✅ Author Profile: A research enthusiast and MATLAB simulation developer, skilled in data processing, modeling simulation, program design, complete code acquisition, paper reproduction, and scientific simulation.
🍎 Previous Reviews: Follow my personal homepage:MATLAB Research Studio
🍊 Personal Motto: Investigate to gain knowledge, complete MATLAB code and simulation consultation available via private message.
🔥 Content Introduction
Ferromagnetic materials, with their unique hysteresis characteristics, high magnetic permeability, and magnetic saturation phenomena, have become core foundational materials in fields such as electrical equipment, sensors, and precision instruments. From optimizing magnetic losses in transformer cores to controlling electromagnetic forces in maglev trains, and precisely regulating magnetic fields in MRI equipment, the performance of ferromagnetic materials directly determines the efficiency and reliability of related systems. However, the magnetic characteristics of ferromagnetic materials exhibit strong nonlinearity and hysteresis, with the magnetization process influenced by multiple factors such as temperature, stress, and frequency, making traditional empirical designs inadequate for high-precision control requirements.The emergence of ferromagnetic material control simulation technology, through the construction of mathematical models and numerical simulations, has achieved precise prediction and dynamic control of the magnetic behavior of ferromagnetic materials, becoming a key tool to break through engineering application bottlenecks.
1. Magnetic Characteristics of Ferromagnetic Materials: The “Driving Force” of Simulation
The magnetic behavior of ferromagnetic materials (such as silicon steel sheets, permalloy, and ferrites) is the core research object of control simulation, and the complexity of these characteristics directly drives the development of simulation technology.
(1) Nonlinear Magnetization Curve
The relationship between the magnetization intensity of ferromagnetic materials and the applied magnetic field strength is not linear, but presents an “S” shaped magnetization curve (B-H curve): in the initial stage, the magnetization intensity increases slowly with the magnetic field (reversible magnetization), then rises rapidly (domain wall movement), and finally approaches saturation (magnetic domains rotate to the direction of the magnetic field). This nonlinearity causes the voltage and current relationship of ferromagnetic components to no longer satisfy Ohm’s law, posing challenges for circuit simulation. For example, when a transformer core operates in the near-saturation region of the magnetization curve under rated voltage, if the voltage fluctuates, the excitation current will increase sharply, necessitating precise prediction through simulation.
(2) Hysteresis Loss and Eddy Current Loss
When the applied magnetic field alternates, ferromagnetic materials will produce two main types of losses:
- Hysteresis Loss: Arising from the friction and irreversible movement of magnetic domains during repeated magnetization processes, its magnitude is proportional to the area of the hysteresis loop and increases with frequency.
- Eddy Current Loss: Alternating magnetic fields induce eddy currents within the material, generating Joule heat, with losses proportional to the material’s conductivity, the square of its thickness, and the square of the frequency.
In devices such as motors and transformers, these two types of losses account for over 60% of total losses. By optimizing material selection (e.g., using thin silicon steel sheets to reduce eddy current loss) and magnetic field waveforms through simulation, energy efficiency can be significantly improved.
(3) Effects of Stress and Temperature
Mechanical stress can alter the magnetic domain structure of ferromagnetic materials, leading to a decrease in permeability and an increase in hysteresis loss (e.g., stress effects on motor rotors due to centrifugal force); elevated temperatures may cause materials to transition from ferromagnetic to paramagnetic (beyond the Curie point), completely losing their magnetic properties. These factors must be incorporated into the model during simulation to accurately reflect actual working conditions.
⛳️ Operating Results
📣 Sample Code
% By Yifei Shao 2018
classdef ElectroMagnet_Class
properties (Access = public)
Dipole_est
I
N
L
R
Location
Direction
B%the 4dimension one with i j k
end
properties (SetAccess = private, GetAccess = ? Field3D)
Br
Bn
Bmag
end
methods
%constructor
function obj = ElectroMagnet_Class(Dipole_est,I,N,L,R,Location,Direction)
obj.I = I;
🔗 References
🎈 Some theoretical references are from online literature; please contact the author for removal if there is any infringement.
