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Computing the shape gradient of stellarator coil complexity with respect to the plasma boundary

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 نشر من قبل Arthur Carlton-Jones
 تاريخ النشر 2020
  مجال البحث فيزياء
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Coil complexity is a critical consideration in stellarator design. The traditional two-step optimization approach, in which the plasma boundary is optimized for physics properties and the coils are subsequently optimized to be consistent with this boundary, can result in plasma shapes which cannot be produced with sufficiently simple coils. To address this challenge, we propose a method to incorporate considerations of coil complexity in the optimization of the plasma boundary. Coil complexity metrics are computed from the current potential solution obtained with the REGCOIL code (Landreman 2017 Nucl. Fusion 57 046003). We compute the local sensitivity of these metrics with respect to perturbations of the plasma boundary using the shape gradient (Landreman & Paul 2018 Nucl. Fusion 58 076023). We extend REGCOIL to compute derivatives of these metrics with respect to parameters describing the plasma boundary. In keeping with previous research on winding surface optimization (Paul et al. 2018 Nucl. Fusion 58 076015), the shape derivatives are computed with a discrete adjoint method. In contrast with the previous work, derivatives are computed with respect to the plasma rather than the winding surface parameters. To further reduce the required resolution, we present a more efficient representation of the plasma surface using a single Fourier series to describe the radial distance from a coordinate axis and a spectrally condensed poloidal angle. This representation is advantageous over the standard cylindrical representation used in the VMEC code (Hirshman & Whitson 1983 The Physics of Fluids 26 3553-3568), as it provides a uniquely defined poloidal angle, eliminating a null space in the optimization of the plasma surface. The resulting shape gradient highlights features of the plasma boundary consistent with simple coils and can be used to couple coil and fixed-boundary optimization.

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