AI is making spacecraft propulsion more efficient – and could even lead to nuclear-powered rockets

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This article was originally published on The conversation. The publication contributed the article to Space.com Expert voices: opinion pieces and perspectives.

Every year, space companies and agencies launch hundreds of rockets into space – and this number is set to increase significantly with ambitious missions to the Moon, Mars and beyond. But these dreams hinge on a crucial challenge: propulsion – the methods used to move rockets and spacecraft forward.

To make interplanetary travel faster, safer and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is a type of technology that has begun to provide some of these necessary advances.

We are a team of engineers and graduate students who study how AI works in general and a subset of AI called machine learning in particular, can transform spacecraft propulsion. Optimization nuclear heat engines to complex management plasma confinement in fusion systemsAI is reshaping propulsion design and operations. She quickly becomes an indispensable partner in humanity’s journey to the stars.

Machine learning and reinforcement learning

Machine learning is a branch of AI that identifies patterns in data that it has not been explicitly trained on. It’s a vast area with its own brancheswith many applications. Each branch emulates intelligence in different ways: by recognizing patterns, analyzing and generating language, or learning from experience. This last subset in particular, commonly called reinforcement learningteaches machines to perform their tasks by evaluating their performance, allowing them to continually improve through experience.

As a simple example, imagine a chess player. The player does not calculate every move but rather recognizes patterns from a thousand matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a speed and computational scale impossible for humans. It learns through experiments and iterations by observing your environment. These observations allow the machine to correctly interpret each result and deploy the best strategies for the system to achieve its objective.

Reinforcement learning can improve human understanding of deeply complex systems – those that challenge the limits of human intuition. This can help determine the most efficient trajectory for a spacecraft head anywhere in space, by optimizing the propulsion necessary to send the machine there. It can also potentially design better propulsion systemsfrom selecting the best materials to creating configurations that transfer heat between engine parts more efficiently.

Reinforcement learning for propulsion systems

When it comes to space propulsion, reinforcement learning generally falls into two categories: those that help during the design phase – when engineers define the mission needs and system capabilities – and those that support real-time operation once the spacecraft is in flight.

Among the most exotic and promising propulsion concepts are nuclear propulsionwhich harnesses the same forces that power atomic bombs and power the sun: nuclear fission and nuclear fusion.

Fission works by splitting heavy atoms such as uranium or plutonium to release energy – a principle used in most terrestrial nuclear reactors. On the other hand, the merger fuses lighter atoms like hydrogen to produce even more energy, although this requires much more extreme conditions to be initiated.

Fission is a more mature technology that has been tested in some space propulsion prototypes. It has even been used in space in the form of radioisotope thermoelectric generatorslike those who powered the Voyager probes. But fusion remains a tempting frontier.

Thermal nuclear propulsion could one day take spacecraft to Mars and beyond at a lower cost than simply burning fuel. This would allow a ship to be brought there more quickly than electric propulsionwhich uses a heated gas made of charged particles called plasma.

Unlike these systems, nuclear propulsion relies on heat generated by atomic reactions. This heat is transferred to a propellant, usually hydrogen, which expands and exits through a nozzle to produce thrust and propel the craft forward.

So how can reinforcement learning help engineers develop and exploit these powerful technologies? Let’s start with the design.

The role of reinforcement learning in design

Early nuclear thermal propulsion models from the 1960s, such as those in the NASA project NERVA programused solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations – from ceramic pebble beds to grooved rings with complex channels.

Why were there so many experiments? Because the more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates.

This is where reinforcement learning has proven essential. Optimizing geometry and heat flow between fuel and propellant is a complex problem, involving countless variables – from material properties to the amount of hydrogen passing through the reactor at any given time. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer. Think of it as a smart thermostat, but for a rocket engine – one that you definitely don’t want to stand too close to, given the extreme temperatures involved.

Reinforcement learning and fusion technology

Reinforcement learning also plays a key role in the development of nuclear fusion technology. Large-scale experiments like Tokamak JT-60SA in Japan are pushing the boundaries of fusion energy, but their massive size makes them impractical for spaceflight. This is why researchers are exploring compact designs such as polywells. These exotic devices look like hollow cubes, measuring about a few centimeters in diameter, and confine plasma in magnetic fields to create the conditions necessary for fusion.

Controlling magnetic fields in a polywell is no small feat. The magnetic fields must be strong enough to allow the hydrogen atoms to bounce around until they fuse – a process that requires immense energy to start, but can become self-sustaining once started. Meeting this challenge is necessary to extend this technology to nuclear thermal propulsion.

Reinforcement learning and energy generation

However, the role of reinforcement learning does not stop at design. This can help manage fuel consumption – a critical task for missions that must adapt on the fly. In today’s space industry, there is growing interest in spacecraft that can fulfill different roles depending on mission needs and how they adapt to changing priorities over time.

Military applications, for example, must respond quickly to changing geopolitical scenarios. An example of technology adapted to rapid changes is Lockheed Martin’s LM400 satellite, equipped with various capabilities such as anti-missile warning or remote sensing.

But this flexibility introduces uncertainty. How much fuel will a mission require? And when will he need it? Reinforcement learning can help with these calculations.

From bicycles to rockets, learning through experience – whether human or mechanical – is shaping the future of space exploration. As scientists push the boundaries of propulsion and intelligence, AI is playing an increasing role in space travel. This could help scientists explore inside and beyond our solar system and pave the way for new discoveries.