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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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.
R. L. Macklin
Nuclear Science and Engineering | Volume 59 | Number 3 | March 1976 | Pages 231-236
Technical Paper | doi.org/10.13182/NSE76-A26821
Articles are hosted by Taylor and Francis Online.
The 165Ho(n, γ) cross section was measured at the Oak Ridge Electron Linear Accelerator neutron time-of-flight facility. Nonhydrogenous scintillation detectors were used with pulse-height weighting to measure the prompt photon yield, normalized to the saturated 3.92-eV resonance in (165Ho + n) and the shape of the 6Li(n, α) cross section. Resonance parameters for many of the observed peaks below 3 keV were determined by a nonlinear least-squares fit. The data to 100 keV were well fitted with energy-independent strength functions 104 S0 = 1.33 ± 0.14, 104 S1 = 1.36 ± 0.24, 104S2 = 1.19 ± 0.76 and γ/D0 = 0.076/(3.23 ± 0.55 eV). The fluctuations of the cross section about the strength function fit are analyzed for 250-eV averages. The Wald-Wolfowitz “Runs” test is consistent with no additional nonrandom structure in the cross section.