GDP quantity. It’s mostly here because Jakke is going to ask for it. It is very fat tailed, though there aren’t that many points.

Niko Porjo on Google+

Banking access maps

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Map week continues. I stumbled upon paper with a table showing banking sector branch and ATM penetration. It’s called “Reaching out: Access to and use of banking services across countries” and was written by Beck et al from World Bank. Unfortunately It’s behind a paywall.

What the authors have attempted among other things is to assess how easily people can access banking services. I was interested because of two things. First any sort of decent standard of living probably includes easy bank access. it’s difficult to buy anything large, like a home or to save for anything, like old age if you don’t have access to a bank.

The second thing is that from my perspective an ATM is ancient tech and this paper was written in 2006 which is not that long ago. I think this can be explained by two things: technology has advanced rather fast and living in a rich western country makes things easy. I can also add that visiting an actual physical bank feels like a waste of time. This is also why I think that these penetrations don’t really tell what the authors hope they would. I suspected that even during 2006 access to a physical bank had lost value as a proxy to good standard of living. However, Figure 1 shows a fair linear relationship between demographic ATM penetration and GDP per capita.

ATM penetration vs GDPFigure 1. Demographic ATM penetration vs. GDP per capita

 

Geographic branch penetrationMap 1. Geographic branch penetration. Branches per 1000 km2.

 

Demographic branch penetrationMap 2. Demographic branch penetration. Branches per 100k people.

 

Geographic ATM penetrationMap 3. Geographic ATM penetration. ATMs per 1000 km2.

 

Demographic ATM penetrationMap 4. Demographic ATM penetration. ATMs per 100k people.

Niko Porjo on Google+

Refugee maps

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Map week continues. The first one was perhaps a bit boring. Mostly because the data was not normalized by area of each country. But you can still say something about the state of the economies.

Some data is better shown when split to categories. So, I made an algorithm that finds deciles from the data and colors the countries according to how they compare against these.

Refugees from country_DecilesMap 1. Number of refugees from country. Data from Wikipedia (Feb 2013)

From Map 1 it can easily be seen that refugees come from all over the globe, but again it should be noted that no correction for population is made. So for example from the numbers available in the map, when corrected for population Finland and the US could have the same number of refugees per capita. Refugees from the US number over 500 times more than from Finland, which is a much larger factor that the ratio of populations would suggest.

This type of map also conceals the fact that there are a couple of countries in the last group that have sent out a lot of refugees.

Refugees_from_countryMap 2. Origin of refugees, linear scale. Data from Wikipedia (Feb 2013)

Map 1 and Map 2 are based on the same data, but Afghanistan, Iraq and a couple of others really stand out from Map 2.

Where did the refugees go?

natives_per_refugee_DecilesMap 3. Natives per refugee population in that country. Data from Wikipedia (Feb 2013)

Map 3 also tells something about where refugees want/can go and what sort of burden they are for the host country. It doesn’t tell anything about how rich the receiving country is so the burden will still varies.

Color selection in the maps is all mine and I apologise. I have pretty much no artistic talent or taste for that matter. Perhaps I need to make an algorithm that calculates which colors would give maximum contrast between the groups. Then I could say that the selection is based on something other than me manually inserting hex values that seem to be far from each other.

 

 

Niko Porjo on Google+

Number of TV stations

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countries tv stations_LogMap 1. Number of TV stations per country. Colors are set by calculating log(N+1), so the scale is not linear.

The map is based on Wikipedia’s list of countries by number of television broadcast stations (Feb 2013). I made it using my own Python script and it is based on this map. As the original map is in svg format it is fairly easy to add more text definitions to it in order to change colors and add the color bar.

The Executions map was made using Google’s tools, but I found the experience a bit cramped. I did look around to find a suitable tool set in the web, but there seemed to be something wrong with every one. Since I have wanted to do this for a long time I decided to go for it.

Why TV stations? It was the first list I found in Wikipedia that had many countries in it.

Edit: Map was updated 7 Feb. I found a bug in the code, it may have had an effect on the map.

Niko Porjo on Google+