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2024 ANS Annual Conference
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Fusion Science and Technology
Latest News
Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
M. Salewski, M. Nocente, A. S. Jacobsen, F. Binda, C. Cazzaniga, J. Eriksson, B. Geiger, G. Gorini, C. Hellesen, V. G. Kiptily, T. Koskela, S. B. Korsholm, T. Kurki-Suonio, F. Leipold, D. Moseev, S. K. Nielsen, J. Rasmussen, P. A. Schneider, S. E. Sharapov, M. Stejner, M. Tardocchi, JET Contributors, ASDEX Upgrade Team, EUROfusion MST1 Team
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 23-36
Technical Paper | doi.org/10.1080/15361055.2017.1380482
Articles are hosted by Taylor and Francis Online.
Bayesian integrated data analysis combines measurements from different diagnostics to jointly measure plasma parameters of interest such as temperatures, densities, and drift velocities. Integrated data analysis of fast-ion measurements has long been hampered by the complexity of the strongly non-Maxwellian fast-ion distribution functions. This has recently been overcome by velocity-space tomography. In this method two-dimensional images of the velocity distribution functions consisting of a few hundreds or thousands of pixels are reconstructed using the available fast-ion measurements. Here we present an overview and current status of this emerging technique at the ASDEX Upgrade tokamak and the JET toamak based on fast-ion D-alpha spectroscopy, collective Thomson scattering, gamma-ray and neutron emission spectrometry, and neutral particle analyzers. We discuss Tikhonov regularization within the Bayesian framework. The implementation for different types of diagnostics as well as the uncertainties are discussed, and we highlight the importance of integrated data analysis of all available detectors.