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Adjoint approach to calculating shape gradients for three-dimensional magnetic confinement equilibria

Published online by Cambridge University Press:  28 March 2019

Thomas Antonsen Jr.*
Affiliation:
Institute for Research in Electronics and the Applied Physics, University of Maryland, College Park, MD 20742, USA
Elizabeth J. Paul
Affiliation:
Institute for Research in Electronics and the Applied Physics, University of Maryland, College Park, MD 20742, USA
Matt Landreman
Affiliation:
Institute for Research in Electronics and the Applied Physics, University of Maryland, College Park, MD 20742, USA
*
Email address for correspondence: antonsen@umd.edu
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Abstract

The shape gradient quantifies the change in some figure of merit resulting from differential perturbations to a shape. Shape gradients can be applied to gradient-based optimization, sensitivity analysis and tolerance calculation. An efficient method for computing the shape gradient for toroidal three-dimensional magnetohydrodynamic (MHD) equilibria is presented. The method is based on the self-adjoint property of the equations for driven perturbations of MHD equilibria and is similar to the Onsager symmetry of transport coefficients. Two versions of the shape gradient are considered. One describes the change in a figure of merit due to an arbitrary displacement of the outer flux surface; the other describes the change in the figure of merit due to the displacement of a coil. The method is implemented for several example figures of merit and compared with direct calculation of the shape gradient. In these examples the adjoint method reduces the number of equilibrium computations by factors of $O(N)$, where $N$ is the number of parameters used to describe the outer flux surface or coil shapes.

Information

Type
Research Article
Copyright
© Cambridge University Press 2019 
Figure 0

Figure 1. (a) The shape gradient for $f_{\unicode[STIX]{x1D6FD}}$ (3.1) computed using the adjoint solution (3.9) (left) and using parameter derivatives (right). (b) The shape gradient computed with the adjoint solution in the $\unicode[STIX]{x1D701}-\unicode[STIX]{x1D703}$ plane. (c) The fractional difference (3.10) between the shape gradient obtained with the adjoint solution and with parameter derivatives. The two methods give virtually indistinguishable results, as they should. (d) The fractional difference between the shape gradient obtained with the adjoint solution and with parameter derivatives, $S_{\text{residual}}$, depends on the scale of the perturbation added to the adjoint force balance equation, $\unicode[STIX]{x1D6E5}$.

Figure 1

Figure 2. (a) The shape gradient for $f_{\unicode[STIX]{x1D704}}$ (3.11) computed using the adjoint solution (3.15) (left) and using parameter derivatives (right). (b) The shape gradient computed with the adjoint solution in the $\unicode[STIX]{x1D701}-\unicode[STIX]{x1D703}$ plane. (c) The fractional difference (3.10) between the shape gradient obtained with the adjoint solution and with parameter derivatives. Again, the results are essentially indistinguishable, as expected.

Figure 2

Figure 3. The coil shape gradient for $f_{\unicode[STIX]{x1D704}}$ (3.11) computed using the adjoint solution (3.18) for each of the 3 unique coil shapes (black). The arrows indicate the direction of $\boldsymbol{S}_{k}$, and their length indicates the local magnitude relative to the reference arrow shown. The arrows are not visible on this scale on the outboard side.

Figure 3

Figure 4. (a) The Cartesian components of the coil shape gradient for each of the 3 unique modular NCSX coils computed with the adjoint and direct approaches. (b) The fractional difference (3.19) between the shape gradient computed with the adjoint approach and the direct approach is plotted for each Cartesian component and each of the 3 unique coils.