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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
Latest News
Princeton-led team develops AI for fusion plasma monitoring
A new AI software tool for monitoring and controlling the plasma inside nuclear fuel systems has been developed by an international collaboration of scientists from Princeton University, Princeton Plasma Physics Laboratory (PPPL), Chung-Ang University, Columbia University, and Seoul National University. The software, which the researchers call Diag2Diag, is described in the paper, “Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas,” published in Nature Communications.
T. W. Petrie, G. H. Miley
Nuclear Science and Engineering | Volume 64 | Number 1 | September 1977 | Pages 151-162
Technical Paper | doi.org/10.13182/NSE77-A27086
Articles are hosted by Taylor and Francis Online.
Phase-space grouping techniques have been applied to two distinct problems in fusion product physics: (a) slowing down drift motion of highly energetic alpha particles in a symmetric toroidal field, and (b) first wall loading by 3.52-MeV alpha particles resulting from magnetic ripple. In the former, a weighted energy-loss approximation method permits the evolving orbits to be determined for any representative phase-space group. This enables rapid computation of several important suprathermal effects in a tokamak plasma. For example, code SYMALF, which embodies this idea, is applied to plasma heating and alpha-particle thermalization source problems. In the ripple field case, a probabilistic density function is employed to determine drift losses associated with ripple-trapped, 3.52-MeV alpha particles. When used to determine 3.52-MeV alpha-particle wall loadings, code RIPALF, which is based on this probability function, predicts the position of local “hot spots” along the first wall.