Transformation front kinetics in deformable ferromagnets

Michael Poluektov · arXiv · 2026

A continuum magneto-mechanical framework is developed to derive thermodynamic driving forces and efficiently model transformation-front kinetics in deformable ferromagnets and magnetic shape-memory alloys.

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Plain English summary

Magnetic shape-memory alloys couple magnetization with mechanical deformation and can undergo structural phase transitions. In these materials, moving phase boundaries separate different phases, and their motion depends on both magnetic fields and mechanical stresses. The paper aims to model how these phase boundaries propagate at the continuum scale. It identifies three needed components: coupled magnetic-mechanical bulk equations, a rule for phase-boundary velocity based on governing factors, and a computational method to track moving interfaces. It focuses on deriving the thermodynamic driving force for transformation fronts in a general magneto-mechanical setting. It also adapts the cut-finite-element method to handle propagating interfaces efficiently without changing the finite-element mesh, and applies the approach to qualitative modeling of magneto-mechanics in magnetic shape-memory alloys.

Why this matters

The paper claims novelty in focusing on the derivation of the thermodynamic driving force for transformation fronts in a general magneto-mechanical setting and in adapting the cut-finite-element method for magneto-mechanics transformation fronts. The abstract describes derivation and qualitative modeling with a computational method; it does not provide evidence of prototypes, field testing, or commercialization.

Key findings

  • Derivation of a thermodynamic driving force for transformation fronts in a general magneto-mechanical setting.
  • Adaptation of the cut-finite-element method for transformation fronts in magneto-mechanics to efficiently handle propagating interfaces without remeshing.
  • Qualitative modeling application to magneto-mechanics of magnetic shape-memory alloys.

Limitations

The abstract notes prior work exists but under limitations; it does not specify experimental validation, quantitative predictive performance, or how broadly the method is validated beyond qualitative modeling.

Publication

Publisher
arXiv
Publication date
February 3, 2026
Research type
Preprint
arXiv
2602.03745
Access
open

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Method note: Summaries and ratings on this page are generated by AI from the abstract only. Read the original paper for full context. · Model: gpt-5.4-nano-2026-03-17