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Optimal Design of an Inductive MHD Electric Generator

Author

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  • Sara Carcangiu

    (Electrical and Electronic Engineering Department, University of Cagliari, Via Marengo, 2, 09123 Cagliari, Italy)

  • Alessandra Fanni

    (Electrical and Electronic Engineering Department, University of Cagliari, Via Marengo, 2, 09123 Cagliari, Italy)

  • Augusto Montisci

    (Electrical and Electronic Engineering Department, University of Cagliari, Via Marengo, 2, 09123 Cagliari, Italy)

Abstract

In this paper, the problem of optimizing the design of an inductive Magneto-Hydro-Dynamic (MHD) electric generator is formalized as a multi-objective optimization problem where the conflicting objectives consist of maximizing the output power while minimizing the hydraulic losses and the mass of the apparatus. In the proposal, the working fluid is ionized with periodical pulsed discharges and the resulting neutral plasma is unbalanced by means of an intense DC electrical field. The gas is thus split into two charged streams, which induce an electromotive force into a magnetically coupled coil. The resulting generator layout does not require the use of superconducting coils and allows you to manage the issues related to the conductivity of the gas and the corrosion of the electrodes, which are typical limits of the MHD generators. A tailored multi-objective optimization algorithm, based on the Tabu Search meta-heuristics, has been implemented, which returns a set of Pareto optimal solutions from which it is possible to choose the optimal solution according to further applicative or performance constraints.

Suggested Citation

  • Sara Carcangiu & Alessandra Fanni & Augusto Montisci, 2022. "Optimal Design of an Inductive MHD Electric Generator," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16457-:d:997897
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    References listed on IDEAS

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    1. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
    2. Roberto Battiti & Giampietro Tecchiolli, 1994. "The Reactive Tabu Search," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 126-140, May.
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