What Makes a Steady Flow to Favour Kinematic Magnetic Field Generation: A Statistical Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

de Almeida, FS (Author) Other; Chertovskih, R (Author) FEUP; Sílvio Gama (Author) FCUP; Gonçalves, R (Author) Other

Conference proceedings International

Title: Lecture Notes in Electrical Engineering

Pages: 662-673

16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

Porto, 17 July 2024 through 19 July 2024

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Other information

Authenticus ID: P-018-Q17

DOI: 10.1007/978-3-031-81724-3_59

Abstract (EN): To advance our understanding of the magnetohydrodynamic (MHD) processes in liquid metals, in this paper we propose an approach combining the classical methods in the dynamo theory based on numerical simulations of the partial differential equations governing the evolution of the magnetic field with the statistical methods. In this study, we intend to answer the following ¿optimization¿ question: Can we find a statistical explanation what makes a flow to favour magnetic field generation in the linear regime (i.e. the kinematic dynamo is considered), where the Lorenz force is neglected? The flow is assumed to be steady and incompressible, and the magnetic field generation is governed by the magnetic induction equation. The behaviour of its solution is determined by the dominant (i.e. with the largest real part) eigenvalue of the magnetic induction operator. Considering an ensemble of 2193 randomly generated flows, we solved the kinematic dynamo problem and performed an attempt to find a correlation between the dominant eigenvalue and the standard quantities used in hydrodynamics ¿ vorticity and kinetic helicity. We have found that there is no visible relation between the property of the flow to be a kinematic dynamo and these quantities. This enables us to conclude that the problem requires a more elaborated approach to ¿recognize¿ if the flow is a dynamo or not; we plan to solve it using contemporary data-driven approach based on deep neural networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11