An anonymous reader shared this report from The Conversation:
To make interplanetary travel faster, safer, and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is one type of technology that has begun to provide some of these necessary breakthroughs. We're a team of engineers and graduate students who are studying how AI in general, and a subset of AI called machine learning in particular, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind's journey to the stars...
Early nuclear thermal propulsion designs from the 1960s, such as those in NASA's NERVA program, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations — from beds of ceramic pebbles to grooved rings with intricate channels... [T]he more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates. This area is where reinforcement learning has proved to be essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem, involving countless variables — from the material properties to the amount of hydrogen that flows across the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer.
To make interplanetary travel faster, safer, and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is one type of technology that has begun to provide some of these necessary breakthroughs. We're a team of engineers and graduate students who are studying how AI in general, and a subset of AI called machine learning in particular, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind's journey to the stars...
Early nuclear thermal propulsion designs from the 1960s, such as those in NASA's NERVA program, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations — from beds of ceramic pebbles to grooved rings with intricate channels... [T]he more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates. This area is where reinforcement learning has proved to be essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem, involving countless variables — from the material properties to the amount of hydrogen that flows across the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer.
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