Affordable Transport to the Moon
Justin Mansell and Samantha Dickmann
The launch is often the shortest and most expensive phase of a satellite’s mission. That’s why many launches carry not just one but multiple spacecraft. Usually, multiple small satellites will ride into orbit alongside a larger payload such as a telecom satellite or cargo vehicle. This allows companies, universities, and other organizations to fly small but cost effective experiments without having to pay for an entire launch.
Small modular satellites called “CubeSats” have already taken low Earth orbit by storm using this inexpensive approach. But can it also be extended to support growing interest in exploring the moon? How can small satellites reach the Moon efficiently if they are launched as secondary payloads to larger spacecraft that may have very different mission objectives?
A new generation of upper stages such as the Advanced Cryogenic Evolved Stage will provide the ability to restart their rocket engines after weeks or even months on orbit. Combine this with natural orbit perturbations from the Earth’s oblateness and a strategy of coasting until the original orbit lines up just right with the Moon can be surprisingly effective.
This research concerns the optimization of lunar transfers that could be useful for small satellites reaching the moon. A particular type of starting orbit called a Geostationary Transfer Orbit (GTO) is emphasized. GTOs are used to deliver telecom and weather satellites to their operational orbits. These orbits experience high traffic and are therefore prime candidates for carrying small secondary payloads. With a maximum altitude of 35,786 km they also already contain much of the momentum needed to reach the moon.
There’s just one complication: radiation. GTOs pass through both of the Van Allen radiation belts. Whereas most satellites only remain on a GTO for half an orbit, a secondary payload lingering on a GTO for weeks could be at risk of having its electronics fried. In addition to optimization this research uses machine learning to make statistical predictions of the radiation dose accumulated from multiple passes through the belts. Trajectories that receive a dangerously high dose are filtered out from solutions or adjusted so that they spend less time in the most intense regions of the belts.
The resulting trajectories to the moon could provide a key stepping stone to developing a cis-lunar economy.
Video music credit: "Dawn" by Andrew Odd.