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Overview of stellarator theory at the University of Montana

This overview discusses numerical studies for quasi-poloidal symmetric (QPS) stellarators, ideal MHD stability studies, flexibility studies, and high-b compact stellarator studies. It also covers second ballooning stability in quasi-symmetric stellarators and the development of improved optimization algorithms.

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Overview of stellarator theory at the University of Montana

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  1. Overview of stellarator theory at the University of Montana A. S. Ware University of Montana 5/22/2003

  2. Overview • Numerical studies for QPS • Ideal MHD stability studies • Flexibility studies • High-b compact stellarator studies • Second ballooning stability in quasi-symmetric stellarators • Development of improved optimization algorithms • A breeder algorithm

  3. Numerical studies for QPS • Mercier Stability • Stability criteria checked in VMEC • Ballooning Stability • Using COBRAVMEC and TERPSICHORE to test infinite- and finite-n ballooning stability • Kink and Vertical Stability • Using TERPSICHORE to test stability for n=0 and n=1 modes in QPS

  4. Numerical studies for QPS:Impact of the current profile on ballooning stability • b at marginal stability as a function of the normalized flux

  5. Numerical studies for QPS:Impact of external coil currents on ballooning stability • Optimized only by changing external coil currents Modular Coil Currents <b> = 2.0% <b> = 2.2%

  6. High-b compact stellarator studies • High-b, tokamak-stellarator hybrids • high-shear, tokamak-like rotational transform profile • |B| approximately poloidally symmetric • high interchange stability b limits up to <b> = 23% • Second ballooning stability in QPS plasmas • Equilibria with large second ballooning stable regions have been obtained at <b> ~ 6%

  7. High-bcompact stellarator studies:Minimizing the gap between 1st & 2nd stability • Second stability in QPS plasmas with optimized profiles Ballooning growth rate b S

  8. Development of improved optimization algorithms: Breeder Algorithm • Develop a hybrid optimizer which couples the global parameter space coverage of the genetic algorithm with the directness of the Levenberg-Marquadt optimization • Outer loop would be the genetic algorithm • Inner loop would optimize each member of the population using a Levenberg-Marquadt step

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