Fiche individuelle
Zhenxin LI | ||
Titre | Doctorant | |
Equipe | Outils et Méthodes Numériques | |
Téléphone | +33 (0)3-XX-XX-XX-XX | |
zhenxin.li.edu@univ-lille.fr | ||
Observation / Thématique de recherche | Multi-Physical Modelling of the Grain Oriented Electrical Steels for their Implementation in Electromagnetic Computation Codes | |
Publications |
ACLI Revue internationale avec comité de lecture |
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[1] ODF-based model considering compressive stress for modeling the magnetic properties of Grain-oriented electrical steels Journal of Magnetism and Magnetic Materials (JMMM), Vol. 604, 08/2024, URL, Abstract LI Zhenxin, TANG Zuqi, MESSAL Oualid, BENABOU Abdelkader, WANG Shuhong |
Grain-oriented electrical steels (GOES) are widely used in the manufacturing of high efficiency energy conversion systems thanks to their excellent magnetic properties along the rolling direction. Nevertheless, on the one hand, GOES exhibit strong magnetic anisotropy, resulting in distinct magnetic properties regarding the direction of the applied magnetic field to the rolling direction. On the other hand, GOES undergoes changes in their magnetic properties due to the industrial manufacturing process and mechanical constraints during operation. In general, stress, and particularly compressive stress, deteriorates magnetic properties. This paper deals with the modeling of the magnetic behavior of GOES under compressive stress. The orientation distribution function (ODF) based approach recently applied to describe the B-H first magnetization curves of a conventional GOES without stress as well as under applied uniaxial mechanical tensile stress is extended to account for the effect of compressive stress, with different considerations of modeling. The magnetic properties and the sensitivity of the ODF-based model to the magnetization direction as well as to the applied compressive stress are analyzed. Oscillation issues inherent to the ODF-based model are also discussed. |
[2] Consideration of tensile stress in the ODF-based approach for modelling the first magnetization curves of Grain-Oriented Electrical Steels Journal of Magnetism and Magnetic Materials (JMMM), Vol. 590, 01/2024, URL, Abstract LI Zhenxin, TANG Zuqi, MESSAL Oualid, BENABOU Abdelkader, WANG Shuhong |
Grain-oriented electrical steels (GOES) are extensively utilized in power transformers to enhance energy efficiency and reduce their size and weight. GOES are characterized by their strong magnetic anisotropy, leading to distinct magnetic characteristics (behavior law and iron losses) according to the direction and the intensity of the applied field with respect to the rolling direction (RD). The Orientation Distribution Function (ODF) based approach is a convenient method for describing the anisotropy of the GOES behavior law owing to its ease of identification and implementation. However, it exhibits a significant oscillation issue at low magnetic fields. To address this issue, a high-order ODF-based method has been proposed in the literature. In this article, the ODF-based model, initially stress-free, is extended to include the effects of mechanical tensile stress on the first magnetization curves of a conventional GOES. An ODF-based model considering both magnetic anisotropy and mechanical tensile stress is then proposed. The results are discussed and compared with experimental data. |
[3] Optimal selection of angular input data for the ODF based model applied to B-H magnetization curves of Grain-Oriented Electrical Steels IEEE Transactions on Magnetics, 11/2023, URL, Abstract LI Zhenxin, TANG Zuqi, MESSAL Oualid, BENABOU Abdelkader, WANG Shuhong |
Grain-Oriented Electrical Steels (GOES) are widely used in transformers to increase energy efficiency while reducing the volume and weight of such transformers. These GOES are characterized by their strong anisotropy, which leads to different magnetic properties (behavior law and iron losses) depending on the strength and the direction of the applied magnetic field to the rolling direction (RD). To describe the anisotropy of the GOES behavior law, the Orientation Distribution Function (ODF) based approach shows interesting features regarding ease of identification and implementation. However, a significant oscillation issue in this model at low magnetic field exits. It is recently reported that a high-order ODF based method can improve the accuracy of the original model. But the high-order ODF based model implies an important number of experimental data according to different directions of the applied field to RD (different magnetization angles) to build a model with sufficient accuracy. In addition, the accuracy also depends on how the input magnetization angles are selected between the RD and the transverse direction (TD). In this article, an optimal algorithm for input magnetization angle selection is proposed for the high-order ODF based method. To validate the proposed algorithm, 13 magnetization angles have been measured. A comparative study has been conducted based on the original and the high-order ODF based models built with the proposed selected magnetization angles. It is shown that the proposed algorithm can reduce the number of experimental data needed for the ODF based model while providing an acceptable global error. |
ACT Conférence internationale avec acte |
[1] Improved ODF approach to model magnetic properties of Grain Oriented Electrical Steels taking into account mechanical stress 26th Soft Magnetic Materials Conference, Prague, Czech Republic, 09/2023 LI Zhenxin, TANG Zuqi, MESSAL Oualid, BENABOU Abdelkader, WANG Shuhong |
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Dernières actualités
- Soutenance de Thèse, Wei CHEN, 29 Nov. 2024
- Séminaire, Pr. Hajime IGARASHI (Hokkaido University, Japan), 28 Nov. 2024
- Séminaire, Dr. Nathan WILLIAMS, Nov. 25, 2024
- Soutenance de Thèse, Ghazala SHAFIQUE, 21 Nov. 2024
- Soutenance de thèse, Yahya LAMRANI, 30 Octobre 2024
- Séminaire JCJC, 25 octobre 2024
- Soutenance de thèse, Othmane MARBOUH, 23 octobre 2024
- Visite du HCERES, 16 et 17 Octobre 2024
- Séminaire, Dr. Alessandro Formisano, Sept. 23, 2024
- Réunion d’information: Valorisation des résultats de recherche / SATT Nord, 18 Sept. 2024