👇 Follow me to receive a wealth of MATLAB e-books and mathematical modeling materials
🏆 Our team specializes in guiding customized MATLAB simulations in various research fields, helping to realize research dreams:
🌈 Various intelligent optimization algorithm improvements and applications
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, 3D packing, logistics site selection, cargo position optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, container ship loading optimization, pump combination optimization, medical resource allocation optimization, facility layout optimization, visual domain base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distribution of distributed generation units, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency supply material distribution center site selection, base station site selection, road lamp post arrangement, hub node deployment, transmission line typhoon monitoring devices, container scheduling, unit optimization, investment optimization portfolio, cloud server combination optimization, antenna linear array distribution optimization, CVRP problem, VRPPD problem, multi-center VRP problem, multi-layer network VRP problem, multi-center multi-vehicle VRP problem, dynamic VRP problem, two-layer vehicle routing planning (2E-VRP), electric vehicle routing planning (EVRP), hybrid vehicle routing planning, mixed flow shop problem, order splitting scheduling problem, bus scheduling optimization problem, flight shuttle vehicle scheduling problem, site selection path planning problem, port scheduling, port bridge scheduling, parking space allocation, airport flight scheduling, leak source localization
🌈 Time series, regression, classification, clustering, and dimensionality reduction in machine learning and deep learning
2.1 BP time series, regression prediction, and classification
2.2 ENS voice neural network time series, regression prediction, and classification
2.3 SVM/CNN-SVM/LSSVM/RVM support vector machine series time series, regression prediction, and classification
2.4 CNN|TCN|GCN convolutional neural network series time series, regression prediction, and classification
2.5 ELM/KELM/RELM/DELM extreme learning machine series time series, regression prediction, and classification
2.6 GRU/Bi-GRU/CNN-GRU/CNN-BiGRU gated neural network time series, regression prediction, and classification
2.7 Elman recurrent neural network time series, regression prediction, and classification
2.8 LSTM/BiLSTM/CNN-LSTM/CNN-BiLSTM long short-term memory neural network series time series, regression prediction, and classification
2.9 RBF radial basis function neural network time series, regression prediction, and classification
2.10 DBN deep belief network time series, regression prediction, and classification
2.11 FNN fuzzy neural network time series, regression prediction
2.12 RF random forest time series, regression prediction, and classification
2.13 BLS broad learning system time series, regression prediction, and classification
2.14 PNN pulse neural network classification
2.15 Fuzzy wavelet neural network prediction and classification
2.16 Time series, regression prediction, and classification
2.17 Time series, regression prediction, and classification
2.18 XGBOOST ensemble learning time series, regression prediction, and classification
2.19 Transform various combinations of time series, regression prediction, and classification
Covering wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load forecasting, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, precise prediction of subway stops, transformer fault diagnosis
🌈 In image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
🌈 In path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), 3D path planning for drones, drone collaboration, drone formation, robot path planning, grid map path planning, multimodal transport problems, electric vehicle routing planning (EVRP), two-layer vehicle routing planning (2E-VRP), hybrid vehicle routing planning, ship trajectory planning, full path planning, warehouse patrol
🌈 In drone applications
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, online optimization of safe communication trajectories for drones, vehicle collaborative drone path planning
🌈 In communication
Sensor deployment optimization, communication protocol optimization, routing optimization, target localization optimization, Dv-Hop localization optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI localization optimization, underwater acoustic communication, communication upload and download allocation
🌈 In signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, electromyography signals, electroencephalography signals, signal timing optimization, electrocardiogram signals, DOA estimation, encoding and decoding, variational mode decomposition, pipeline leakage, filters, digital signal processing + transmission + analysis + denoising, digital signal modulation, bit error rate, signal estimation, DTMF, signal detection
🌈 In power systems
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity, electric/cold/heat load forecasting, power equipment fault diagnosis, battery management system (BMS) SOC/SOH estimation (particle filter/Kalman filter), multi-objective optimization in power system dispatch, photovoltaic MPPT control algorithm improvement (perturbation observation method/incremental conductance method)
🌈 In cellular automata
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion
🌈 In radar
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification
🌈 Workshop scheduling
Zero-wait flow shop scheduling problem (NWFSP), Permutation flow shop scheduling problem (PFSP), Hybrid flow shop scheduling problem (HFSP), zero idle flow shop scheduling problem (NIFSP), distributed permutation flow shop scheduling problem (DPFSP), blocking flow shop scheduling problem (BFSP)
👇