Fast physics-based launcher optimization for electron cyclotron current drive

TL;DR

The paper tackles the challenge of fast ECCD launcher optimization for fusion pilot plants by replacing exhaustive coarse scans of the launcher parameter space with a physics-based method that uses the HARE reduced model to determine optimal wave parameters for deposition at a given flux surface. These parameters are then embedded in a commercial ray-tracing code (GENRAY) to extract launcher geometry, with the optimization implemented in a 1-D deposition framework along the flux coordinate ρ and exploiting time-reversed exit rays to ensure localization. Across two reactor-relevant equilibria, the approach achieves ECCD efficiency around $\zeta \approx 0.3$ comparable to traditional methods, but with a dramatic reduction in simulations (hundreds vs. tens of thousands to millions). The method also yields more localized ECCD profiles and provides a practical, fast toolkit for zeroth-order launcher design, ramp-up planning, and potential neoclassical tearing mode control for spherical tokamak-based fusion pilot plants.

Abstract

With the increased urgency to design fusion pilot plants, fast optimization of electron cyclotron current drive (ECCD) launchers is paramount. Traditionally, this is done by coarsely sampling the 4-D parameter space of possible launch conditions consisting of (1) the launch location (constrained to lie along the reactor vessel), (2) the launch frequency, (3) the toroidal launch angle, and (4) the poloidal launch angle. For each initial condition, a ray-tracing simulation is performed to evaluate the ECCD efficiency. Unfortunately, this approach often requires a large number of simulations (sometimes millions in extreme cases) to build up a dataset that adequately covers the plasma volume, which must then be repeated every time the design point changes. Here we adopt a different approach. Rather than launching rays from the plasma periphery and hoping for the best, we instead directly reconstruct the optimal ray for driving current at a given flux surface using a reduced physics model coupled with a commercial ray-tracing code. Repeating this throughout the plasma volume requires only hundreds of simulations, constituting a significant speedup. The new method is validated on two separate example tokamak profiles, and is shown to reliably drive localized current at the specified flux surface with the same optimal efficiency as obtained from the traditional approach.

Figures (7)

• Figure 1: (a) Electron density $n_e$ and electron temperature $T_e$ profiles as functions of the normalized minor radius $\rho$, with $\rho$ defined in Eq. \ref{['eq:rhoDEF']}. (b) Characteristic cutoff and resonance frequencies [see Eqs. \ref{['eq:freqBEG']} -- \ref{['eq:freqEND']}] as functions of major radius along the plasma midplane.
• Figure 2: (a) Emitted rays launched from the desired deposition location with the HARE-predicted optimal parameters (Table \ref{['tab:HAREfull_A2']}). The blue dashed lines and the red solid lines represent rays emitted towards the top and bottom of the plasma periphery, respectively. (b) Re-launched rays giving near-optimal ECCD using the exit trajectories determined from the emitted rays, as listed in Table \ref{['tab:ECCDparams_HAREfull_A2']}. (c) Nearly-optimal ECCD ray trajectories obtained using the traditional approach based on coarse parameter scan of initial launcher conditions, as listed in Table \ref{['tab:ECCDparams_traditional_A2']}.
• Figure 3: Toroidal projection of the launched EC ray trajectory. Notably, both the toroidal magnetic field $B_T$ and the plasma current $I_p$ are counter-clockwise when viewed from above, so $N_\parallel < 0$. This feature is common for all cases considered here.
• Figure 4: (a) Comparison of the obtained ECCD profiles using the new physics-based optimization scheme and the traditional optimization scheme based on coarse parameter scan. (b) Comparison of the ECCD efficiency (total driven current per total injected power) for the two optimization methods. Also shown is the corresponding curve [Eq. \ref{['eq:ECCDtotal']}] for when the non-dimensional ECCD efficiency $\zeta = 0.3$, with $\zeta$ defined in Eq. \ref{['eq:ECCDeffic']}.
• Figure 5: Same as Fig. \ref{['fig:profs_freqs_A2']}, but for the second example profiles.