利用遗传算法和网格提高TJ-II仿星器的传输水平

A. Gómez-Iglesias, M. A. Vega-Rodríguez, Francisco Castejón-Magaña, M. Cárdenas-Montes, Enrique Morales-Ramos
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引用次数: 8

摘要

核聚变能源是下一代能源。科学家们用来进行研究的设备需要比它们产生的能量更多的能量,因为在聚变装置中出现了许多问题。在磁约束装置中,其中一个问题是粒子在受限等离子体中的输运。一些建模工具可以用来提高传输级别,但是这些工具的计算成本和模拟不同配置的数量使得不可能执行所需的测试来获得良好的设计。但是有了网格计算,我们就有了运行所需数量的测试所需的计算资源,有了遗传算法,我们就可以在不探索所有解空间的情况下寻找好的结果。
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Using a Genetic Algorithm and the Grid to Improve Transport Levels in the TJ-II Stellarator
Fusion energy is the next generation of energy. The devices that scientists are using to carry out their researches need more energy than they produce, because many problems are presented in fusion devices. In magnetic confinement devices, one of these problems is the transport of particles in the confined plasma. Some modeling tools can be used to improve the transport levels, but the computational cost of these tools and the number of different configurations to simulate make impossible to perform the required test to obtain good designs. But with grid computing we have the computational resources needed for running the required number of tests and with genetic algorithms we can look for a good result without exploring all the solution space.
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