Model-based optimal scenario planning in EAST

• Published in: Fusion engineering and design Vol. 123; no. C; pp. 569 - 573
• Main Authors: Wang, Hexiang, Schuster, Eugenio, Rafiq, Tariq, Kritz, Arnold, Ding, Siye
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2017; Elsevier Science Ltd; Elsevier
• Subjects: Beam injection; Computational fluid dynamics; Computer simulation; Constraint modelling; Control theory; Cyclotrons; Design optimization; Electron cyclotron heating; Evolution; Feedforward control; Fluid flow; Fluid mechanics; Magnetic flux; Magnetohydrodynamics; Model-based control; Neutral beams; Nonlinear control; Nonlinear differential equations; Nuclear power plants; Nuclear Science & Technology; Partial differential equations; Plasma currents; Plasma density; Plasma dynamics; Plasma etching; Scenario planning; Stability; Studies; Tokamak devices; Tokamak plasma control; Trajectory analysis; Trajectory optimization; Waveforms; Tokamak plasma control; Scenario planning; Model-based control
• ISSN: 0920-3796; 1873-7196; 1873-7196
• DOI: 10.1016/j.fusengdes.2017.03.143

•Scenario planning in EAST is formulated as a constrained nonlinear optimization problem.•The magnetic diffusion equation is combined with physics-based correlations to obtain a control-oriented response model.•The optimization objective is to design feedforward actuator trajectories to reach a desired plasma state.•The desired plasma state is defined in terms of the q profile, βN, and a stationary condition.•The model-based optimization problem under input and state constraints is solved by employing sequential quadratic programming. Ongoing work in the fusion community focuses on developing advanced plasma scenarios characterized by high plasma confinement, magnetohydrodynamic (MHD) stability, and noninductively driven plasma current. The toroidal current density profile, or alternatively the q profile, together with the normalized beta, are often used to characterize these advanced scenarios. The development of these advanced scenarios is experimentally carried out by specifying the devices’ actuator trajectory waveforms, such as the total plasma current, the plasma density, and the auxiliary heating and current-drive (H&CD) sources based on trial-and-error basis. In this work, a model-based numerical optimization approach is followed to complement the experimental effort on actuator trajectory planning in the EAST tokamak. The evolution of the q profile is closely related to the evolution of the poloidal magnetic flux profile, whose dynamics is modeled by a nonlinear partial differential equation (PDE) referred to as the magnetic-flux diffusion equation (MDE). In this work, the MDE is combined with physics-based correlations obtained from EAST experimental data for the plasma density, temperature, resistivity and non-inductive current drives to develop a control-oriented nonlinear PDE model. The optimization objective is to design feedforward trajectories for the plasma current, plasma density, electron cyclotron heating power, neutral beam injection power and lower hybrid current drive power that steer the plasma to desired q profile and βN such that the achieved state is stationary in time. The optimization is subject to the plasma dynamics (described by the physics-based PDE model) and plasma state and actuator constraints, such as the maximum available amount of H&CD power and MHD stability limits. This defines a nonlinear, constrained optimization problem that is solved by employing sequential quadratic programming. The optimized actuator trajectories are assessed in nonlinear transport simulations in preparation for experimental tests in EAST.