Abstract:Quasisymmetric stellarators are an attractive class of optimised magnetic confinement configurations. The property of quasisymmetry (QS) is in practice limited to be approximate, and thus the construction requires measures that quantify the deviation from the exact property. In this paper we study three measure candidates used in the literature, placing the focus on their origin and a comparison of their forms. The analysis shows clearly the lack of universality in these measures. As these metrics do not direc… Show more
“…We draw attention to the broken orange lines in Figure 4, which include some estimates for the transitions. In particular, in addition to the expressions for (12), we have considered the other two sign combinations. For k ≥ 3, these are not too bad as guides for the configuration phase.…”
Section: Anharmonic Torus Unknotsmentioning
confidence: 99%
“…When a single harmonic dominates, the symmetric torus unknot is expected to be a good model, and we expect to find s n somewhere between unity and n. In practice, though, one finds multiple harmonics to be relevant, which leads to a growth in complexity (see Figure 4). Although the comparison of s n with unity is still a good first test, a more detailed comparison will be necessary using the analytic conditions (12) or their generalisation (D3). Generally, we shall not need to deal with all the complex, intermediate phases, as strong shaping tends to make them impractical.…”
Section: Practical Application and Qualitative Assessmentmentioning
confidence: 99%
“…In this appendix, we show the universality of the phase transitions that we found in Section IVC, Eqs. (12). The idea is to prove that these transitions prevail as one includes an arbitrary number of harmonics in the description of the magnetic axis, as well as allow for any choice of Z n with respect to R n .…”
Section: Appendix D: Invariance Of Phase Transitionsmentioning
confidence: 99%
“…Little is known (beyond experience gained by performing numerous optimization studies) of the global spatial structure of the optimization space and how different forms of cost functions shape it. 12 The present paper is the first of a sequence of papers a) Email: eduardor@princeton.edu b) Email: ws3883@princeton.edu c) Email: amitava@princeton.edu where we will attempt to shed some light on the structure of configuration space as it pertains to QS. To do so, we identify configurations approximately with sets of ordered functions and parameters, beginning with the magnetic axis and moving outward.…”
“…We draw attention to the broken orange lines in Figure 4, which include some estimates for the transitions. In particular, in addition to the expressions for (12), we have considered the other two sign combinations. For k ≥ 3, these are not too bad as guides for the configuration phase.…”
Section: Anharmonic Torus Unknotsmentioning
confidence: 99%
“…When a single harmonic dominates, the symmetric torus unknot is expected to be a good model, and we expect to find s n somewhere between unity and n. In practice, though, one finds multiple harmonics to be relevant, which leads to a growth in complexity (see Figure 4). Although the comparison of s n with unity is still a good first test, a more detailed comparison will be necessary using the analytic conditions (12) or their generalisation (D3). Generally, we shall not need to deal with all the complex, intermediate phases, as strong shaping tends to make them impractical.…”
Section: Practical Application and Qualitative Assessmentmentioning
confidence: 99%
“…In this appendix, we show the universality of the phase transitions that we found in Section IVC, Eqs. (12). The idea is to prove that these transitions prevail as one includes an arbitrary number of harmonics in the description of the magnetic axis, as well as allow for any choice of Z n with respect to R n .…”
Section: Appendix D: Invariance Of Phase Transitionsmentioning
confidence: 99%
“…Little is known (beyond experience gained by performing numerous optimization studies) of the global spatial structure of the optimization space and how different forms of cost functions shape it. 12 The present paper is the first of a sequence of papers a) Email: eduardor@princeton.edu b) Email: ws3883@princeton.edu c) Email: amitava@princeton.edu where we will attempt to shed some light on the structure of configuration space as it pertains to QS. To do so, we identify configurations approximately with sets of ordered functions and parameters, beginning with the magnetic axis and moving outward.…”
“…The equilibrium was then optimized for quasi-symmetry on the last closed flux surface (ρ = 1) in this two-dimensional parameter space using each of the three objective functions described previously, and with both first and second-order optimization methods. This simple optimization space was chosen for comparison to a previous study of this problem 24 , and it lends itself well to visualization. Note that in the example used here, quasi-symmetry is only being targeted on a single flux surface and the rotational transform profile is held fixed during optimization.…”
The DESC stellarator optimization code takes advantage of advanced numerical methods to search the full parameter space much faster than conventional tools. Only a single equilibrium solution is needed at each optimization step thanks to automatic differentiation, which efficiently provides exact derivative information. A Gauss-Newton trust-region optimization method uses second-order derivative information to take large steps in parameter space and converges rapidly. With just-in-time compilation and GPU portability, high-dimensional stellarator optimization runs take orders of magnitude less computation time with DESC compared to other approaches. This paper presents the theory of the DESC fixed-boundary local optimization algorithm along with demonstrations of how to easily implement it in the code. Example quasi-symmetry optimizations are shown and compared to results from conventional tools. Three different forms of quasi-symmetry objectives are available in DESC, and their relative advantages are discussed in detail. In the examples presented, the triple product formulation yields the best optimization results in terms of minimized computation time and particle transport. This paper concludes with an explanation of how the modular code suite can be extended to accommodate other types of optimization problems.
Adjoint methods can speed up stellarator optimisation by providing gradient information more efficiently compared with finite-difference evaluations. Adjoint methods are herein applied to vacuum magnetic fields, with objective functions targeting quasi-symmetry and a rotational transform value on a surface. To measure quasi-symmetry, a novel way of evaluating approximate flux coordinates on a single flux surface without the assumption of a neighbourhood of flux surfaces is proposed. The shape gradients obtained from the adjoint formalism are evaluated numerically and verified against finite-difference evaluations.
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