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| 001 | 904087 | ||
| 005 | 20240708133438.0 | ||
| 024 | 7 | _ | |a 10.1016/j.fusengdes.2021.112680 |2 doi |
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| 100 | 1 | _ | |a Wei, Yanling |0 P:(DE-Juel1)168343 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Analysis of HL-2A charge exchange spectra using parallel genetic algorithm |
| 260 | _ | _ | |a New York, NY [u.a.] |c 2021 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a kein Zugriff auf Postprint |
| 520 | _ | _ | |a In this work, we present a new method based on parallel genetic algorithm (GA) for in-between shot data analysis of the Charge-Exchange (CX) spectra on the HL-2A tokamak. The neutral beam induced active CX spectra is a powerful ion diagnostic technique to provide spatially resolved ion temperature and rotation velocity measurements on fusion devices. Currently CX spectra obtained in HL-2A experiments are mainly analyzed by the CXSFIT code [A. D. Whiteford, et.al, 2007]. While the analysis itself is fast, its accuracy relies on proper setup of the initial values for the spectral fitting parameters. Time-consuming manual interventions are needed. In the new parallel GA code, a two-loop GA analysis is used to gradually update the fitting parameter search ranges, which enables automatic analysis. A parallel algorithm based on the Linux Message Passing Interface (MPI) cluster is adapted to speed up the process. In a test run, for a set of 1600 data slices, the total time elapsed with 8 CPU nodes is about 310 s (0.2 s per data slice), which is efficient for in-between shot analysis on HL-2A. The uncertainty calculations using virtual CX signals with a noise level up to 5% show that the accuracies for ion temperature and rotation velocity are better than 10.14% and 2.14%, respectively. The ion temperature and rotation velocity obtained by applying the new parallel genetic algorithm on experimental CX data show good agreement with the conventional CXSFIT results. |
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| 700 | 1 | _ | |a Liu, Liang |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Yu, Deliang |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a von Hellermann, Manfred |0 P:(DE-Juel1)167437 |b 3 |
| 700 | 1 | _ | |a Chen, Wenjin |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Wang, Jie |0 P:(DE-HGF)0 |b 5 |
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| 700 | 1 | _ | |a He, Xiaoxue |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a He, Xiaofei |0 P:(DE-HGF)0 |b 8 |
| 773 | _ | _ | |a 10.1016/j.fusengdes.2021.112680 |g Vol. 168, p. 112680 - |0 PERI:(DE-600)1492280-0 |p 112680 - |t Fusion engineering and design |v 168 |y 2021 |x 0920-3796 |
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