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 Talk. The publication posted an article on Space.com. Voices of Experts: Review and Insights.

Every year, companies and space agencies launch hundreds of rockets into space – and that number is set to skyrocket thanks to ambitious missions to the Moon, Mars and beyond. But those dreams depend on one major issue: propulsion—the methods used to propel rockets and spacecraft forward.

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

We are a team engineers and graduate students who study how AI in general and its subset called machine learning in particular, it can change the propulsion system of spacecraft. From optimization nuclear thermal engines managing complex plasma confinement in fusion systemsAI is changing the design and operation of power plants. He quickly becomes humanity's indispensable partner on the 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. This is a huge field with its brancheswith a lot of applications. Each branch simulates intelligence in different ways: by recognizing patterns, analyzing and generating language, or learning from experience. In particular, this last subset is commonly known as reinforcement learningteaches machines to perform their tasks by assessing their performance, allowing them to continually improve based on experience.

As a simple example, imagine a chess player. The player does not calculate every move, but rather recognizes patterns after playing a thousand matches. Reinforcement learning creates similar intuitive experiences in machines and systems, but at a computational speed and scale not available to humans. He learns through experience and iteration. observing the environment. These observations allow the machine to correctly interpret each result and use the best strategies to help the system achieve its goal.

Reinforcement learning can improve human understanding of highly complex systems—those that challenge the limits of human intuition. This may help determine the most effective trajectory of the spacecraft is directed to any point in space, and it does this by optimizing the thrust required to send the ship there. This could also potentially develop better propulsion systemsfrom choosing the best materials to creating configurations that transfer heat between engine parts more efficiently.

Reinforcement learning for motor systems

For space propulsion, reinforcement learning typically falls into two categories: those that help during the design phase, when engineers determine mission needs and system capabilities, and those that support work in real time once the spacecraft is in flight.

Some of the more exotic and promising propulsion concepts include nuclear propulsion systemwhich uses the same forces that power atomic bombs and power the sun: nuclear fission and nuclear fusion.

Fission occurs by splitting heavy atoms. such as uranium or plutonium to release energy, a principle used in most land-based nuclear reactors. Merger, on the other hand, combines lighter atoms for example, hydrogen to produce even more energy, although it requires much more extreme conditions to run.

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

Nuclear thermal propulsion system could one day take spacecraft to Mars and beyond at a fraction of the cost of simply burning fuel. This would get the ship there faster than electric tractionwhich uses a heated gas of charged particles called plasma.

Unlike these systems, nuclear propulsion is based on the heat generated by atomic reactions. This heat is transferred to the fuel, usually hydrogen, which expands and exits through the nozzle, creating thrust and propelling the ship forward.

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

The Role of Reinforcement Learning in Design

Early nuclear thermal engine designs from the 1960s, such as those from NASA. NERVA programsolid uranium fuel was used, molded into prismatic blocks. Since then, engineers have explored alternative configurations, from ceramic pebble layers to grooved rings with complex channels.

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

This is where reinforcement learning has proven its importance. Optimizing the geometry and heat flow between fuel and fuel is a complex problem involving countless variables, from material properties to the amount of hydrogen flowing through the reactor at any given time. Reinforcement learning can analyze these design options and identify configurations that maximize heat transfer. Think of it like a smart thermostat, but for a rocket engine – which you definitely shouldn't stand too close to, given the extreme temperatures.

Reinforcement learning and fusion technology

Reinforcement learning also plays a key role in the development of nuclear fusion technologies. Large scale experiments such as tokamak JT-60SA Japan is pushing the boundaries of fusion energy, but their enormous size makes them impractical for spaceflight. That's why researchers are exploring compact structures such as polywells. These exotic devices look like hollow cubes about a few inches in diameter and trap plasma in magnetic fields, creating the conditions necessary for nuclear fusion.

Control of magnetic fields in polywell, this is no small feat. The magnetic fields must be strong enough to keep the hydrogen atoms moving until they fuse, a process that requires enormous energy to start but can become self-sustaining once it starts. Overcoming this problem is necessary to scale up this technology for use in nuclear thermal engines.

Reinforcement learning and energy production

However, the role of reinforcement learning is not limited to design. This could help control 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 serve different roles depending on mission needs and how they adapt to priority changes over time.

For example, military applications must respond quickly to changing geopolitical scenarios. An example of technology that has adapted to rapid change is Lockheed Martin LM400 a satellite that has various capabilities such as missile warning or remote sensing.

But this flexibility introduces uncertainty. How much fuel will the mission require? And when will it be needed? Reinforcement learning can help with these calculations.

From bicycles to rockets, learning through experience—whether human or machine—is shaping the future of space exploration. As scientists push the boundaries of motor performance and intelligence, AI is playing an increasingly important role in space travel. This could help scientists explore our solar system and beyond and open the gates to new discoveries.