Each year, aerospace companies and government agencies launch hundreds of rockets into space, and that number is set to rapidly increase with new missions to the Moon, Mars, and beyond. But these ambitions hinge on one important challenge: the propulsion technology that powers rockets and spacecraft. Scientists need revolutionary breakthroughs to make interplanetary travel faster, safer and more efficient. And AI is already starting to provide that.
We are a team of engineers and graduate students looking for ways to do just that. artificial intelligenceAnd that field in particular, known as machine learning, can change the propulsion of spacecraft. From improving nuclear thermal engines to mastering plasma confinement in fusion systems, AI is redefining both rocket engine design and operation. It is rapidly becoming an essential partner in humanity’s pursuit of the stars.
•Marcos Fernandez Toss
• Preity Nile
• Sai Susmita Gudanti
• Sreejith Vidyadharan Nair
Machine learning and reinforcement learning
Machine learning is a field of AI that detects patterns in data without explicit programming. This is a vast field with numerous subfields and applications. Each mimics human intelligence in different ways, including recognizing patterns, processing language, and learning from experience. Finally, reinforcement learning helps machines learn through trial and feedback, improving performance over time.
Think of a chess player. Rather than calculating every move, they recognize familiar patterns after thousands of games. Reinforcement learning develops similar intuitive skills in machines, but at a speed and scale that far exceeds human capabilities. By observing the environment, the system learns from any outcomes and adjusts its strategy to achieve its goals.
This approach can enhance our understanding of highly complex systems that challenge the limits of human intuition. You can calculate the most efficient route for your spacecraft and determine the thrust needed to get there. It also guides engineers in designing better propulsion systems, from choosing materials to optimizing how heat is transferred between engine components.
Reinforcement learning for propulsion systems
When applied to space propulsion, reinforcement learning typically serves two purposes: Assisting the system. design It supports real-time operations when engineers plan missions and after the spacecraft is in flight.
One of the most promising ideas in modern propulsion is nuclear propulsion, which harnesses the same forces that power the sun and the atomic bomb: nuclear fission and fusion.
Nuclear fission, as used in most nuclear reactors on Earth, splits heavy atoms such as uranium and plutonium to release energy. In contrast, nuclear fusion combines lighter atoms such as hydrogen to release even more energy, but requires more extreme conditions.
Nuclear fission is already a mature technology, tested in early propulsion prototypes and used in radioisotope thermoelectric generators such as those powering the Voyager spacecraft. But fusion remains an exciting frontier.
Nuclear thermal propulsion is cheaper than traditional fuel systems and much faster than electric propulsion, which relies on a gas of heated charged particles called plasma, and could one day take spacecraft to Mars and beyond.
Unlike these systems, nuclear propulsion relies on heat produced by atomic reactions and transferred to the atoms. propellantUsually hydrogen, it expands and is ejected from a nozzle to create thrust.
So how can reinforcement learning help engineers design and control these powerful systems? It starts with the design process.
The role of reinforcement learning in design
Similar to NASA’s NERVA program (Nuclear Engine for Rocket Propulsion), the first nuclear thermal propulsion prototypes in the 1960s used solid uranium fuel formed into prismatic blocks. Since then, engineers have experimented with ceramic bead beds and grooved rings containing intricate channels.
Why are there so many variations? The better the reactor transfers heat from the fuel to the hydrogen, the stronger its thrust will be.
This is where reinforcement learning proves invaluable. Optimizing the geometry and heat flow between the fuel and propellant involves thousands of variables, from material properties to hydrogen flow rates. Reinforcement learning can evaluate all of these factors to identify a design that maximizes heat transfer. Imagine a smart thermostat for a rocket engine. However, you don’t want to get too close.
Reinforcement learning and fusion technology
Reinforcement learning also plays an important role in progress fusion Promotion. Large-scale experiments such as Japan’s JT-60SA tokamak are pushing the limits of fusion energy, but their sheer size makes them unsuitable for spacecraft. So researchers are exploring compact systems like polywells, small hollow cubes that confine plasma within a magnetic field to trigger fusion.
Controlling these magnetic fields is a major challenge. They must be strong enough to keep the hydrogen atoms moving until they fuse. This process requires a huge amount of startup energy, but once ignited it can be sustained. Solving this problem is critical to adapting nuclear fusion to space propulsion.
Reinforcement learning and energy generation
The usefulness of reinforcement learning goes beyond design. You can also manage fuel consumption, a critical factor for missions that need to adapt in real time. The modern space industry increasingly values spacecraft that can change roles as mission goals and priorities change.
For example, military planning must respond quickly to changing geopolitical conditions. Lockheed Martin’s LM400 satellite perfectly demonstrates this adaptability, combining missile warning and remote sensing capabilities.
But flexibility brings uncertainty. How much fuel will be needed for the mission and when will it be needed? Reinforcement learning provides the answer.
From bicycles to rockets, learning through human and artificial experiences will shape the future. space exploration. As scientists push the limits of propulsion and AI, this technology will play a leading role in exploring the solar system and beyond, opening the door to new discoveries.
