Plasma models for real-time control of advanced tokamak scenarios

Abstract

An integrated plasma profile control strategy, ARTAEMIS, is being developed for extrapolating present-day advanced tokamak (AT) scenarios to steady-state operation. The approach is based on semi-empirical modelling and was initially explored on JET (Moreau et al 2008 Nucl. Fusion 48 106001). This paper deals with the general applicability of this strategy for simultaneous magnetic and kinetic control on various tokamaks. The determination of the device-specific, control-oriented models that are needed to compute optimal controller matrices for a given operation scenario is discussed. The methodology is generic and can be applied to any device, with different sets of heating and current drive actuators, controlled variables and profiles. The system identification algorithms take advantage of the large ratio between the magnetic and thermal diffusion time scales and have been recently applied to both JT-60U and DIII-D data. On JT-60U, an existing series of high bootstrap current (∼70%), 0.9 MA non-inductive AT discharges was used. The actuators consisted of four groups of neutral beam injectors aimed at perpendicular injection (on-axis and off-axis), and co-current tangential injection (also on-axis and off-axis). On DIII-D, dedicated system identification experiments were carried out in the loop voltage (V ext) control mode (as opposed to current control) to avoid feedback in the response data from the primary circuit. The reference plasma state was that of a 0.9 MA AT scenario which had been optimized to combine non-inductive current fractions near unity with 3.5 < βN < 3.9, bootstrap current fractions larger than 65% and H 98(y,2) = 1.5. Actuators other than V ext were co-current, counter-current and balanced neutral beam injection, and electron cyclotron current drive. Power and loop voltage modulations resulted in dynamic variations of the plasma current between 0.7 and 1.2 MA. It is concluded that the response of essential plasma parameter profiles to specific actuators of a given device can be satisfactorily identified from a small set of experiments. This provides, for control purposes, a readily available alternative to first-principles plasma modelling.

Dynamic optimization of open-loop input signals for ramp-up current profiles in tokamak plasmas

“…The pioneering work in this area is due to Moreau, who in [15] derived empirical models for current profile evolutions using system identification techniques, and then used these models to synthesize a controller for safety factor profile manipulation. This approach, called the data-driven modeling method, does not rely on first principles to derive the model, but rather involves constructing the model by fitting observed data from experiments (e.g., JT-60 in Japan and DIII-D in the USA [16]), where the observed data is generated by input signals covering a sufficiently broad range of frequencies. As an alternative approach, it is also possible to use PDE models derived from first principles for the control and optimization of internal spatial profiles.…”

Sliding mode control of a tokamak transformer

“…Distributed parameter simulations [44] are the preferred option to simulate current profile evolution during plasma current transients. There are many such distributed parameter models available [45]- [48], some control oriented [49], [50] and some even available in real time [51]. However, for control systems design a lumped parameter formulation is generally preferable [52], [53].…”

“…The information from magnetic sensors is combined to obtain the Shafranov parameter Λ and the poloidal using the formulas described in [65]. From these the normalized internal inductance is obtained as (49) which is normalized to the machine major radius r 0. The internal inductance in SI units is The magnetic energy content inside the vacuum vessel walls is then trivially obtained as…”

Detection of Disruptions in the High-β Spherical Torus NSTX

“…Operations scenarios with reduced intrinsic disruptivity should be found, and control techniques must be developed to both maintain those optimal equilibrium characteristics and to prevent the growth of disruptive MHD. Examples of equilibrium control techniques include control of the strongly shaped plasma boundary [37,38], the global β N [39][40][41], the internal profiles [42][43][44][45], or error fields [46,47]. The vertical position instability [48][49][50][51][52] is controlled [50][51][52] as a matter of routine in all shaped tokamaks.…”