MATLAB Program for Calculating Electromagnetic Compatibility Shielding Effectiveness

MATLAB programs for calculating electromagnetic compatibility shielding effectiveness typically involve electromagnetic field simulation, shielding material characteristic analysis, and signal attenuation assessment. The core algorithm logic is based on electromagnetic wave propagation characteristics through shielding materials, combined with field strength attenuation formulas for numerical computation. Program implementation generally employs MATLAB's PDE toolbox for wave equation solving or custom finite-difference time-domain (FDTD) methods for electromagnetic wave propagation simulation.

The program typically starts by defining input parameters through structured data classes or parameter initialization functions, including electromagnetic wave frequency range, shielding material conductivity and permeability, and material thickness. Subsequently, the program calculates shielding effectiveness (SE), usually expressed in decibels (dB). Common computational approaches include analytical solutions based on transmission line theory (implemented using impedance matching algorithms) or numerical simulation methods like method of moments (MoM) with matrix solving functions.

For multilayer shielding structures, the program may involve iterative calculations using recursive algorithms to determine reflection and transmission losses for each material layer, ultimately synthesizing overall shielding effectiveness through cascaded network analysis. When considering near-field and far-field effects, different attenuation models can be integrated through conditional branching statements to correct calculation results, using field_type flags to switch between models.

Program optimization can incorporate parallel computing using MATLAB's Parallel Computing Toolbox to enhance computational efficiency in high-frequency ranges, or implement GUI interfaces with App Designer for parameter adjustment and real-time result visualization. Additionally, model calibration can be achieved through curve fitting functions (like fitlm) combined with experimental data to improve simulation accuracy, with optional data validation modules for error minimization.