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Research Needs for Magnetic Fusion Energy Sciences - US Burning ...

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Demonstrate active control of the plasma equilibrium state maintained in steady state<br />

close to an optimum per<strong>for</strong>mance configuration: How near optimal can the plasma profiles and<br />

bulk parameters be robustly and efficiently maintained in sustained steady state?<br />

The basic control challenge is to maintain the plasma parameters and profiles in a steady state optimized<br />

<strong>for</strong> a given desired confinement and stability, with sufficiently high efficiency to enable<br />

economic power production.<br />

Specific Challenges:<br />

a tokamak reactor based on highly optimized at scenarios will ultimately require control of the<br />

equilibrium pressure, current, and rotation profiles in a high-fluence nuclear environment maintained<br />

in steady state. The control system sensors, algorithms, and actuators must be capable of<br />

responding on multiple time scales, from magnetohydrodynamic (mhd) (msec and msec) scales,<br />

to current diffusion and transport (seconds) scales. The components must also survive <strong>for</strong> long<br />

time scales during which wall interaction, and atomic and nuclear chemistry effects, can modify<br />

the plasma characteristics. These will require real-time equilibrium reconstructions and sufficient<br />

diagnostics and actuators capable of measuring and modifying key equilibrium profiles: the plasma<br />

species density n k and temperature t k <strong>for</strong> each species k, and pressure p = Σn k t k , current density<br />

j, and rotation v profiles. Rotation is of interest since it is known to affect stability and transport,<br />

and could potentially provide a powerful knob <strong>for</strong> affecting density and temperature profiles.<br />

Presently, the required actuators <strong>for</strong> controlling the full complement of these, either directly<br />

or indirectly, are either nonexistent or not sufficiently capable <strong>for</strong> demo-level plasmas. density<br />

control will rely on fueling techniques, particle transport and the possible effect on particle transport<br />

of the current drive techniques and the alpha heating, which all require further development<br />

and increased understanding. The current must be sustained noninductively (i.e., without using<br />

the trans<strong>for</strong>mer) and its profile will largely be determined by the bootstrap current. The challenge<br />

<strong>for</strong> the actuators (whether radiofrequency systems or neutral beams) will be to deliver the supplementary<br />

current to the appropriate location with high efficiency. While the physics of current<br />

drive is well understood, the ability to place it where required is much more poorly demonstrated.<br />

control of density and rotation profiles is the least well developed; radiofrequency techniques <strong>for</strong><br />

rotation control are at a rudimentary level of understanding, as is the knowledge of the intrinsic<br />

rotation, especially in the presence of alpha heating. tailoring the rotation profile appears feasible<br />

using a combination of techniques, but has not been demonstrated in a predictable way. control of<br />

edge gradients in all quantities is expected to be essential, requiring diagnostics <strong>for</strong> the first-wall<br />

conditions, and in situ wall conditioning will be needed to control external conditions. control<br />

system algorithms will need to be capable of optimizing and providing closed loop signals to radiofrequency<br />

systems, fueling systems, external coils, and other actuators from necessarily limited<br />

diagnostic data supplemented by timely simulations and control-level models.<br />

additional serious challenges arise in a burning plasma, where the pressure is largely determined<br />

by alpha particle heating and direct control of the profile may be impossible. indirect control will<br />

depend on developing sufficient understanding of the coupled state to develop model-based controllers<br />

and algorithms to achieve the needed level of control.<br />

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