Solving Hysteresis Curve MATLAB Simulation Challenges

Hysteresis curve simulation is crucial for electromagnetic material and device analysis. MATLAB provides powerful numerical computation and visualization tools to tackle this challenge. The core solution framework consists of three levels: model establishment, algorithm selection, and program implementation.

During the model establishment phase, classic hysteresis theoretical models like Jiles-Atherton are typically employed, using differential equations to describe the nonlinear relationship between magnetization intensity and magnetic field strength. Particular attention should be paid to the physical significance of model parameters, such as saturation magnetization and pinning coefficients, which directly influence the curve shape. In code implementation, these parameters should be defined as modifiable variables with proper documentation of their physical meanings.

For programming, an object-oriented approach is recommended to encapsulate the hysteresis model class, facilitating parameter management and multiple simulation comparisons. For numerical solving, the ode45 variable-step algorithm is suggested for handling differential equations, combined with event detection functionality to capture magnetization saturation points. When simulating periodic excitation, careful design of the magnetic field strength H input waveform is necessary, commonly using triangular waves or sinusoidal sweep signals. Implementation tip: Use MATLAB's function handle system to define custom waveform generators.

In the results analysis phase, focus on implementing automatic extraction algorithms for characteristic values like remanence and coercivity. For visualization, using subplot to simultaneously display B-H curves and μ-H curves is advised, with added crosshair cursor interactive functionality for measuring key point coordinates. For performance optimization, pre-allocate array memory to avoid dynamic expansion overhead in loops. Code example: Initialize arrays using zeros() function with estimated size before simulation loops.

Advanced techniques include: introducing temperature influence factors for multi-physics coupling simulation, combining FFT analysis for harmonic distortion characteristics, and using parameter scanning for batch analysis of material composition effects. These extensions enable the simulation system to address more complex engineering requirements. Implementation approach: Create parameter sweep scripts using for-loops or parfor for parallel computation when dealing with large parameter spaces.