Current Limit Avoidance Algorithms for DEMO Operation

Luigi Emanuel Grazia (), Domenico Frattolillo (), Gianmaria Tommasi () and Massimiliano Mattei ()
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Luigi Emanuel Grazia: Università degli Studi della Campania L. Vanvitelli
Domenico Frattolillo: l’Automazione e le Tecnologie dell’Elettromagnetismo
Gianmaria Tommasi: l’Automazione e le Tecnologie dell’Elettromagnetismo
Massimiliano Mattei: l’Automazione e le Tecnologie dell’Elettromagnetismo

Journal of Optimization Theory and Applications, 2023, vol. 198, issue 3, No 4, 958-987

Abstract: Abstract Tokamaks are the most promising devices to prove the feasibility of energy production using nuclear fusion on Earth which is foreseen as a possible source of energy for the next centuries. In large tokamaks with superconducting poloidal field (PF) coils, the problem of avoiding saturation of the currents is of paramount importance, especially for a reactor such as the European demonstration fusion power plant DEMO. Indeed, reaching the current limits during plasma operation may cause a loss of control of the plasma shape and/or current, leading to a major disruption. Therefore, a current limit avoidance (CLA) system is essential to assure safe operation. Three different algorithms to be implemented within a CLA system are proposed in this paper: two are based on online solutions of constrained optimization problems, while the third one relies on dynamic allocation. The performance assessment for all the proposed solutions is carried out by considering challenging operation scenarios for the DEMO reactor, such as the case where more than one PF current simultaneously saturates during the discharge. An evaluation of the computational burden needed to solve the allocation problem for the various proposed alternatives is also presented, which shows the compliance of the optimization-based approaches with the envisaged deadlines for real-time implementation of the DEMO plasma magnetic control system.

Keywords: Current limit avoidance; Input allocation; Plasma magnetic control; Tokamaks; 90C05; 90C20; 34H05; 49K15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10957-023-02277-2

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