LETIA

Laboratoire d'Excellence

Chargement...
LETIA Laboratoire d'électrotechnique, télécommunications et informatique appliquée

Publications

Détails de la publication scientifique

Contribution to the comparison of conventional concentric magnetic gear and double stage concentric magnetic gear for high power offshore wind applications

Informations de base

Titre: Contribution to the comparison of conventional concentric magnetic gear and double stage concentric magnetic gear for high power offshore wind applications

Type: Article dans revue indexée

Journal/Conférence: International Journal of Applied Power Engineering (IJAPE)

Date de publication: 10/12/2025

Unité de recherche: URM2E - Unité de Recherche en Matériaux pour Énergie et Environnement

Statut: Accepté

Auteurs
  • D’Almeida Renaud Philippe (N/A) Ordre: 1
  • Agbokpanzo Richard Gilles (N/A) Ordre: 2
  • Agbomahena Bienvenu Macaire (N/A) Ordre: 3
Contenu scientifique

Résumé:

Nowadays, the replacement of mechanical technologies by magnetic technologies has several advantages. Therefore, in this paper, we compare in an indirect drive chain the conventional concentric magnetic gear (CCMG) and the double-stage concentric magnetic gear (DSCMG) used as a speed multiplier for a high-power offshore wind turbine. This comparison is performed for the same gear ratio and the same torque at the input of both magnetic gears to obtain the same torque values at the output of each gear. The goal is to determine which one has the smaller amount of magnet and the higher volumetric torque density. After the calculation of the gear ratio, a first choice of geometrical parameters is adopted. Several simulations carried out by the finite element method (FEM) allowed to obtain the desired torques and to fix the final geometrical parameters of each magnetic gear. The results obtained show that the DSCMG has both the smallest magnet volume and the highest volumetric torque density compared to the CCMG.

Mots-clés:

Mot-clé

Contenu détaillé:

Contenu

Références bibliographiques:

Ref
Métadonnées

Indexation: IPMU

Document PDF: Voir le PDF

Visibilité: Privée

Licence: cc-by

Source de financement: FED

Domaines de recherche:

IA Deep Learning