β Author Introduction: A Matlab simulation developer passionate about scientific research, skilled in data processing, modeling simulation, program design, obtaining complete code, reproducing papers, and scientific simulation.
π Previous Review: Follow the personal homepage:Matlab Research Studio
π Personal Motto: Investigate to gain knowledge, complete Matlab code and simulation consultation available via private message.
π₯ Content Introduction
Magnetic field calculation is a core foundation of electromagnetics and engineering applications (such as motor design, sensor development, and magnetic separation technology). Due to structural differences, the magnetic field distribution of axial magnets (such as axially magnetized cylindrical magnets) and annular magnets (such as annularly magnetized hollow cylindrical magnets) varies, requiring separate calculations through analytical formulas (suitable for simple scenarios) or numerical methods (suitable for complex boundaries). This article will systematically outline the principles, formula derivation, and example verification of magnetic field calculations for both types of magnets at key spatial locations.
1. Basic Concepts and Assumptions
Before calculations, it is essential to clarify the core physical quantities and simplification conditions for magnetic field calculations to ensure the results are reasonable and practical:

2. Magnetic Field Calculation of Axial Magnets



3. Magnetic Field Calculation of Annular Magnets


4. Summary and Application Scenarios
- Axial Magnets: High magnetic field strength along the axis (up to over 1T), suitable for scenarios requiring strong magnetic fields (such as magnetic adsorption, magnetic therapy devices). Use analytical formulas for calculations along the axis, and numerical integration or simulation for non-axial regions;
- Annular Magnets: Uniform but low magnetic field strength in the inner hole, suitable for scenarios requiring annular magnetic fields (such as motor stators, annular sensors). Use analytical formulas for the magnetic field along the inner hole axis, and magnetic dipole approximation or simulation for the external magnetic field.
If specific structural magnetic field calculations (such as specific dimensions or magnetization methods) are needed, detailed parameters can be provided for further derivation of customized formulas or design simulation plans.
β³οΈ Operation Results






π References
[1] Wang Ruikai, Zuo Hongfu, LΓΌ Meng. Analytical Calculation and Simulation of the Spatial Magnetic Field of Annular Magnets [J]. Aviation Computing Technology, 2011, 41(5):5. DOI:10.3969/j.issn.1671-654X.2011.05.005.
[2] Li Fei, Wu Yunfeng, Zhang Ping, et al. Data Processing of Hall Effect Experimental Data Based on Matlab [J]. Laboratory Research and Exploration, 2011, 30(1):5. DOI:10.3969/j.issn.1006-7167.2011.01.017.
[3] Bai Zhihong, Zhou Yuhu. Simulation Model and Calculation Analysis of the Dynamic Characteristics of Electromagnets [C]//CNKI; WanFang. CNKI; WanFang, 2007:19-20+23. DOI:CNKI:SUN:DILY.0.2004-03-005.
π£ Partial Code
π Some theoretical references are from online literature; please contact the author for removal if there is any infringement.
π Follow me to receive a wealth of Matlab e-books and mathematical modeling materials
π The team specializes in guiding customized MATLAB simulations in various research fields to support your research dreams:
π 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 location 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 field base station and drone site selection optimization, knapsack problem, wind farm layout, time slot allocation optimization, optimal distributed generation unit allocation, multi-stage pipeline maintenance, factory-center-demand point three-level site selection problem, emergency life material distribution center site selection, base station site selection, road lamp column 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 workshop 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 location.
π Machine learning and deep learning time series, regression, classification, clustering, and dimensionality reduction
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 time series, regression prediction, and classification
Covering wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, electricity consumption prediction, water body optical parameter inversion, NLOS signal identification, subway parking precision prediction, transformer fault diagnosis.
π Image processing aspects
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compressed sensing
π Path planning aspects
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), drone 3D path planning, 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.
π Drone application aspects
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, drone secure communication trajectory online optimization, vehicle collaborative drone path planning.
π Communication aspects
Sensor deployment optimization, communication protocol optimization, routing optimization, target location optimization, Dv-Hop location optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI location optimization, underwater communication, communication upload and download allocation.
π Signal processing aspects
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signals, EEG signals, signal timing optimization, ECG 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.
π Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration, orderly charging, MPPT optimization, household electricity.
π Cellular automata aspects
Traffic flow, crowd evacuation, virus spread, crystal growth, metal corrosion.
π Radar aspects
Kalman filter tracking, trajectory association, trajectory fusion, SOC estimation, array optimization, NLOS identification.
π 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).
π