Generating Terrestrial Planets Pt.1

This is related to Semi-Empirical Stellar Equations. I have had some questions easily answered, and others which are difficult or impossible to get the kind of accuracy I want.

I’m going to order what I’ve learned by the order I am planning to use these equations, marking equations loosely based on empirical data with an asterisk, and double asterisks for things I’ve completely made up to simplify calculation.

  • Radius of Surface* (Rsurface):
    5500km mean, 1550km standard deviation
  • Density* [based on common rock types] (Dplanet):
    4.7g/cm3 mean, 0.55g/cm3 standard deviation
  • Volume [assumes a perfect sphere] (Vplanet):
    4/3 * π * Rsurface3
  • Mass (Mplanet):
    Dplanet * Vplanet
  • Surface Gravity (gsurface):
    6.673×10-11Nm2kg-2 * Mplanet / Rsurface2
  • Surface Area [assumes a perfect sphere]:
    4 * π * Rsurface2
  • Atmospheric Pressure Reduction Rate** (Pr):
    Pr [unit: none] = -0.00011701572 [unit: s2 / m2] * gsurface
  • Atmospheric Halving Height** (hd):
    hd [unit: m-1] = ln(2) / Pr
  • Atmospheric Volume at Constant Pressure** (Vsim):
    (4/3 * π * (Rsurface + hd)3 – Vplanet) * 2
  • Surface Atmospheric Pressure (Psurface):
    Psurface [unit: kPa] = ???
  • Lowest Safe Orbital Height** (h0):
    h0 [unit: m] = ln(1.4×10-11 / Psurface) / Pr
  • True Atmospheric Volume (Vatm):
    4/3 * π * (Rsurface + h0)3 – Vplanet

Vsim exists to support a simulation of an entire planet’s atmospheric contents using my simple fluid simulation mechanic. The magic constant used to calculated Pr is based on Earth’s atmosphere, it can be changed to set an exact “atmospheric height” while maintaining other properties of this simulation.

  • Atmospheric Pressure at Altitude** (Paltitude):
    Paltitude [unit: kPa] = Psurface * exp(Pr * maltitude) [Pr may be -Pr, I don’t remember]

This post is obviously incomplete, but it’s been a while since I posted anything here, and I felt like I should go ahead and share what I’ve been up to.

Semi-Empirical Stellar Equations

I’ve spent a lot of time trying to answer certain questions in astronomy, where I just want a rough approximation for the purpose of a simulation, and don’t need an exact answer. These are some of the equations I’ve come up with.

Temperature (K) → Absolute Magnitude (visual):
Mv = 35.463 * exp(-0.000353 * T)
Roughly accurate between 2000-50000 Kelvin. Probably doesn’t continue accuracy at hotter temperatures.

Absolute Magnitude (visual) → Luminosity (solar luminosities):
Lsun = 100 * exp(-0.944 * Mv)
Accurate between 0-10 Mv. Probably continues accuracy relatively well.

Spectral (UBVRI) Filters:
Ultraviolet, Blue, Visual, Red, Infrared.
Objects are listed in different indexes:
UV for the hottest objects (stellar remnants, galaxies), then BV (the majority of stars), and RI for the coolest (LTY “stars” and below).

B-V index (x) → Temperature (K):
T = -772.2x3 + 3152x2 – 6893x + 9500
Sorta accurate between 0-2. (This equation I am least comfortable with, and don’t plan to use.)

Main Sequence Luminosity/Mass Relation:
Lstar / Lsun = (Mstar / Msun)3.5


Yerkes Classes’ T/Mv Relations

These equations are shown as functions where x is temperature in Kelvin, and f(x) is absolute visual magnitude. Most are valid from 2400 K to a little bit past 30000 K, the exceptions are noted. For the hypergiants and white dwarfs, the range is within an elliptical region, of which these functions define the major axis; for all others, they are a center-point along a Yerkes classification.

Hypergiants (0):
f(x) = -8.9
Supergiants (Ia):
f(x) = -0.00135x3 + 0.0233x2 + 0.0187x – 7.349
Supergiants (Ib):
f(x) = 0.00329x3 – 0.0962x2 + 0.829x – 7.209
Bright Giants (II):
f(x) = 0.00557x3 – 0.166x2 + 1.505x – 6.816
Giants (III):
f(x) = 0.0135x3 – 0.373x2 + 3.019x – 7.233
Subgiants (IV):
f(x) = -0.151x2 + 2.216x – 5.128 (4450-100000 K only)
Dwarfs (V):
f(x) = 0.00193x5 – 0.0615x4 + 0.742x3 – 4.257x2 + 12.439x – 12.996 (note: this one is the least accurate)
Subdwarfs (VI):
f(x) = 0.131x3 – 3.275x2 + 27.576x – 71.7 (3050-6000 K only)
White Dwarfs (VII):
f(x) = 0.489x + 10.01 (4450-30000 K only)

Better Fluid Storage

A while back, I posted a prototype fluid storage system with a mechanic for handling breaches in a pressurized system. I thought I’d be clever by storing fluids as a percentage of a defined volume and pressure. For a “simplified” system, it was quite complicated, and fundamentally flawed.

