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A year in orbit: ISS deployment tests radiation detectors for future space missions
The predawn darkness on a cool Florida night was shattered by the ignition of nine Merlin engines on a SpaceX Falcon 9 rocket. The thrust of the engines shook the ground miles away. From a distance, the rocket appeared to slowly rise above the horizon. For the cargo onboard, the launch was anything but gentle, as the ignition of liquid oxygen generated more than 1.5 million pounds of force. After the rocket had been out of sight for several minutes, the booster dramatically returned to Earth with several sonic booms in a captivating show of engineering designed to make space travel less expensive and more sustainable.
B. A. Grierson, X. Yuan, M. Gorelenkova, S. Kaye, N. C. Logan, O. Meneghini, S. R. Haskey, J. Buchanan, M. Fitzgerald, S. P. Smith, L. Cui, R. V. Budny, F. M. Poli
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 101-115
Technical Paper | doi.org/10.1080/15361055.2017.1398585
Articles are hosted by Taylor and Francis Online.
TRANSP simulations are being used in the OMFIT workflow manager to enable a machine-independent means of experimental analysis, postdictive validation, and predictive time-dependent simulations on the DIII-D, NSTX, JET, and C-MOD tokamaks. The procedures for preparing input data from plasma profile diagnostics and equilibrium reconstruction, as well as processing of the time-dependent heating and current drive sources and assumptions about the neutral recycling, vary across machines, but are streamlined by using a common workflow manager. Settings for TRANSP simulation fidelity are incorporated into the OMFIT framework, contrasting between-shot analysis, power balance, and fast-particle simulations. A previously established series of data consistency metrics are computed such as comparison of experimental versus calculated neutron rate, equilibrium stored energy versus total stored energy from profile and fast-ion pressure, and experimental versus computed surface loop voltage. Discrepancies between data consistency metrics can indicate errors in input quantities such as electron density profile or , or indicate anomalous fast-particle transport. Measures to assess the sensitivity of the verification metrics to input quantities are provided by OMFIT, including scans of the input profiles and standardized postprocessing visualizations. For predictive simulations, TRANSP uses GLF23 or TGLF to predict core plasma profiles, with user-defined boundary conditions in the outer region of the plasma. International Tokamak Physics Activity (ITPA) validation metrics are provided in postprocessing to assess the transport model validity. By using OMFIT to orchestrate the steps for experimental data preparation, selection of operating mode, submission, postprocessing, and visualization, we have streamlined and standardized the usage of TRANSP.