2012
DOI: 10.4236/jmp.2012.312234
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Simulation and Control of Turbulence at Tokamaks with Artificial Intelligence Methods

Abstract: The control of turbulence at tokamaks is very complex problem.The idea is to apply the fuzzy Markovian processes and fuzzy Brownian motions as good approximation of general robust drift kinetic equation. It is obtained by using the artificial neural networks for solving of appropriate advanced control problem. The proof of the appropriate theorem is shown.

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Cited by 6 publications
(10 citation statements)
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“…In the cross section calculations for the investigated (n,xn) reaction, the code ALICE-2011 with standard parameters has been used. In calculations, we used a calculation of neutrons on 9 Be at incident energy of 14.2 MeV in 2 MeV increments, with a histogram mesh-size of 0.2 MeV and with the number of 10,000 cascade events at each incident energy for the Monte Carlo Calculatıon. Finally, we used the data in ENGANG output file.…”
Section: Resultsmentioning
confidence: 99%
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“…In the cross section calculations for the investigated (n,xn) reaction, the code ALICE-2011 with standard parameters has been used. In calculations, we used a calculation of neutrons on 9 Be at incident energy of 14.2 MeV in 2 MeV increments, with a histogram mesh-size of 0.2 MeV and with the number of 10,000 cascade events at each incident energy for the Monte Carlo Calculatıon. Finally, we used the data in ENGANG output file.…”
Section: Resultsmentioning
confidence: 99%
“…Fusion researchers have sought to understand a variety of abrupt transitions in plasma operating conditions observed in tokamak experiments, and to anticipate others that might be present in fusion reactors [9]. Achieving acceptable levels of the performance of a fusion reactor in the areas of safety, economics and environmental acceptability, is critically dependent on performance of divertor and blanket systems [10].…”
Section: Introductionmentioning
confidence: 99%
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