This time, it is straightforward. Keep track of the amounts of each fluid, a total sum, and volume of the container. Pressure is the sum divided by the volume, and the percent of a fluid is its amount divided by the total sum of all fluids.

tank = {
  volume: 200, sum: 300,
  contents: { hydrogen: 200, oxygen: 100 }
}
pressure = tank.sum / tank.volume -- 1.5
percent_hydrogen = tank.contents.hydrogen / tank.sum -- 0.67

Everything needed from a container can be accessed immediately or with a single calculation involving only two variables.

But what about hull breaches?

Fluids vs Mechanical Classes

I realized that I should define fluid containers very narrowly, all they need care about is a small set of numbers, and have a few functions to modify that state. Enter the Breach class.

Breach(fluidA, fluidB, volume)

Specify which fluid containers are interacting and the volume ( I guess technically it should be area) of the breach. Each update cycle moves the pressure difference multiplied by the volume (area) of the breach from the higher pressure container to the lower pressure container.

What about pumps? I have those, with a “volume” and a “rate” modifier to allow you to adjust how fast the pump works. Pumps only work in one direction, but have a function to reverse them.

Want only one fluid to go through..say, a filter? Made that as well. Valves, so that you can adjust flow rate, filter-pump combos for pushing just the right amount of one fluid, and one-way valves to allow pressure to escape but not allow any blowback.

The Flaws

  • Once pressure is equalized, contents do not mix between fluids.
  • All fluids have the same density. This probably isn’t that hard to fix, but is unneeded for my purposes.
  • All fluids mix. This may or may not be harder to fix depending on how it is approached.
  • Temperature isn’t simulated at all. I would love to have heat transfer and heat affecting density, but these details are not necessary for my current project.

The Code

As of publishing this article, I don’t have a library to give you, but I will update it as soon as I do release it. For now, here is where I have the beginnings of a library. No matter what, I can promise it will be available by the end of April (or upon request).

Simplified Fluid Storage System

One of my game ideas involves constructing 2D spaceships, and the concept of a simplified system for storing fuel, oxygen, water – really any kind of fluid mixture – in storage tanks. Along with this, it allows simulating breaches between containers, hard vacuum, and the pressurized areas of the ship itself!

{ -- a rough approximation of Earth's atmosphere
  pressure: 1
  volume: 4.2e12 -- 4.2 billion km^3
  contents: {
    nitrogen: 0.775
    oxygen: 0.21
    argon: 0.01
    co2: 0.005
  }
}

{ -- hard vacuum
  pressure: 0
  volume: math.huge -- infinity
  -- the contents table will end up containing negative infinity of anything that leaks into the vacuum
}

It all comes down to storing a total pressure and volume per container, and a table of contents as percentages of the total mixture. The total amount of mass in the system can be easily calculated (volume * pressure), as can the amount of any item in the system ( volume * pressure * percent).

Breaches are stored as a reference in the container with a higher pressure, and a size value is added to the container with lower pressure (representing the size of the hole between them).

Limitations

  • Everything has the same density and mixes evenly.
  • There are no states of matter, everything is treated as a gas.
  • Attempting to directly modify the amount of a fluid is prone to floating-point errors it seems, while mixing containers via the breach mechanic is working as expected.

The code was written in MoonScript / Lua and is available here.

Realistic Star Frequencies/Colors

UPDATE: A much better resource for frequencies of types of stars is on this wiki page for Elite: Dangerous.

I’ve done a lot of research on stellar classification, perhaps too much. Here’s a visual representation (not at all to scale!) of stars by type from O to L.. and L is a brown dwarf, so it’s not technically a “star” per say.

Stars by type, in order: OBAFGKML

And here’s a table of those color values (in percents RGBA):

  O: {0.521, 0.521, 0.996, 1}
  B: {0.701, 0.898, 1, 1}
  A: {1, 1, 1, 1}
  F: {1, 0.996, 0.780, 1}
  G: {0.988, 0.972, 0.250, 1}
  K: {0.905, 0.549, 0.015, 1}
  M: {0.847, 0.011, 0.031, 1}
  L: {0.302, 0.187, 0.177, 1}

As for the frequencies (as in, percent of stars that are a specific type), they have been slightly manipulated (if I am remembering correctly) to make the rarest types a little more common, but are based on my best guesses from research:

  O: 0.00003
  B: 0.13
  A: 0.6
  F: 3
  G: 7.6
  K: 12.1
  M: 76.45
  L: 0.11

A couple last notes:

  • There are two more brown dwarf / substellar mass classifications I could be using (T and Y), but I am not because they are not commonly visible.
  • Because L-type (and T & Y) “stars” are so hard to see, it is entirely possible they could outweigh the existence of other types, but we just can’t see them!

Code to create the image above is available here.