Solving the N-Body Problem with Numerical Simulation

Conduct gravitational N-body simulations with Norbit, a numerical solution to the N-body problem. Norbit enables you to set up an orbital system with any number of bodies and observe how the system evolves over time.

• Solving the N-Body Problem with Numerical Simulation
• 1. Introduction
  • 1.1 Background
  • 1.2 High Difficulty
  • 1.2.1 Accounting for Warped Spacetime
• 2. Mathematical Formalism
  • 2.1 Problem Statement
  • 2.2 Strategy
  • 2.3 Multidimensional Arrays
  • 2.3 Numerical Implementation
• 3. Core Files
  • 3.1 simulate_orbits.py
  • 3.2 config.yaml
  • 3.3 animator.py
  • 3.4 orbiter.py
  • 3.5 plotter.py
• 4. 10-Body Investigation
  • 4.2 25-Year Euler Simulation
  • 4.3 500-Year Euler Simulation
• 5. Future Roadmap
  • 6. Appendix: Setup for New Developers

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1. Introduction

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1.1 Background

In physics, the N-body problem is about predicting the motions of celestial objects interacting via gravity. It's essential for understanding the orbits of bodies like the Sun, Moon, and planets. In the 20th century, as astronomers discovered more orbiting bodies in the universe, the desire to solve this problem intensified, but a complete analytical solution remains elusive to this day.

These twenty beautiful examples of the 3-body problem from Wikipedia are an infinitesimally small subset of the N-body solution space.

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1.2 High Difficulty

Solving the N-body problem is notoriously difficult because the gravitational interactions between each pair of objects create a highly complex, non-linear system of differential equations that cannot be solved analytically for $N>2$. Additionally, the problem's sensitivity to initial conditions makes long-term predictions highly sensitive to even the smallest perturbations.

An N-body system is an example of a complete graph, a network in which every pair of distinct vertices is connected by a unique edge.

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1.2.1 Accounting for Warped Spacetime

Then, to make things even harder, a truly complete physical solution needs to include general relativity to account time and space distortions. Despite this, the two-body problem and the three-body problem (with restrictions) have been fully solved. Norbit's simulations do not account for warped spacetime.

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2. Mathematical Formalism

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2.1 Problem Statement

Simply put, the problem is:

Given the current position, velocity, and time of celestial bodies, calculate their gravitational interactions and predict their future motions. <!-- TOC -->

2.2 Strategy

We must solve Newton's equations of motion for N separate bodies in 3D. Given a set of positions, the equation below shows how to obtain the 3D force vector experienced by body $i$ in the presence of $j$ other bodies.

The accelerations are numerically integrated to find velocities, and then the velocities are numerically integrated to find positions.

The positions then enable the calculation of potential energies while the velocities corresponding to kinetic energies.

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2.3 Multidimensional Arrays

Norbit approaches this problem using matrix algebra, enabled by multidimensional arrays, and facilitated by pandas and numpy. Think of each moment in time as a layer in a large stack. Each moment is a snapshot of six critical numbers associated with each orbiting body, exactly at that moment: $x$-position, $y$-position, $z$-position, $x$-velocity, $y$-velocity, and $z$-velocity.

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2.3 Numerical Implementation

We need to step the simulation through time, and the lower the step size, the more accurate the simulation. Step size is not the only important factor though. Using different stepping methods can drastically improve the precision of the orbital trajectory calculations. The methods Norbit employs are listed here in ascending order of precision. Refer to orbiter.Orbiter to review the actual algorithms. 1. Euler 2. Euler-Cromer 3. Second-Order Runge-Kutta 4. Fourth-Order Runge-Kutta 5. Velocity Verlet 6. Position Verlet 7. Velocity Extended Forest-Ruth-Like 8. Position Extended Forest-Ruth-Like

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3. Core Files

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3.1 simulate_orbits.py

Run python simulate_orbits.py to interface with this project. config.yaml directs this script on whether to run a new simulation, load an old one, plot results, or transform results into an animated GIF.

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3.2 config.yaml

Define how the script should run. Many of the options are self-explanatory, but if you need clarification, refer to the Simulator class in simulate_orbits.py. Look to config.yaml for a concrete example.

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3.3 animator.py

Animate 3D orbital trajectories with the Animator class, given a solved orbital system.

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3.4 orbiter.py

Calculate (i.e. solve for) 3D orbital trajectories with the Orbiter class.

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3.5 plotter.py

Plot 3D orbital trajectories with the Plotter class, given a solved orbital system.

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4. 10-Body Investigation

We compare the results of a 25-year and a 500-year simulation of a stable orbital system similar to our solar system. Both simulations used the Euler method, which inherently introduces imprecision. As a result, the orbits "smeared" over time. In a stable system, where the total energy is negative, this smearing indicates energy loss over time.

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4.2 25-Year Euler Simulation

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4.3 500-Year Euler Simulation

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5. Future Roadmap

Here are some ideas for future development. - Enable animation of 2D projections. - Enable animation of all graphs. - Enable multithreading or parallelization to speed up calculations. - Create a dynamic connection to the NASA JPL Horizon System to get the most up-to-date planetary data of our solar system. ```python from astroquery.jplhorizons import Horizons

# Example for Earth. obj = Horizons(id=399, location='@sun', epochs='2024-01-01', id_type='majorbody') vectors = obj.vectors()

print(vectors['x'], vectors['y'], vectors['z']) # Position vectors. print(vectors['vx'], vectors['vy'], vectors['vz']) # Velocity vectors.

```

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6. Appendix: Setup for New Developers

If you are fairly new to Python programming, I'd recommend setting up this project by following these steps. If you want more in depth knowledge about environment setup, I'd recommend you read my tutorial on interfacing with the computer like a software developer.

1. Download and install VS Code.

2. Install Python 3.12.4 (☑️ Add python.exe to PATH if you have no other Python versions installed).

3. Install Git bash.

4. Open VS Code.

5. Press F1, and in the command palette, search for Terminal: Select Default Profile and set Git bash as the default terminal.

6. Start a new terminal with Ctrl + `.

7. Clone this repository to a directory where you like to store your coding projects.

8. Open this repository (i.e. the norbit folder) as the current workspace folder with Ctrl + K Ctrl + O.

9. Make sure the terminal path points to the norbit folder, and if it doesn't, navigate there via cd <path_to_norbit_folder>. You can confirm you're in the right spot with quick ls -la command.

10. From the terminal, run pip install virtualenv to install the virtualenv module.

11. Run python -m virtualenv <myenvname> --python=python3.12.4 to create a virtual environment that runs on Python 3.12.4.

12. Activate the virtual environment with source <myenvname>/Scripts/activate.

13. You should see (<myenvname>) two lines above the terminal input line when the environment is active.

14. Press F1 to open VS Code's command palette, then search for Python: Select Interpreter and select Python 3.12.4 64-bit ('<myenvname>':venv).

15. Run pip install -r requirements.txt to install all dependencies on your activated virtual environment.

16. Run simulate_orbits.py.