Spinning Plasma Solves a Long-Standing Fusion Reactor Mystery

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 Spinning Plasma Solves a Long-Standing Fusion Reactor Mystery


For years, researchers have struggled to explain why plasma particles in tokamaks consistently strike the inner divertor more heavily than the outer one, a subtle but crucial imbalance for fusion reactor design. New simulations reveal that the answer lies not only in sideways particle drifts near the exhaust but also in the powerful rotation of the plasma core itself. 
A persistent asymmetry in fusion exhaust has challenged researchers for years. New simulations show that plasma core rotation, working together with cross-field drifts, determines where particles land inside a tokamak.

Tokamaks are often described as giant magnetic “doughnuts,” built to keep an ultra-hot soup of charged particles suspended long enough for atomic nuclei to fuse and release energy. But even in the best magnetic cages, some of that plasma leaks out. When it does, the particles race along magnetic field lines into a specially engineered exhaust region called the divertor, which functions a bit like a high-tech tailpipe for a fusion reactor.
Down in the divertor, those particles slam into metal plates, lose heat, and can rebound back as neutral atoms. (The returning atoms help fuel the fusion reaction.) In principle, designers would like this exhaust to be predictable and spread out. In reality, experiments across tokamaks have kept showing the same troublesome skew: far more particles end up striking the inner divertor target than the outer one.

Where the exhaust lands determines where the heat concentrates, how quickly materials wear out, and how aggressively future reactors will need to cool and protect their divertor surfaces. For years, the most common explanation pointed to cross-field drifts inside the divertor, meaning particles sliding sideways across magnetic field lines instead of neatly following them.The idea made sense, but there was a major problem: computer simulations that included only these drifts still could not reproduce the strong inner target “preference” seen in real machines. If the models cannot get that basic pattern right, it becomes much harder to trust them for predicting how next generation devices will behave under even harsher conditions.

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