Deorbiting simulation with GMAT
GMAT code can be found here.
Introduction
Increased concerns with space debris increases effort to ensure that satellites are disposed of responsibly at the end of the operations. According to ESA’s “Space Debris Mitigation Requirement”, if spacecraft use natural orbital decay for deorbiting, they have to undertake deorbiting within 5 years or less
This reduces collision risk and assists in sustaining a sustainable orbital environment. One finding from this is understanding how atmospheric drag affects the deorbiting timeline of the satellites in the low earth orbit, LEO.
GMAT (General Mission Analysis Tool) is used in this project to simulate the deorbiting process is when atmospheric drag was acting on a satellite. Then analyzed with Python to visualize crucial orbital parameters over time, such as altitude, eccentricity, and semi-major axis.
This is the main theme of this blog post, which would shed light on the methodology, results, and implication of our analysis, that could give ideas to engineers and scientists to prepare themselves in operating within increasingly crowded orbital environments.
Methodology
Step 1: Setting up the GMAT Simulation
Begin by configuring a spacecraft (Sat1) in GMAT with the following initial parameters:
- Epoch: January 1, 2025
- Initial Position (X, Y, Z): (4.211560342267748e-13, 6878 km, 0)
- Initial Velocity (VX, VY, VZ): (0.9541223000883489, -5.842314103991534e-17, 7.552655699128404)
- Dry Mass: 3 kg
- Drag Coefficient (Cd): 2.2
- Cross-sectional Area for Drag: 0.01281 m²
- Solar Radiation Pressure Area (SRP): 0.01 m²
Additionally, configure the force model to include Earth’s gravity (degree and order 4), atmospheric drag using the MSISE90 model, and other environmental factors such as solar flux (F10.7 = 150) and geomagnetic activity (Magnetic Index = 3).
The propagator is set up with a Runge-Kutta 89 integrator, ensuring high accuracy during the simulation. The propagation continues until the satellite reaches a reentry altitude of 120 km.
Step 2: Generating and Analysing Data
After running the simulation in GMAT, the output data is saved in a report file (e.g., AltitudeReport.txt). This file contains detailed information about the satellite’s orbital parameters over time, including:
- Altitude
- Elapsed Days
- True Anomaly (TA)
- Eccentricity (ECC)
- Argument of Perigee (AOP)
- Semi-Major Axis (SMA)
- Inclination (INC)
- Right Ascension of the Ascending Node (RAAN)
Load this data into Python using pandas and visualise it using matplotlib. Below are some of the key visualisations generated from the data.
Results
1. Satellite Altitude vs Time
The first plot shows the satellite’s altitude decreasing over time due to atmospheric drag. The simulation indicates that the satellite takes approximately 5.01 years to deorbit from its initial altitude of 500 km to the reentry altitude of 120 km.
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2. True Anomaly vs Time
The true anomaly (TA) represents the angle between the satellite’s position and its perigee. Over time, the true anomaly oscillates, reflecting the satellite’s orbit evolution under the influence of drag.
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3. Eccentricity vs Time
The eccentricity of the orbit gradually increases as the satellite descends, indicating that the orbit becomes more elliptical due to atmospheric drag.
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4. Semi-Major Axis vs Time
The semi-major axis decreases steadily over time, showing the shrinking orbit as the satellite loses energy due to drag.
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5. Inclination and RAAN vs Time
The inclination and right ascension of the ascending node (RAAN) remain relatively stable throughout the simulation, as these parameters are less affected by atmospheric drag.
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Discussion
The results of the model simulation show that atmospheric drag is rather influential on satellite orbits in LEO. Some important findings are as follows:
-The satellite’s altitude has a continuous downward trend, indicating that the satellite will have reentry into the atmosphere within nearly 5 years. -That the eccentricity thus indicates the orbit’s future time, which continues to become more elliptical. -Parameters such as inclination and RAAN are not greatly affected, showing their relative independence with respect to drag.
These results, therefore, underpin the importance of provision for deorbiting measures in satellite design as more and more objects come into LEO.
Conclusion
Through GMAT orbital mechanics simulations and Python data analyses, these results presented key insights into atmospheric drag dynamics and influence on satellite orbits. Such analyses are critical for the sustainable continuation of space operations and mitigation against space debris risks.
Future work might include such variables as solar activity variations, different satellite types, and other deorbiting technologies like propulsion systems or tethers.