To Arrive at Mars, Pick up Fuel in Space
Engineering 360 News Desk | October 15, 2015A new MIT study says that fueling in Earth orbit from a moon-based supply station would streamline initial cargo requirements.
A new MIT study suggests that a Martian mission may lighten its launch load considerably by refueling on the moon. Credit image: Christian Daniloff, MITPrevious studies suggested mining lunar soil and ice in craters to convert to fuel. Thus, using a moon base to supply fuel for the Mars journey would reduce a Mars mission’s launch mass by 68%.
The MIT group determined the optimal route to Mars and found that the most mass-efficient path involves launching with sufficient fuel to reach the orbit around the Earth. A fuel-producing plant on the moon would then launch tankers of fuel into space that would enter gravitational orbit and be picked up by the Mars-bound crew.
Olivier de Weck, a professor of aeronautics and astronautics and of engineering systems at MIT, says that while the plan is a departure from U.S. space agency NASA’s carry-along approach, a resupply strategy could be affordable in the long term.
The study, based on the doctoral thesis by Takuto Ishimatsu, is published in the Journal of Spacecraft and Rockets. Both de Weck and Ishimatsu say that, as budgets are constrained and destinations are far away from Earth, a well-planned logistics strategy becomes imperative.
Ishimatsu developed a network flow model to explore various routes to Mars to minimize the mass that would be launched from Earth, even when accounting for the mass of a fuel-producing plant, and spares that would need to be pre-deployed.
He also developed a mathematical model that improves on a conventional model for routing vehicles. He adapted the model for the more complex scenario of long-term missions in space. Ishimatsu says that his research demonstrates the importance of establishing an infrastructure, given what is expected to be colonization and repeated trips to and from Mars.
News Articles:
Toyota to Cooperate with MIT, Stanford on AI Research